Create first version of NNAPI AIDL interface
Bug: 161428342
Test: m android.hardware.neuralnetworks-update-api && m
Change-Id: Icf8123746def6f4c654dc3e413e5169ab020c8b4
Merged-In: Icf8123746def6f4c654dc3e413e5169ab020c8b4
(cherry picked from commit 8090245174e252697a406852d302fc30ad97d5db)
diff --git a/compatibility_matrices/compatibility_matrix.current.xml b/compatibility_matrices/compatibility_matrix.current.xml
index 5e7e4c7..cbdce23 100644
--- a/compatibility_matrices/compatibility_matrix.current.xml
+++ b/compatibility_matrices/compatibility_matrix.current.xml
@@ -378,6 +378,13 @@
<regex-instance>.*</regex-instance>
</interface>
</hal>
+ <hal format="aidl" optional="true">
+ <name>android.hardware.neuralnetworks</name>
+ <interface>
+ <name>IDevice</name>
+ <regex-instance>.*</regex-instance>
+ </interface>
+ </hal>
<hal format="hidl" optional="true">
<name>android.hardware.nfc</name>
<version>1.2</version>
diff --git a/neuralnetworks/aidl/Android.bp b/neuralnetworks/aidl/Android.bp
new file mode 100644
index 0000000..308f89f
--- /dev/null
+++ b/neuralnetworks/aidl/Android.bp
@@ -0,0 +1,19 @@
+aidl_interface {
+ name: "android.hardware.neuralnetworks",
+ vendor_available: true,
+ srcs: [
+ "android/hardware/neuralnetworks/*.aidl",
+ ],
+ stability: "vintf",
+ imports: [
+ "android.hardware.common",
+ ],
+ backend: {
+ java: {
+ enabled: false,
+ },
+ cpp: {
+ enabled: false,
+ },
+ },
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/BufferDesc.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/BufferDesc.aidl
new file mode 100644
index 0000000..2074a2a
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/BufferDesc.aidl
@@ -0,0 +1,22 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@VintfStability
+parcelable BufferDesc {
+ int[] dimensions;
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/BufferRole.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/BufferRole.aidl
new file mode 100644
index 0000000..97f748b
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/BufferRole.aidl
@@ -0,0 +1,24 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@VintfStability
+parcelable BufferRole {
+ int modelIndex;
+ int ioIndex;
+ float frequency;
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/Capabilities.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/Capabilities.aidl
new file mode 100644
index 0000000..31afafc
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/Capabilities.aidl
@@ -0,0 +1,26 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@VintfStability
+parcelable Capabilities {
+ android.hardware.neuralnetworks.PerformanceInfo relaxedFloat32toFloat16PerformanceScalar;
+ android.hardware.neuralnetworks.PerformanceInfo relaxedFloat32toFloat16PerformanceTensor;
+ android.hardware.neuralnetworks.OperandPerformance[] operandPerformance;
+ android.hardware.neuralnetworks.PerformanceInfo ifPerformance;
+ android.hardware.neuralnetworks.PerformanceInfo whilePerformance;
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/DataLocation.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/DataLocation.aidl
new file mode 100644
index 0000000..5b03ba0
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/DataLocation.aidl
@@ -0,0 +1,24 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@VintfStability
+parcelable DataLocation {
+ int poolIndex;
+ long offset;
+ long length;
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/DeviceBuffer.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/DeviceBuffer.aidl
new file mode 100644
index 0000000..9cff6db
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/DeviceBuffer.aidl
@@ -0,0 +1,23 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@VintfStability
+parcelable DeviceBuffer {
+ android.hardware.neuralnetworks.IBuffer buffer;
+ int token;
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/DeviceType.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/DeviceType.aidl
new file mode 100644
index 0000000..dd4dae7
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/DeviceType.aidl
@@ -0,0 +1,25 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@Backing(type="int") @VintfStability
+enum DeviceType {
+ OTHER = 1,
+ CPU = 2,
+ GPU = 3,
+ ACCELERATOR = 4,
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/ErrorStatus.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/ErrorStatus.aidl
new file mode 100644
index 0000000..ba18c38
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/ErrorStatus.aidl
@@ -0,0 +1,30 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@Backing(type="int") @VintfStability
+enum ErrorStatus {
+ NONE = 0,
+ DEVICE_UNAVAILABLE = 1,
+ GENERAL_FAILURE = 2,
+ OUTPUT_INSUFFICIENT_SIZE = 3,
+ INVALID_ARGUMENT = 4,
+ MISSED_DEADLINE_TRANSIENT = 5,
+ MISSED_DEADLINE_PERSISTENT = 6,
+ RESOURCE_EXHAUSTED_TRANSIENT = 7,
+ RESOURCE_EXHAUSTED_PERSISTENT = 8,
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/ExecutionPreference.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/ExecutionPreference.aidl
new file mode 100644
index 0000000..cccae54
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/ExecutionPreference.aidl
@@ -0,0 +1,24 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@Backing(type="int") @VintfStability
+enum ExecutionPreference {
+ LOW_POWER = 0,
+ FAST_SINGLE_ANSWER = 1,
+ SUSTAINED_SPEED = 2,
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/ExecutionResult.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/ExecutionResult.aidl
new file mode 100644
index 0000000..c17ddb9
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/ExecutionResult.aidl
@@ -0,0 +1,24 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@VintfStability
+parcelable ExecutionResult {
+ boolean outputSufficientSize;
+ android.hardware.neuralnetworks.OutputShape[] outputShapes;
+ android.hardware.neuralnetworks.Timing timing;
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/Extension.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/Extension.aidl
new file mode 100644
index 0000000..9eb8896
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/Extension.aidl
@@ -0,0 +1,23 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@VintfStability
+parcelable Extension {
+ String name;
+ android.hardware.neuralnetworks.ExtensionOperandTypeInformation[] operandTypes;
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/ExtensionNameAndPrefix.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/ExtensionNameAndPrefix.aidl
new file mode 100644
index 0000000..a271a63
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/ExtensionNameAndPrefix.aidl
@@ -0,0 +1,23 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@VintfStability
+parcelable ExtensionNameAndPrefix {
+ String name;
+ char prefix;
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/ExtensionOperandTypeInformation.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/ExtensionOperandTypeInformation.aidl
new file mode 100644
index 0000000..d1c3f09
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/ExtensionOperandTypeInformation.aidl
@@ -0,0 +1,24 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@VintfStability
+parcelable ExtensionOperandTypeInformation {
+ char type;
+ boolean isTensor;
+ int byteSize;
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/FusedActivationFunc.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/FusedActivationFunc.aidl
new file mode 100644
index 0000000..ddd3c2a
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/FusedActivationFunc.aidl
@@ -0,0 +1,25 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@Backing(type="int") @VintfStability
+enum FusedActivationFunc {
+ NONE = 0,
+ RELU = 1,
+ RELU1 = 2,
+ RELU6 = 3,
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/IBuffer.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/IBuffer.aidl
new file mode 100644
index 0000000..a297a6b
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/IBuffer.aidl
@@ -0,0 +1,23 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@VintfStability
+interface IBuffer {
+ void copyFrom(in android.hardware.neuralnetworks.Memory src, in int[] dimensions);
+ void copyTo(in android.hardware.neuralnetworks.Memory dst);
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/IDevice.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/IDevice.aidl
new file mode 100644
index 0000000..38fda16
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/IDevice.aidl
@@ -0,0 +1,36 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@VintfStability
+interface IDevice {
+ android.hardware.neuralnetworks.DeviceBuffer allocate(in android.hardware.neuralnetworks.BufferDesc desc, in android.hardware.neuralnetworks.IPreparedModelParcel[] preparedModels, in android.hardware.neuralnetworks.BufferRole[] inputRoles, in android.hardware.neuralnetworks.BufferRole[] outputRoles);
+ android.hardware.neuralnetworks.Capabilities getCapabilities();
+ android.hardware.neuralnetworks.NumberOfCacheFiles getNumberOfCacheFilesNeeded();
+ android.hardware.neuralnetworks.Extension[] getSupportedExtensions();
+ boolean[] getSupportedOperations(in android.hardware.neuralnetworks.Model model);
+ android.hardware.neuralnetworks.DeviceType getType();
+ String getVersionString();
+ void prepareModel(in android.hardware.neuralnetworks.Model model, in android.hardware.neuralnetworks.ExecutionPreference preference, in android.hardware.neuralnetworks.Priority priority, in long deadline, in ParcelFileDescriptor[] modelCache, in ParcelFileDescriptor[] dataCache, in byte[] token, in android.hardware.neuralnetworks.IPreparedModelCallback callback);
+ void prepareModelFromCache(in long deadline, in ParcelFileDescriptor[] modelCache, in ParcelFileDescriptor[] dataCache, in byte[] token, in android.hardware.neuralnetworks.IPreparedModelCallback callback);
+ const int BYTE_SIZE_OF_CACHE_TOKEN = 32;
+ const int MAX_NUMBER_OF_CACHE_FILES = 32;
+ const int EXTENSION_TYPE_HIGH_BITS_PREFIX = 15;
+ const int EXTENSION_TYPE_LOW_BITS_TYPE = 16;
+ const int OPERAND_TYPE_BASE_MAX = 65535;
+ const int OPERATION_TYPE_BASE_MAX = 65535;
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/IFencedExecutionCallback.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/IFencedExecutionCallback.aidl
new file mode 100644
index 0000000..a7cf906
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/IFencedExecutionCallback.aidl
@@ -0,0 +1,22 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@VintfStability
+interface IFencedExecutionCallback {
+ android.hardware.neuralnetworks.ErrorStatus getExecutionInfo(out android.hardware.neuralnetworks.Timing timingLaunched, out android.hardware.neuralnetworks.Timing timingFenced);
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/IPreparedModel.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/IPreparedModel.aidl
new file mode 100644
index 0000000..8767712
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/IPreparedModel.aidl
@@ -0,0 +1,25 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@VintfStability
+interface IPreparedModel {
+ android.hardware.neuralnetworks.ExecutionResult executeSynchronously(in android.hardware.neuralnetworks.Request request, in boolean measureTiming, in long deadline, in long loopTimeoutDuration);
+ android.hardware.neuralnetworks.IFencedExecutionCallback executeFenced(in android.hardware.neuralnetworks.Request request, in ParcelFileDescriptor[] waitFor, in boolean measureTiming, in long deadline, in long loopTimeoutDuration, in long duration, out @nullable ParcelFileDescriptor syncFence);
+ const long DEFAULT_LOOP_TIMEOUT_DURATION_NS = 2000000000;
+ const long MAXIMUM_LOOP_TIMEOUT_DURATION_NS = 15000000000;
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/IPreparedModelCallback.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/IPreparedModelCallback.aidl
new file mode 100644
index 0000000..d1ae2eb
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/IPreparedModelCallback.aidl
@@ -0,0 +1,22 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@VintfStability
+interface IPreparedModelCallback {
+ void notify(in android.hardware.neuralnetworks.ErrorStatus status, in android.hardware.neuralnetworks.IPreparedModel preparedModel);
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/IPreparedModelParcel.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/IPreparedModelParcel.aidl
new file mode 100644
index 0000000..048251a
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/IPreparedModelParcel.aidl
@@ -0,0 +1,22 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@VintfStability
+parcelable IPreparedModelParcel {
+ android.hardware.neuralnetworks.IPreparedModel preparedModel;
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/Memory.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/Memory.aidl
new file mode 100644
index 0000000..aa735c0
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/Memory.aidl
@@ -0,0 +1,24 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@VintfStability
+parcelable Memory {
+ android.hardware.common.NativeHandle handle;
+ long size;
+ String name;
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/Model.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/Model.aidl
new file mode 100644
index 0000000..944bd7f
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/Model.aidl
@@ -0,0 +1,27 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@VintfStability
+parcelable Model {
+ android.hardware.neuralnetworks.Subgraph main;
+ android.hardware.neuralnetworks.Subgraph[] referenced;
+ byte[] operandValues;
+ android.hardware.neuralnetworks.Memory[] pools;
+ boolean relaxComputationFloat32toFloat16;
+ android.hardware.neuralnetworks.ExtensionNameAndPrefix[] extensionNameToPrefix;
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/NumberOfCacheFiles.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/NumberOfCacheFiles.aidl
new file mode 100644
index 0000000..ca5f917
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/NumberOfCacheFiles.aidl
@@ -0,0 +1,23 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@VintfStability
+parcelable NumberOfCacheFiles {
+ int numModelCache;
+ int numDataCache;
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/Operand.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/Operand.aidl
new file mode 100644
index 0000000..6615b9b
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/Operand.aidl
@@ -0,0 +1,28 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@VintfStability
+parcelable Operand {
+ android.hardware.neuralnetworks.OperandType type;
+ int[] dimensions;
+ float scale;
+ int zeroPoint;
+ android.hardware.neuralnetworks.OperandLifeTime lifetime;
+ android.hardware.neuralnetworks.DataLocation location;
+ @nullable android.hardware.neuralnetworks.OperandExtraParams extraParams;
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/OperandExtraParams.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/OperandExtraParams.aidl
new file mode 100644
index 0000000..20317c7
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/OperandExtraParams.aidl
@@ -0,0 +1,23 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@VintfStability
+union OperandExtraParams {
+ android.hardware.neuralnetworks.SymmPerChannelQuantParams channelQuant;
+ byte[] extension;
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/OperandLifeTime.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/OperandLifeTime.aidl
new file mode 100644
index 0000000..1082f9e
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/OperandLifeTime.aidl
@@ -0,0 +1,28 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@Backing(type="int") @VintfStability
+enum OperandLifeTime {
+ TEMPORARY_VARIABLE = 0,
+ SUBGRAPH_INPUT = 1,
+ SUBGRAPH_OUTPUT = 2,
+ CONSTANT_COPY = 3,
+ CONSTANT_POOL = 4,
+ NO_VALUE = 5,
+ SUBGRAPH = 6,
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/OperandPerformance.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/OperandPerformance.aidl
new file mode 100644
index 0000000..9232b4c
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/OperandPerformance.aidl
@@ -0,0 +1,23 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@VintfStability
+parcelable OperandPerformance {
+ android.hardware.neuralnetworks.OperandType type;
+ android.hardware.neuralnetworks.PerformanceInfo info;
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/OperandType.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/OperandType.aidl
new file mode 100644
index 0000000..bd95fab
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/OperandType.aidl
@@ -0,0 +1,37 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@Backing(type="int") @VintfStability
+enum OperandType {
+ FLOAT32 = 0,
+ INT32 = 1,
+ UINT32 = 2,
+ TENSOR_FLOAT32 = 3,
+ TENSOR_INT32 = 4,
+ TENSOR_QUANT8_ASYMM = 5,
+ BOOL = 6,
+ TENSOR_QUANT16_SYMM = 7,
+ TENSOR_FLOAT16 = 8,
+ TENSOR_BOOL8 = 9,
+ FLOAT16 = 10,
+ TENSOR_QUANT8_SYMM_PER_CHANNEL = 11,
+ TENSOR_QUANT16_ASYMM = 12,
+ TENSOR_QUANT8_SYMM = 13,
+ TENSOR_QUANT8_ASYMM_SIGNED = 14,
+ SUBGRAPH = 15,
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/Operation.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/Operation.aidl
new file mode 100644
index 0000000..383eba4
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/Operation.aidl
@@ -0,0 +1,24 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@VintfStability
+parcelable Operation {
+ android.hardware.neuralnetworks.OperationType type;
+ int[] inputs;
+ int[] outputs;
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/OperationType.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/OperationType.aidl
new file mode 100644
index 0000000..f786829
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/OperationType.aidl
@@ -0,0 +1,123 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@Backing(type="int") @VintfStability
+enum OperationType {
+ ADD = 0,
+ AVERAGE_POOL_2D = 1,
+ CONCATENATION = 2,
+ CONV_2D = 3,
+ DEPTHWISE_CONV_2D = 4,
+ DEPTH_TO_SPACE = 5,
+ DEQUANTIZE = 6,
+ EMBEDDING_LOOKUP = 7,
+ FLOOR = 8,
+ FULLY_CONNECTED = 9,
+ HASHTABLE_LOOKUP = 10,
+ L2_NORMALIZATION = 11,
+ L2_POOL_2D = 12,
+ LOCAL_RESPONSE_NORMALIZATION = 13,
+ LOGISTIC = 14,
+ LSH_PROJECTION = 15,
+ LSTM = 16,
+ MAX_POOL_2D = 17,
+ MUL = 18,
+ RELU = 19,
+ RELU1 = 20,
+ RELU6 = 21,
+ RESHAPE = 22,
+ RESIZE_BILINEAR = 23,
+ RNN = 24,
+ SOFTMAX = 25,
+ SPACE_TO_DEPTH = 26,
+ SVDF = 27,
+ TANH = 28,
+ BATCH_TO_SPACE_ND = 29,
+ DIV = 30,
+ MEAN = 31,
+ PAD = 32,
+ SPACE_TO_BATCH_ND = 33,
+ SQUEEZE = 34,
+ STRIDED_SLICE = 35,
+ SUB = 36,
+ TRANSPOSE = 37,
+ ABS = 38,
+ ARGMAX = 39,
+ ARGMIN = 40,
+ AXIS_ALIGNED_BBOX_TRANSFORM = 41,
+ BIDIRECTIONAL_SEQUENCE_LSTM = 42,
+ BIDIRECTIONAL_SEQUENCE_RNN = 43,
+ BOX_WITH_NMS_LIMIT = 44,
+ CAST = 45,
+ CHANNEL_SHUFFLE = 46,
+ DETECTION_POSTPROCESSING = 47,
+ EQUAL = 48,
+ EXP = 49,
+ EXPAND_DIMS = 50,
+ GATHER = 51,
+ GENERATE_PROPOSALS = 52,
+ GREATER = 53,
+ GREATER_EQUAL = 54,
+ GROUPED_CONV_2D = 55,
+ HEATMAP_MAX_KEYPOINT = 56,
+ INSTANCE_NORMALIZATION = 57,
+ LESS = 58,
+ LESS_EQUAL = 59,
+ LOG = 60,
+ LOGICAL_AND = 61,
+ LOGICAL_NOT = 62,
+ LOGICAL_OR = 63,
+ LOG_SOFTMAX = 64,
+ MAXIMUM = 65,
+ MINIMUM = 66,
+ NEG = 67,
+ NOT_EQUAL = 68,
+ PAD_V2 = 69,
+ POW = 70,
+ PRELU = 71,
+ QUANTIZE = 72,
+ QUANTIZED_16BIT_LSTM = 73,
+ RANDOM_MULTINOMIAL = 74,
+ REDUCE_ALL = 75,
+ REDUCE_ANY = 76,
+ REDUCE_MAX = 77,
+ REDUCE_MIN = 78,
+ REDUCE_PROD = 79,
+ REDUCE_SUM = 80,
+ ROI_ALIGN = 81,
+ ROI_POOLING = 82,
+ RSQRT = 83,
+ SELECT = 84,
+ SIN = 85,
+ SLICE = 86,
+ SPLIT = 87,
+ SQRT = 88,
+ TILE = 89,
+ TOPK_V2 = 90,
+ TRANSPOSE_CONV_2D = 91,
+ UNIDIRECTIONAL_SEQUENCE_LSTM = 92,
+ UNIDIRECTIONAL_SEQUENCE_RNN = 93,
+ RESIZE_NEAREST_NEIGHBOR = 94,
+ QUANTIZED_LSTM = 95,
+ IF = 96,
+ WHILE = 97,
+ ELU = 98,
+ HARD_SWISH = 99,
+ FILL = 100,
+ RANK = 101,
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/OutputShape.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/OutputShape.aidl
new file mode 100644
index 0000000..1300c49
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/OutputShape.aidl
@@ -0,0 +1,23 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@VintfStability
+parcelable OutputShape {
+ int[] dimensions;
+ boolean isSufficient;
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/PerformanceInfo.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/PerformanceInfo.aidl
new file mode 100644
index 0000000..b5dc179
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/PerformanceInfo.aidl
@@ -0,0 +1,23 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@VintfStability
+parcelable PerformanceInfo {
+ float execTime;
+ float powerUsage;
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/Priority.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/Priority.aidl
new file mode 100644
index 0000000..980bee3
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/Priority.aidl
@@ -0,0 +1,24 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@Backing(type="int") @VintfStability
+enum Priority {
+ LOW = 0,
+ MEDIUM = 1,
+ HIGH = 2,
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/Request.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/Request.aidl
new file mode 100644
index 0000000..6f77066
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/Request.aidl
@@ -0,0 +1,24 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@VintfStability
+parcelable Request {
+ android.hardware.neuralnetworks.RequestArgument[] inputs;
+ android.hardware.neuralnetworks.RequestArgument[] outputs;
+ android.hardware.neuralnetworks.RequestMemoryPool[] pools;
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/RequestArgument.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/RequestArgument.aidl
new file mode 100644
index 0000000..c9560ef
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/RequestArgument.aidl
@@ -0,0 +1,24 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@VintfStability
+parcelable RequestArgument {
+ boolean hasNoValue;
+ android.hardware.neuralnetworks.DataLocation location;
+ int[] dimensions;
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/RequestMemoryPool.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/RequestMemoryPool.aidl
new file mode 100644
index 0000000..123e4b0
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/RequestMemoryPool.aidl
@@ -0,0 +1,23 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@VintfStability
+union RequestMemoryPool {
+ android.hardware.neuralnetworks.Memory pool;
+ int token;
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/Subgraph.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/Subgraph.aidl
new file mode 100644
index 0000000..771d15a
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/Subgraph.aidl
@@ -0,0 +1,25 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@VintfStability
+parcelable Subgraph {
+ android.hardware.neuralnetworks.Operand[] operands;
+ android.hardware.neuralnetworks.Operation[] operations;
+ int[] inputIndexes;
+ int[] outputIndexes;
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/SymmPerChannelQuantParams.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/SymmPerChannelQuantParams.aidl
new file mode 100644
index 0000000..2282feb
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/SymmPerChannelQuantParams.aidl
@@ -0,0 +1,23 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@VintfStability
+parcelable SymmPerChannelQuantParams {
+ float[] scales;
+ int channelDim;
+}
diff --git a/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/Timing.aidl b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/Timing.aidl
new file mode 100644
index 0000000..b08d34a
--- /dev/null
+++ b/neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/Timing.aidl
@@ -0,0 +1,23 @@
+///////////////////////////////////////////////////////////////////////////////
+// THIS FILE IS IMMUTABLE. DO NOT EDIT IN ANY CASE. //
+///////////////////////////////////////////////////////////////////////////////
+
+// This file is a snapshot of an AIDL interface (or parcelable). Do not try to
+// edit this file. It looks like you are doing that because you have modified
+// an AIDL interface in a backward-incompatible way, e.g., deleting a function
+// from an interface or a field from a parcelable and it broke the build. That
+// breakage is intended.
+//
+// You must not make a backward incompatible changes to the AIDL files built
+// with the aidl_interface module type with versions property set. The module
+// type is used to build AIDL files in a way that they can be used across
+// independently updatable components of the system. If a device is shipped
+// with such a backward incompatible change, it has a high risk of breaking
+// later when a module using the interface is updated, e.g., Mainline modules.
+
+package android.hardware.neuralnetworks;
+@VintfStability
+parcelable Timing {
+ long timeOnDevice;
+ long timeInDriver;
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/BufferDesc.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/BufferDesc.aidl
new file mode 100644
index 0000000..1b92ebc
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/BufferDesc.aidl
@@ -0,0 +1,31 @@
+/*
+ * Copyright (C) 2020 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+
+package android.hardware.neuralnetworks;
+
+/**
+ * A buffer descriptor. Describes the properties of a buffer.
+ */
+@VintfStability
+parcelable BufferDesc {
+ /**
+ * Dimensions of the buffer. May have unknown dimensions or rank. A buffer with some number of
+ * unspecified dimensions is represented by setting each unspecified dimension to 0. A buffer
+ * with unspecified rank is represented by providing an empty dimensions vector.
+ */
+ int[] dimensions;
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/BufferRole.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/BufferRole.aidl
new file mode 100644
index 0000000..7877bc0
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/BufferRole.aidl
@@ -0,0 +1,40 @@
+/*
+ * Copyright (C) 2020 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+
+package android.hardware.neuralnetworks;
+
+/**
+ * Describes a role of an input or output to a prepared model.
+ */
+@VintfStability
+parcelable BufferRole {
+ /**
+ * The index of the IPreparedModel within the "preparedModel" argument passed in
+ * IDevice::allocate.
+ */
+ int modelIndex;
+ /**
+ * The index of the input or output operand.
+ */
+ int ioIndex;
+ /**
+ * A floating-point value within the range (0.0, 1.0]. Describes how likely the buffer is to be
+ * used in the specified role. This is provided as a hint to optimize the case when multiple
+ * roles prefer different buffer locations or data layouts.
+ */
+ float frequency;
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/Capabilities.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/Capabilities.aidl
new file mode 100644
index 0000000..5ce78ee
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/Capabilities.aidl
@@ -0,0 +1,63 @@
+/*
+ * Copyright (C) 2020 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+
+package android.hardware.neuralnetworks;
+
+import android.hardware.neuralnetworks.OperandPerformance;
+import android.hardware.neuralnetworks.PerformanceInfo;
+
+/**
+ * The capabilities of a driver.
+ *
+ * This represents performance of non-extension operations.
+ *
+ * Performance of an operation other than {@link OperationType::IF} and {@link OperationType::WHILE}
+ * comes from the type of its first operand.
+ */
+@VintfStability
+parcelable Capabilities {
+ /**
+ * Driver performance when operating on float32 data but performing calculations with range
+ * and/or precision as low as that of the IEEE 754 16-bit floating-point format.
+ */
+ PerformanceInfo relaxedFloat32toFloat16PerformanceScalar;
+ PerformanceInfo relaxedFloat32toFloat16PerformanceTensor;
+ /**
+ * Performance by operand type. Must be sorted by OperandType.
+ *
+ * If a particular {@link OperandType} is not present in operandPerformance, its performance is
+ * treated as { .execTime = FLT_MAX, .powerUsage = FLT_MAX }.
+ *
+ * Performance does not apply to {@link OperandType::SUBGRAPH}, and a driver must not report
+ * operand performance for {@link OperandType::SUBGRAPH}.
+ */
+ OperandPerformance[] operandPerformance;
+ /**
+ * Performance of an {@link OperationType::IF} operation is the sum of
+ * {@link Capabilities::ifPerformance} and the mean of performance for the two branch subgraphs,
+ * where performance for a subgraph is the sum of the performance of all operations within the
+ * subgraph.
+ */
+ PerformanceInfo ifPerformance;
+ /**
+ * Performance of a {@link OperationType::WHILE} operation is the sum of
+ * {@link Capabilities::whilePerformance}, performance for the condition subgraph and
+ * performance for the body subgraph, where performance for a subgraph is the sum of the
+ * performance of all operations within the subgraph.
+ */
+ PerformanceInfo whilePerformance;
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/DataLocation.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/DataLocation.aidl
new file mode 100644
index 0000000..57e3f4a
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/DataLocation.aidl
@@ -0,0 +1,37 @@
+/*
+ * Copyright (C) 2020 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+
+package android.hardware.neuralnetworks;
+
+/**
+ * Describes the location of a data object.
+ */
+@VintfStability
+parcelable DataLocation {
+ /**
+ * The index of the memory pool where this location is found.
+ */
+ int poolIndex;
+ /**
+ * Offset in bytes from the start of the pool.
+ */
+ long offset;
+ /**
+ * The length of the data in bytes.
+ */
+ long length;
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/DeviceBuffer.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/DeviceBuffer.aidl
new file mode 100644
index 0000000..d51e1b2
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/DeviceBuffer.aidl
@@ -0,0 +1,36 @@
+/*
+ * Copyright (C) 2020 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+ package android.hardware.neuralnetworks;
+
+import android.hardware.neuralnetworks.IBuffer;
+
+/**
+ * A type that is used to represent a driver allocated buffer and token that corresponds to it.
+ */
+ @VintfStability
+ parcelable DeviceBuffer {
+ /**
+ * An IBuffer object used to interact with the device allocated buffer.
+ */
+ IBuffer buffer;
+ /**
+ * A positive token identifying the allocated buffer. The token is provided when referencing the
+ * buffer as one of the memory pools in the request of an execution. The token must not collide
+ * with the tokens of other IBuffer objects that are currently alive in the same driver service.
+ */
+ int token;
+ }
\ No newline at end of file
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/DeviceType.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/DeviceType.aidl
new file mode 100644
index 0000000..8399d50
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/DeviceType.aidl
@@ -0,0 +1,45 @@
+/*
+ * Copyright (C) 2020 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+
+package android.hardware.neuralnetworks;
+
+/**
+ * Device types.
+ *
+ * The type of NNAPI device.
+ */
+@VintfStability
+@Backing(type="int")
+enum DeviceType {
+ /**
+ * The device does not fall into any category below.
+ */
+ OTHER = 1,
+ /**
+ * The device runs NNAPI models on single or multi-core CPU.
+ */
+ CPU = 2,
+ /**
+ * The device can run NNAPI models and also accelerate graphics APIs such as OpenGL ES and
+ * Vulkan.
+ */
+ GPU = 3,
+ /**
+ * Dedicated accelerator for Machine Learning workloads.
+ */
+ ACCELERATOR = 4,
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/ErrorStatus.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/ErrorStatus.aidl
new file mode 100644
index 0000000..860f86a
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/ErrorStatus.aidl
@@ -0,0 +1,52 @@
+/*
+ * Copyright (C) 2020 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+
+package android.hardware.neuralnetworks;
+
+/**
+ * Calls to neural networks AIDL interfaces may return a ServiceSpecificException with the following
+ * error codes.
+ */
+@VintfStability
+@Backing(type="int")
+enum ErrorStatus {
+ NONE,
+ DEVICE_UNAVAILABLE,
+ GENERAL_FAILURE,
+ OUTPUT_INSUFFICIENT_SIZE,
+ INVALID_ARGUMENT,
+ /**
+ * Failure because a deadline could not be met for a task, but future deadlines may still be met
+ * for the same task after a short delay.
+ */
+ MISSED_DEADLINE_TRANSIENT,
+ /**
+ * Failure because a deadline could not be met for a task, and future deadlines will likely also
+ * not be met for the same task even after a short delay.
+ */
+ MISSED_DEADLINE_PERSISTENT,
+ /**
+ * Failure because of a resource limitation within the driver, but future calls for the same
+ * task may still succeed after a short delay.
+ */
+ RESOURCE_EXHAUSTED_TRANSIENT,
+ /**
+ * Failure because of a resource limitation within the driver, and future calls for the same
+ * task will likely also fail even after a short delay.
+ */
+ RESOURCE_EXHAUSTED_PERSISTENT,
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/ExecutionPreference.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/ExecutionPreference.aidl
new file mode 100644
index 0000000..901cb38
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/ExecutionPreference.aidl
@@ -0,0 +1,41 @@
+/*
+ * Copyright (C) 2020 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+
+package android.hardware.neuralnetworks;
+
+/**
+ * Execution preferences.
+ */
+@VintfStability
+@Backing(type="int")
+enum ExecutionPreference {
+ /**
+ * Prefer executing in a way that minimizes battery drain. This is desirable for compilations
+ * that will be executed often.
+ */
+ LOW_POWER,
+ /**
+ * Prefer returning a single answer as fast as possible, even if this causes more power
+ * consumption.
+ */
+ FAST_SINGLE_ANSWER,
+ /**
+ * Prefer maximizing the throughput of successive frames, for example when processing successive
+ * frames coming from the camera.
+ */
+ SUSTAINED_SPEED,
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/ExecutionResult.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/ExecutionResult.aidl
new file mode 100644
index 0000000..403fe09
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/ExecutionResult.aidl
@@ -0,0 +1,47 @@
+/*
+ * Copyright (C) 2021 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+
+package android.hardware.neuralnetworks;
+
+import android.hardware.neuralnetworks.ErrorStatus;
+import android.hardware.neuralnetworks.OutputShape;
+import android.hardware.neuralnetworks.Timing;
+
+/**
+ * A result from running a synchronous execution of a prepared model.
+ */
+@VintfStability
+parcelable ExecutionResult {
+ /**
+ * A value of "true" indicates that the execution was successful. A value of "false" indicates
+ * the execution failed because at least one output operand buffer was not large enough to store
+ * the corresponding output.
+ */
+ boolean outputSufficientSize;
+ /**
+ * A list of shape information of model output operands. The index in "outputShapes" corresponds
+ * to the index of the output operand in the Request outputs vector.
+ */
+ OutputShape[] outputShapes;
+ /**
+ * Duration of execution. Unless measure is true and the execution is successful, all times must
+ * be reported as -1. A driver may choose to report any time as -1, indicating that measurement
+ * is not available.
+ */
+ Timing timing;
+}
+
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/Extension.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/Extension.aidl
new file mode 100644
index 0000000..159e3c1
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/Extension.aidl
@@ -0,0 +1,42 @@
+/*
+ * Copyright (C) 2020 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+
+package android.hardware.neuralnetworks;
+
+import android.hardware.neuralnetworks.ExtensionOperandTypeInformation;
+
+/**
+ * Information about an extension.
+ */
+@VintfStability
+parcelable Extension {
+ /**
+ * The extension name.
+ *
+ * The name must consist of lowercase latin letters, numbers, periods, and underscore signs. The
+ * name must contain at least one period.
+ *
+ * The name must start with the reverse domain name of the vendor.
+ *
+ * Example: com.google.test_extension
+ */
+ String name;
+ /**
+ * Information about operand types defined by the extension.
+ */
+ ExtensionOperandTypeInformation[] operandTypes;
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/ExtensionNameAndPrefix.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/ExtensionNameAndPrefix.aidl
new file mode 100644
index 0000000..76074bf
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/ExtensionNameAndPrefix.aidl
@@ -0,0 +1,49 @@
+/*
+ * Copyright (C) 2020 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+
+package android.hardware.neuralnetworks;
+
+/**
+ * The mapping between extension names and prefixes of operand and operation type values.
+ *
+ * An operand or operation whose numeric type value is above {@link IDevice::OPERAND_TYPE_BASE_MAX}
+ * or {@link IDevice::OPERATION_TYPE_BASE_MAX} respectively should be interpreted as an extension
+ * operand/operation. The low {@link IDevice::EXTENSION_TYPE_LOW_BITS_TYPE} bits of the value
+ * correspond to the type ID within the extension and the high
+ * {@link IDevice::EXTENSION_TYPE_HIGH_BITS_PREFIX} bits encode the "prefix", which maps uniquely to
+ * the extension name. The sign bit is always 0.
+ *
+ * For example, if a model contains an operation whose value is 0x7AAABBBB and extensionNameToPrefix
+ * contains an entry with prefix=0x7AAA and name="vendor.test.test_extension", then the operation
+ * should be interpreted as the operation 0xBBBB of the extension named vendor.test.test_extension.
+ *
+ * This is a one-to-one correspondence. That is, there must be at most one prefix corresponding to
+ * each extension name and at most one extension name corresponding to each prefix.
+ */
+@VintfStability
+parcelable ExtensionNameAndPrefix {
+ /**
+ * The extension name.
+ *
+ * See {@link Extension::name} for the format specification.
+ */
+ String name;
+ /**
+ * The extension prefix. Only the lowest 15 bits are used, so the value must be less than 32768.
+ */
+ char prefix;
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/ExtensionOperandTypeInformation.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/ExtensionOperandTypeInformation.aidl
new file mode 100644
index 0000000..d7f93c1
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/ExtensionOperandTypeInformation.aidl
@@ -0,0 +1,38 @@
+/*
+ * Copyright (C) 2020 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+
+package android.hardware.neuralnetworks;
+
+/**
+ * Information about an extension operand type.
+ */
+@VintfStability
+parcelable ExtensionOperandTypeInformation {
+ /**
+ * The extension operand type.
+ */
+ char type;
+ /**
+ * Indicates whether the extension operand type represents a tensor or a scalar.
+ */
+ boolean isTensor;
+ /**
+ * The byte size of the operand (if scalar) or of a single element (if tensor).
+ */
+ int byteSize;
+}
+
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/FusedActivationFunc.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/FusedActivationFunc.aidl
new file mode 100644
index 0000000..40f1053
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/FusedActivationFunc.aidl
@@ -0,0 +1,30 @@
+/*
+ * Copyright (C) 2020 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+
+package android.hardware.neuralnetworks;
+
+/**
+ * Fused activation function types.
+ */
+@VintfStability
+@Backing(type="int")
+enum FusedActivationFunc {
+ NONE,
+ RELU,
+ RELU1,
+ RELU6,
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/IBuffer.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/IBuffer.aidl
new file mode 100644
index 0000000..eb3dec6
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/IBuffer.aidl
@@ -0,0 +1,58 @@
+/*
+ * Copyright (C) 2020 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+
+package android.hardware.neuralnetworks;
+
+import android.hardware.neuralnetworks.Memory;
+
+/**
+ * This interface represents a device memory buffer.
+ */
+@VintfStability
+interface IBuffer {
+ /**
+ * Sets the content of this buffer from a shared memory region.
+ *
+ * @param src The source shared memory region.
+ * @param dimensions Updated dimensional information. If the dimensions of the IBuffer object
+ * are not fully specified, then the dimensions must be fully specified here.
+ * If the dimensions of the IBuffer object are fully specified, then the
+ * dimensions may be empty here. If dimensions.size() > 0, then all dimensions
+ * must be specified here, and any dimension that was specified in the IBuffer
+ * object must have the same value here.
+ * @throws ServiceSpecificException with one of the following ErrorStatus values:
+ * - DEVICE_UNAVAILABLE if driver is offline or busy
+ * - GENERAL_FAILURE if there is an unspecified error
+ * - INVALID_ARGUMENT if provided memory is invalid, or if the dimensions is invalid
+ */
+ void copyFrom(in Memory src, in int[] dimensions);
+
+ /**
+ * Retrieves the content of this buffer to a shared memory region.
+ *
+ * The IBuffer object must have been initialized before the call to IBuffer::copyTo. For more
+ * information on the state of the IBuffer object, refer to IDevice::allocate.
+ *
+ * @param dst The destination shared memory region.
+ * @throws ServiceSpecificException with one of the following ErrorStatus values:
+ * - DEVICE_UNAVAILABLE if driver is offline or busy
+ * - GENERAL_FAILURE if the IBuffer object is uninitialized, or there is an unspecified
+ * error
+ * - INVALID_ARGUMENT if provided memory is invalid
+ */
+ void copyTo(in Memory dst);
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/IDevice.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/IDevice.aidl
new file mode 100644
index 0000000..0c4954c
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/IDevice.aidl
@@ -0,0 +1,431 @@
+/*
+ * Copyright (C) 2020 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+
+package android.hardware.neuralnetworks;
+
+import android.hardware.neuralnetworks.BufferDesc;
+import android.hardware.neuralnetworks.BufferRole;
+import android.hardware.neuralnetworks.Capabilities;
+import android.hardware.neuralnetworks.DeviceBuffer;
+import android.hardware.neuralnetworks.DeviceType;
+import android.hardware.neuralnetworks.ExecutionPreference;
+import android.hardware.neuralnetworks.Extension;
+import android.hardware.neuralnetworks.IPreparedModel;
+import android.hardware.neuralnetworks.IPreparedModelCallback;
+import android.hardware.neuralnetworks.IPreparedModelParcel;
+import android.hardware.neuralnetworks.Model;
+import android.hardware.neuralnetworks.NumberOfCacheFiles;
+import android.hardware.neuralnetworks.Priority;
+
+/**
+ * This interface represents a device driver.
+ */
+@VintfStability
+interface IDevice {
+ /**
+ * The byte size of the cache token.
+ */
+ const int BYTE_SIZE_OF_CACHE_TOKEN = 32;
+ /**
+ * The maximum number of files for each type of cache in compilation caching.
+ */
+ const int MAX_NUMBER_OF_CACHE_FILES = 32;
+
+ /**
+ * Numeric values of extension operand and operation types have the following structure:
+ * - The sign bit is always 0.
+ * - 15 high bits represent the "prefix", which corresponds uniquely to the extension name.
+ * - 16 low bits represent the type ID within the extension.
+ */
+ const int EXTENSION_TYPE_HIGH_BITS_PREFIX = 15;
+ const int EXTENSION_TYPE_LOW_BITS_TYPE = 16;
+ /**
+ * OperandType with any value above {@link IDevice::OPERAND_TYPE_BASE_MAX} must be interpreted
+ * as an extension type according to {@link Model::extensionNameToPrefix}.
+ */
+ const int OPERAND_TYPE_BASE_MAX = 0xFFFF;
+ /**
+ * OperationType with any value above {@link IDevice::OPERATION_TYPE_BASE_MAX} must be
+ * interpreted as an extension type according to {@link Model::extensionNameToPrefix}.
+ */
+ const int OPERATION_TYPE_BASE_MAX = 0xFFFF;
+
+ /**
+ * Allocates a driver-managed buffer with the properties specified by the buffer descriptor as
+ * well as the input and output roles.
+ *
+ * The allocate function must verify its inputs are correct. If there is an error, or if a
+ * certain role or property is not supported by the driver, the allocate function must return a
+ * service specific exception with an appropriate ErrorStatus. If the allocation is successful,
+ * this method must return a DeviceBuffer object with the produced IBuffer and a positive token
+ * identifying the allocated buffer. A successful allocation must accommodate all of the
+ * specified roles and buffer properties.
+ *
+ * The buffer is allocated to an uninitialized state. An uninitialized buffer may only be used
+ * in ways that are specified by outputRoles. A buffer is initialized after it is used as an
+ * output in a successful execution, or after a successful invocation of IBuffer::copyFrom on
+ * the buffer. An initialized buffer may be used according to all roles specified in inputRoles
+ * and outputRoles. A buffer will return to the uninitialized state if it is used as an output
+ * in a failed execution, or after a failed invocation of IBuffer::copyFrom on the buffer.
+ *
+ * The dimensions of the buffer can be deduced from the buffer descriptor as well as the
+ * dimensions of the corresponding model operands of the input and output roles. The dimensions
+ * or rank of the buffer may be unknown at this stage. As such, some driver services may only
+ * create a placeholder and defer the actual allocation until execution time. Note that the same
+ * buffer may be used for different shapes of outputs on different executions. When the buffer
+ * is used as an input, the input shape must be the same as the output shape from the last
+ * execution using this buffer as an output.
+ *
+ * The driver must apply proper validatation upon every usage of the buffer, and must fail the
+ * execution immediately if the usage is illegal.
+ *
+ * @param desc A buffer descriptor specifying the properties of the buffer to allocate.
+ * @param preparedModels A vector of IPreparedModel objects. Must only contain IPreparedModel
+ * objects from the same IDevice as this method is being invoked on.
+ * @param inputRoles A vector of roles with each specifying an input to a prepared model.
+ * @param outputRoles A vector of roles with each specifying an output to a prepared model. Each
+ * role specified in inputRoles and outputRoles must be unique. The
+ * corresponding model operands of the roles must have the same OperandType,
+ * scale, zero point, and ExtraParams. The dimensions of the operands and the
+ * dimensions specified in the buffer descriptor must be compatible with each
+ * other. Two dimensions are incompatible if there is at least one axis that
+ * is fully specified in both but has different values.
+ * @return DeviceBuffer object containing the allocated IBuffer object and a positive token that
+ * can be used to reference the buffer as one of the memory pools.
+ * @throws ServiceSpecificException with one of the following ErrorStatus values:
+ * - DEVICE_UNAVAILABLE if driver is offline or busy
+ * - GENERAL_FAILURE if a certain buffer property or a certain role is not supported,
+ * or if there is an unspecified error
+ * - INVALID_ARGUMENT if one of the input arguments is invalid
+ * - RESOURCE_EXHAUSTED_* if the task was aborted by the driver
+ */
+ DeviceBuffer allocate(in BufferDesc desc, in IPreparedModelParcel[] preparedModels,
+ in BufferRole[] inputRoles, in BufferRole[] outputRoles);
+
+ /**
+ * Gets the capabilities of a driver.
+ *
+ * @return Capabilities of the driver.
+ * @throws ServiceSpecificException with one of the following ErrorStatus values:
+ * - DEVICE_UNAVAILABLE if driver is offline or busy
+ * - GENERAL_FAILURE if there is an unspecified error
+ */
+ Capabilities getCapabilities();
+
+ /**
+ * Gets the caching requirements of the driver implementation.
+ *
+ * There are two types of cache file descriptors provided to the driver: model cache and data
+ * cache.
+ *
+ * The data cache is for caching constant data, possibly including preprocessed and transformed
+ * tensor buffers. Any modification to the data cache should have no worse effect than
+ * generating bad output values at execution time.
+ *
+ * The model cache is for caching security-sensitive data such as compiled executable machine
+ * code in the device's native binary format. A modification to the model cache may affect the
+ * driver's execution behavior, and a malicious client could make use of this to execute beyond
+ * the granted permission. Thus, the driver must always check whether the model cache is
+ * corrupted before preparing the model from cache.
+ *
+ * getNumberOfCacheFilesNeeded returns how many of each type of cache files the driver
+ * implementation needs to cache a single prepared model. Returning 0 for both types indicates
+ * compilation caching is not supported by this driver. The driver may still choose not to cache
+ * certain compiled models even if it reports that caching is supported.
+ *
+ * If the device reports that caching is not supported, the user may avoid calling
+ * IDevice::prepareModelFromCache or providing cache file descriptors to
+ * IDevice::prepareModel.
+ *
+ * @return NumberOfCacheFiles structure indicating how many files for model and data cache the
+ * driver needs to cache a single prepared model. It must be less than or equal to
+ * MAX_NUMBER_OF_CACHE_FILES.
+ * @throws ServiceSpecificException with one of the following ErrorStatus values:
+ * - DEVICE_UNAVAILABLE if driver is offline or busy
+ * - GENERAL_FAILURE if there is an unspecified error
+ */
+ NumberOfCacheFiles getNumberOfCacheFilesNeeded();
+
+ /**
+ * Gets information about extensions supported by the driver implementation.
+ *
+ * All extension operations and operands must be fully supported for the extension to appear in
+ * the list of supported extensions.
+ *
+ * @return A list of supported extensions.
+ * @throws ServiceSpecificException with one of the following ErrorStatus values:
+ * - DEVICE_UNAVAILABLE if driver is offline or busy
+ * - GENERAL_FAILURE if there is an unspecified error
+ */
+ Extension[] getSupportedExtensions();
+
+ /**
+ * Gets the supported operations in a model.
+ *
+ * getSupportedOperations indicates which operations of the top-level subgraph are fully
+ * supported by the vendor driver. If an operation may not be supported for any reason,
+ * getSupportedOperations must return false for that operation.
+ *
+ * The {@link OperationType::IF} and {@link OperationType::WHILE} operations may only be fully
+ * supported if the vendor driver fully supports all operations in the referenced subgraphs.
+ *
+ * @param model A model whose operations -- and their corresponding operands -- are to be
+ * verified by the driver.
+ * @return A list of supported operations, where true indicates the operation is supported and
+ * false indicates the operation is not supported. The index of "supported" corresponds with
+ * the index of the operation it is describing in the main subgraph.
+ * @throws ServiceSpecificException with one of the following ErrorStatus values:
+ * - DEVICE_UNAVAILABLE if driver is offline or busy
+ * - GENERAL_FAILURE if there is an unspecified error
+ * - INVALID_ARGUMENT if provided model is invalid
+ */
+ boolean[] getSupportedOperations(in Model model);
+
+ /**
+ * Get the type of a given device.
+ *
+ * The device type can be used to help application developers to distribute Machine Learning
+ * workloads and other workloads such as graphical rendering. E.g., for an app which renders AR
+ * scenes based on real time object detection results, the developer could choose an ACCELERATOR
+ * type device for ML workloads, and reserve GPU for graphical rendering.
+ *
+ * @return The DeviceType of the device. Please note, this is not a bitfield of DeviceTypes.
+ * Each device must only be of a single DeviceType.
+ * @throws ServiceSpecificException with one of the following ErrorStatus values:
+ * - DEVICE_UNAVAILABLE if driver is offline or busy
+ * - GENERAL_FAILURE if the query resulted in an unspecified error
+ */
+ DeviceType getType();
+
+ /**
+ * Get the version string of the driver implementation.
+ *
+ * The version string must be a unique token among the set of version strings of drivers of a
+ * specific device. The token identifies the device driver's implementation. The token must not
+ * be confused with the feature level which is solely defined by the interface version. This API
+ * is opaque to the Android framework, but the Android framework may use the information for
+ * debugging or to pass on to NNAPI applications.
+ *
+ * Application developers sometimes have specific requirements to ensure good user experiences,
+ * and they need more information to make intelligent decisions when the Android framework
+ * cannot. For example, combined with the device name and other information, the token can help
+ * NNAPI applications filter devices based on their needs:
+ * - An application demands a certain level of performance, but a specific version of the
+ * driver cannot meet that requirement because of a performance regression.
+ * The application can disallow the driver based on the version provided.
+ * - An application has a minimum precision requirement, but certain versions of
+ * the driver cannot meet that requirement because of bugs or certain optimizations.
+ * The application can filter out versions of these drivers.
+ *
+ * @return The version string of the device implementation. Must have nonzero length.
+ * @throws ServiceSpecificException with one of the following ErrorStatus values:
+ * - DEVICE_UNAVAILABLE if driver is offline or busy
+ * - GENERAL_FAILURE if the query resulted in an unspecified error
+ */
+ String getVersionString();
+
+ /**
+ * Asynchronously creates a prepared model for execution and optionally saves it into cache
+ * files.
+ *
+ * prepareModel is used to make any necessary transformations to or alternative representations
+ * to a model for execution, possibly including transformations on the constant data,
+ * optimization on the model's graph, or compilation into the device's native binary format. The
+ * model itself is not changed.
+ *
+ * Optionally, caching information may be provided for the driver to save the prepared model to
+ * cache files for faster model compilation time when the same model preparation is requested in
+ * the future. There are two types of cache file descriptors provided to the driver: model cache
+ * and data cache. For more information on the two types of cache, refer to
+ * getNumberOfCacheFilesNeeded.
+ *
+ * The file descriptors must be opened with read and write permission. A file may have any size,
+ * and the corresponding file descriptor may have any offset. The driver must truncate a file to
+ * zero size before writing to that file. The file descriptors may be closed by the client once
+ * the asynchronous preparation has finished. The driver must dup a file descriptor if it wants
+ * to get access to the cache file later.
+ *
+ * The model is prepared asynchronously with respect to the caller. The prepareModel function
+ * must verify the inputs to the preparedModel function related to preparing the model (as
+ * opposed to saving the prepared model to cache) are correct. If there is an error,
+ * prepareModel must immediately invoke the callback with the appropriate ErrorStatus value and
+ * nullptr for the IPreparedModel, then return a status with a service specific exception with
+ * the same ErrorStatus. If the inputs to the prepareModel function that are related to
+ * preparing the model are valid and there is no error, prepareModel must launch an asynchronous
+ * task to prepare the model in the background, and immediately return from prepareModel. If the
+ * asynchronous task fails to launch, prepareModel must immediately invoke the callback with
+ * ErrorStatus::GENERAL_FAILURE and nullptr for the IPreparedModel, then return a service
+ * specific exception with ErrorStatus::GENERAL_FAILURE.
+ *
+ * When the asynchronous task has finished preparing the model, it must immediately invoke the
+ * callback function provided as an input to prepareModel. If the model was prepared
+ * successfully, the callback object must be invoked with an error status of ErrorStatus::NONE
+ * and the produced IPreparedModel object. If an error occurred preparing the model, the
+ * callback object must be invoked with the appropriate ErrorStatus value and nullptr for the
+ * IPreparedModel.
+ *
+ * The model is prepared with a priority. This priority is relative to other prepared models
+ * owned by the same client. Higher priority executions may use more compute resources than
+ * lower priority executions, and may preempt or starve lower priority executions.
+ *
+ * prepareModel can be called with an optional deadline. If the model is not able to be prepared
+ * before the provided deadline, the model preparation may be aborted, and either
+ * {@link ErrorStatus::MISSED_DEADLINE_TRANSIENT} or {@link
+ * ErrorStatus::MISSED_DEADLINE_PERSISTENT} may be returned. The error due to an abort must be
+ * sent the same way as other errors, described above.
+ *
+ * Optionally, the driver may save the prepared model to cache during the asynchronous
+ * preparation. Any error that occurs when saving to cache must not affect the status of
+ * preparing the model. Even if the input arguments related to the cache may be invalid, or the
+ * driver may fail to save to cache, the prepareModel function must finish preparing the model.
+ * The driver may choose not to save to cache even if the caching information is provided and
+ * valid.
+ *
+ * The only information that may be unknown to the model at this stage is the shape of the
+ * tensors, which may only be known at execution time. As such, some driver services may return
+ * partially prepared models, where the prepared model may only be finished when it is paired
+ * with a set of inputs to the model. Note that the same prepared model object may be used with
+ * different shapes of inputs on different (possibly concurrent) executions.
+ *
+ * Multiple threads may call prepareModel on the same model concurrently.
+ *
+ * @param model The model to be prepared for execution.
+ * @param preference Indicates the intended execution behavior of a prepared model.
+ * @param priority The priority of the prepared model relative to other prepared models owned by
+ * the client.
+ * @param deadline The time by which the model is expected to be prepared. The time is measured
+ * in nanoseconds since epoch of the steady clock (as from
+ * std::chrono::steady_clock). If the model cannot be prepared by the deadline,
+ * the preparation may be aborted. Passing -1 means the deadline is omitted.
+ * Other negative values are invalid.
+ * @param modelCache A vector of file descriptors for the security-sensitive cache. The length
+ * of the vector must either be 0 indicating that caching information is not
+ * provided, or match the numModelCache returned from
+ * getNumberOfCacheFilesNeeded. The cache file descriptors will be provided in
+ * the same order when retrieving the preparedModel from cache files with
+ * prepareModelFromCache.
+ * @param dataCache A vector of file descriptors for the constants' cache. The length of the
+ * vector must either be 0 indicating that caching information is not provided,
+ * or match the numDataCache returned from getNumberOfCacheFilesNeeded. The
+ * cache file descriptors will be provided in the same order when retrieving
+ * the preparedModel from cache files with prepareModelFromCache.
+ * @param token A caching token of length BYTE_SIZE_OF_CACHE_TOKEN identifying the prepared
+ * model. The same token will be provided when retrieving the prepared model from
+ * the cache files with prepareModelFromCache. Tokens should be chosen to have a
+ * low rate of collision for a particular application. The driver cannot detect a
+ * collision; a collision will result in a failed execution or in a successful
+ * execution that produces incorrect output values. If both modelCache and
+ * dataCache are empty indicating that caching information is not provided, this
+ * token must be ignored.
+ * @param callback A callback object used to return the error status of preparing the model for
+ * execution and the prepared model if successful, nullptr otherwise. The
+ * callback object's notify function must be called exactly once, even if the
+ * model could not be prepared.
+ * @throws ServiceSpecificException with one of the following ErrorStatus values:
+ * - DEVICE_UNAVAILABLE if driver is offline or busy
+ * - GENERAL_FAILURE if there is an unspecified error
+ * - INVALID_ARGUMENT if one of the input arguments related to preparing the model is
+ * invalid
+ * - MISSED_DEADLINE_* if the preparation is aborted because the model cannot be prepared by
+ * the deadline
+ * - RESOURCE_EXHAUSTED_* if the task was aborted by the driver
+ */
+ void prepareModel(in Model model, in ExecutionPreference preference, in Priority priority,
+ in long deadline, in ParcelFileDescriptor[] modelCache, in ParcelFileDescriptor[] dataCache,
+ in byte[] token, in IPreparedModelCallback callback);
+
+ /**
+ * Creates a prepared model from cache files for execution.
+ *
+ * prepareModelFromCache is used to retrieve a prepared model directly from cache files to avoid
+ * slow model compilation time. There are two types of cache file descriptors provided to the
+ * driver: model cache and data cache. For more information on the two types of cache files,
+ * refer to getNumberOfCacheFilesNeeded.
+ *
+ * The file descriptors must be opened with read and write permission. A file may have any size,
+ * and the corresponding file descriptor may have any offset. The driver must truncate a file to
+ * zero size before writing to that file. The file descriptors may be closed by the client once
+ * the asynchronous preparation has finished. The driver must dup a file descriptor if it wants
+ * to get access to the cache file later.
+ *
+ * The model is prepared asynchronously with respect to the caller. The prepareModelFromCache
+ * function must verify the inputs to the prepareModelFromCache function are correct, and that
+ * the security-sensitive cache has not been modified since it was last written by the driver.
+ * If there is an error, or if compilation caching is not supported, or if the
+ * security-sensitive cache has been modified, prepareModelFromCache must immediately invoke the
+ * callback with the appropriate ErrorStatus value and nullptr for the IPreparedModel, then
+ * return a status with a service specific exception with the same ErrorStatus. If the inputs to
+ * the prepareModelFromCache function are valid, the security-sensitive cache is not modified,
+ * and there is no error, prepareModelFromCache must launch an asynchronous task to prepare the
+ * model in the background, and immediately return from prepareModelFromCache. If the
+ * asynchronous task fails to launch, prepareModelFromCache must immediately invoke the callback
+ * with ErrorStatus::GENERAL_FAILURE and nullptr for the IPreparedModel, then return a service
+ * specific exception with ErrorStatus::GENERAL_FAILURE.
+ *
+ * When the asynchronous task has finished preparing the model, it must immediately invoke the
+ * callback function provided as an input to prepareModelFromCache. If the model was prepared
+ * successfully, the callback object must be invoked with an error status of ErrorStatus::NONE
+ * and the produced IPreparedModel object. If an error occurred preparing the model, the
+ * callback object must be invoked with the appropriate ErrorStatus value and nullptr for the
+ * IPreparedModel.
+ *
+ * prepareModelFromCache can be called with an optional deadline. If the model is not able to
+ * prepared before the provided deadline, the model preparation may be aborted, and either
+ * {@link ErrorStatus::MISSED_DEADLINE_TRANSIENT} or
+ * {@link ErrorStatus::MISSED_DEADLINE_PERSISTENT} may be returned. The error due to an abort
+ * must be sent the same way as other errors, described above.
+ *
+ * The only information that may be unknown to the model at this stage is the shape of the
+ * tensors, which may only be known at execution time. As such, some driver services may return
+ * partially prepared models, where the prepared model may only be finished when it is paired
+ * with a set of inputs to the model. Note that the same prepared model object may be used with
+ * different shapes of inputs on different (possibly concurrent) executions.
+ *
+ * @param deadline The time by which the model is expected to be prepared. The time is measured
+ * in nanoseconds since epoch of the steady clock (as from
+ * std::chrono::steady_clock). If the model cannot be prepared by the deadline,
+ * the preparation may be aborted. Passing -1 means the deadline is omitted.
+ * Other negative values are invalid.
+ * @param modelCache A vector of file descriptors for the security-sensitive cache. The length
+ * of the vector must match the numModelCache returned from
+ * getNumberOfCacheFilesNeeded. The cache file descriptors will be provided in
+ * the same order as with prepareModel.
+ * @param dataCache A vector of file descriptors for the constants' cache. The length of the
+ * vector must match the numDataCache returned from
+ * getNumberOfCacheFilesNeeded. The cache file descriptors will be provided in
+ * the same order as with prepareModel.
+ * @param token A caching token of length BYTE_SIZE_OF_CACHE_TOKEN identifying the prepared
+ * model. It is the same token provided when saving the cache files with
+ * prepareModel. Tokens should be chosen to have a low rate of collision for a
+ * particular application. The driver cannot detect a collision; a collision will
+ * result in a failed execution or in a successful execution that produces
+ * incorrect output values.
+ * @param callback A callback object used to return the error status of preparing the model for
+ * execution and the prepared model if successful, nullptr otherwise. The
+ * callback object's notify function must be called exactly once, even if the
+ * model could not be prepared.
+ * @throws ServiceSpecificException with one of the following ErrorStatus values:
+ * - DEVICE_UNAVAILABLE if driver is offline or busy
+ * - GENERAL_FAILURE if caching is not supported or if there is an unspecified error
+ * - INVALID_ARGUMENT if one of the input arguments is invalid
+ * - MISSED_DEADLINE_* if the preparation is aborted because the model cannot be prepared by
+ * the deadline
+ * - RESOURCE_EXHAUSTED_* if the task was aborted by the driver
+ */
+ void prepareModelFromCache(in long deadline, in ParcelFileDescriptor[] modelCache,
+ in ParcelFileDescriptor[] dataCache, in byte[] token, in IPreparedModelCallback callback);
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/IFencedExecutionCallback.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/IFencedExecutionCallback.aidl
new file mode 100644
index 0000000..47e5916
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/IFencedExecutionCallback.aidl
@@ -0,0 +1,56 @@
+/*
+ * Copyright (C) 2020 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+
+package android.hardware.neuralnetworks;
+
+import android.hardware.neuralnetworks.ErrorStatus;
+import android.hardware.neuralnetworks.Timing;
+
+/**
+ * IFencedExecutionCallback can be used to query the error status result and duration information
+ * from an IPreparedModel::executeFenced call.
+ */
+@VintfStability
+interface IFencedExecutionCallback {
+ /**
+ * The getExecutionInfo method is used by the clients to query error status result and duration
+ * information. The method must only be called after the actual evaluation has finished or
+ * resulted in an runtime error, as indicated by the status of the sync fence returned by the
+ * IPreparedModel::executeFenced call, otherwise GENERAL_FAILURE must be returned.
+ *
+ * @param out timingLaunched The duration starts when executeFenced is called and ends when
+ * executeFenced signals the returned syncFence. Unless measureTiming
+ * was set to true when launching the execution and status is NONE,
+ * all times must be reported as -1. A driver may choose to report any
+ * time as -1, indicating that particular measurement is not
+ * available.
+ * @param out timingFenced The duration starts when all waitFor sync fences have been signaled
+ * and ends when executeFenced signals the returned syncFence. Unless
+ * measureTiming was set to true when launching the execution and status
+ * is NONE, all times must be reported as -1. A driver may choose to
+ * report any time as -1, indicating that particular measurement is not
+ * available.
+ * @return Error status returned from the asynchronously dispatched execution must be:
+ * - NONE if the asynchronous execution was successful
+ * - DEVICE_UNAVAILABLE if driver is offline or busy
+ * - GENERAL_FAILURE if the asynchronous task resulted in an unspecified error
+ * - MISSED_DEADLINE_* if the execution is aborted because it cannot be completed by the
+ * deadline
+ * - RESOURCE_EXHAUSTED_* if the task was aborted by the driver
+ */
+ ErrorStatus getExecutionInfo(out Timing timingLaunched, out Timing timingFenced);
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/IPreparedModel.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/IPreparedModel.aidl
new file mode 100644
index 0000000..c1b2992
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/IPreparedModel.aidl
@@ -0,0 +1,173 @@
+/*
+ * Copyright (C) 2020 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+
+package android.hardware.neuralnetworks;
+
+import android.hardware.common.NativeHandle;
+import android.hardware.neuralnetworks.ErrorStatus;
+import android.hardware.neuralnetworks.ExecutionResult;
+import android.hardware.neuralnetworks.IFencedExecutionCallback;
+import android.hardware.neuralnetworks.Request;
+
+/**
+ * IPreparedModel describes a model that has been prepared for execution and is used to launch
+ * executions.
+ */
+@VintfStability
+interface IPreparedModel {
+ /**
+ * Each {@link OperationType::WHILE} operation in the model has an implicit execution timeout
+ * duration associated with it ("loop timeout duration"). This duration is configurable on a
+ * per-execution basis and must not exceed 15 seconds. The default value is 2 seconds. The units
+ * are nanoseconds.
+ */
+ const long DEFAULT_LOOP_TIMEOUT_DURATION_NS = 2000000000;
+ const long MAXIMUM_LOOP_TIMEOUT_DURATION_NS = 15000000000;
+
+ /**
+ * Performs a synchronous execution on a prepared model.
+ *
+ * The execution is performed synchronously with respect to the caller. executeSynchronously
+ * must verify the inputs to the function are correct, and the usages of memory pools allocated
+ * by IDevice::allocate are valid. If there is an error, executeSynchronously must immediately
+ * return a service specific exception with the appropriate ErrorStatus value. If the inputs to
+ * the function are valid and there is no error, executeSynchronously must perform the
+ * execution, and must not return until the execution is complete.
+ *
+ * The caller must not change the content of any data object referenced by 'request' (described
+ * by the {@link DataLocation} of a {@link RequestArgument}) until executeSynchronously returns.
+ * executeSynchronously must not change the content of any of the data objects corresponding to
+ * 'request' inputs.
+ *
+ * If the prepared model was prepared from a model wherein all tensor operands have fully
+ * specified dimensions, and the inputs to the function are valid, and at execution time every
+ * operation's input operands have legal values, then the execution should complete
+ * successfully: there must be no failure unless the device itself is in a bad state.
+ *
+ * executeSynchronously may be called with an optional deadline. If the execution is not able to
+ * be completed before the provided deadline, the execution may be aborted, and either
+ * {@link ErrorStatus::MISSED_DEADLINE_TRANSIENT} or {@link
+ * ErrorStatus::MISSED_DEADLINE_PERSISTENT} may be returned. The error due to an abort must be
+ * sent the same way as other errors, described above.
+ *
+ * Any number of calls to the execute* functions, in any combination, may be made concurrently,
+ * even on the same IPreparedModel object.
+ *
+ * @param request The input and output information on which the prepared model is to be
+ * executed.
+ * @param measure Specifies whether or not to measure duration of the execution. The duration
+ * runs from the time the driver sees the call to the executeSynchronously
+ * function to the time the driver returns from the function.
+ * @param deadline The time by which the execution is expected to complete. The time is measured
+ * in nanoseconds since epoch of the steady clock (as from
+ * std::chrono::steady_clock). If the execution cannot be finished by the
+ * deadline, the execution may be aborted. Passing -1 means the deadline is
+ * omitted. Other negative values are invalid.
+ * @param loopTimeoutDuration The maximum amount of time in nanoseconds that should be spent
+ * executing a {@link OperationType::WHILE} operation. If a loop
+ * condition model does not output false within this duration, the
+ * execution must be aborted. If -1 is provided, the maximum amount
+ * of time is {@link DEFAULT_LOOP_TIMEOUT_DURATION_NS}. Other
+ * negative values are invalid. When provided, the duration must not
+ * exceed {@link MAXIMUM_LOOP_TIMEOUT_DURATION_NS}.
+ * @return ExecutionResult parcelable, containing the status of the execution, output shapes and
+ * timing information.
+ * @throws ServiceSpecificException with one of the following ErrorStatus values:
+ * - DEVICE_UNAVAILABLE if driver is offline or busy
+ * - GENERAL_FAILURE if there is an unspecified error
+ * - INVALID_ARGUMENT if one of the input arguments is invalid
+ * - MISSED_DEADLINE_* if the execution is aborted because it cannot be completed by the
+ * deadline
+ * - RESOURCE_EXHAUSTED_* if the task was aborted by the driver
+ */
+ ExecutionResult executeSynchronously(in Request request, in boolean measureTiming,
+ in long deadline, in long loopTimeoutDuration);
+
+ /**
+ * Launch a fenced asynchronous execution on a prepared model.
+ *
+ * The execution is performed asynchronously with respect to the caller. executeFenced must
+ * verify the inputs to the function are correct, and the usages of memory pools allocated by
+ * IDevice::allocate are valid. If there is an error, executeFenced must immediately return a
+ * service specific exception with the corresponding ErrorStatus. If the inputs to the function
+ * are valid and there is no error, executeFenced must dispatch an asynchronous task to perform
+ * the execution in the background, assign a sync fence that will be signaled once the execution
+ * is completed and immediately return a callback that can be used by the client to query the
+ * duration and runtime error status. If the task has finished before the call returns,
+ * syncFence file descriptor may be set to -1. The execution must wait for all the sync fences
+ * (if any) in waitFor to be signaled before starting the actual execution.
+ *
+ * When the asynchronous task has finished its execution, it must immediately signal the
+ * syncFence returned from the executeFenced call. After the syncFence is signaled, the task
+ * must not modify the content of any data object referenced by 'request' (described by the
+ * {@link DataLocation} of a {@link RequestArgument}).
+ *
+ * executeFenced may be called with an optional deadline and an optional duration. If the
+ * execution is not able to be completed before the provided deadline or within the timeout
+ * duration (measured from when all sync fences in waitFor are signaled), whichever comes
+ * earlier, the execution may be aborted, and either
+ * {@link ErrorStatus::MISSED_DEADLINE_TRANSIENT} or {@link
+ * ErrorStatus::MISSED_DEADLINE_PERSISTENT} may be returned. The error due to an abort must be
+ * sent the same way as other errors, described above.
+ *
+ * If any of the sync fences in waitFor changes to error status after the executeFenced call
+ * succeeds, or the execution is aborted because it cannot finish before the deadline has been
+ * reached or the duration has elapsed, the driver must immediately set the returned syncFence
+ * to error status.
+ *
+ * Any number of calls to the execute* functions, in any combination, may be made concurrently,
+ * even on the same IPreparedModel object.
+ *
+ * @param request The input and output information on which the prepared model is to be
+ * executed. The outputs in the request must have fully specified dimensions.
+ * @param waitFor A vector of sync fence file descriptors. Execution must not start until all
+ * sync fences have been signaled.
+ * @param measure Specifies whether or not to measure duration of the execution.
+ * @param deadline The time by which the execution is expected to complete. The time is measured
+ * in nanoseconds since epoch of the steady clock (as from
+ * std::chrono::steady_clock).If the execution cannot be finished by the
+ * deadline, the execution may be aborted. Passing -1 means the deadline is
+ * omitted. Other negative values are invalid.
+ * @param loopTimeoutDuration The maximum amount of time in nanoseconds that should be spent
+ * executing a {@link OperationType::WHILE} operation. If a loop
+ * condition model does not output false within this duration, the
+ * execution must be aborted. If -1 is provided, the maximum amount
+ * of time is {@link DEFAULT_LOOP_TIMEOUT_DURATION_NS}. Other
+ * negative values are invalid. When provided, the duration must not
+ * exceed {@link MAXIMUM_LOOP_TIMEOUT_DURATION_NS}.
+ * @param duration The length of time in nanoseconds within which the execution is expected to
+ * complete after all sync fences in waitFor are signaled. If the execution
+ * cannot be finished within the duration, the execution may be aborted. Passing
+ * -1 means the duration is omitted. Other negative values are invalid.
+ * @param out syncFence The sync fence that will be signaled when the task is completed. The
+ * sync fence will be set to error if a critical error, e.g. hardware
+ * failure or kernel panic, occurs when doing execution.
+ * @return The IFencedExecutionCallback can be used to query information like duration and error
+ * status when the execution is completed.
+ * @throws ServiceSpecificException with one of the following ErrorStatus values:
+ * - DEVICE_UNAVAILABLE if driver is offline or busy
+ * - GENERAL_FAILURE if there is an unspecified error
+ * - INVALID_ARGUMENT if one of the input arguments is invalid, including fences in error
+ * states.
+ * - MISSED_DEADLINE_* if the execution is aborted because it cannot be completed by the
+ * deadline
+ * - RESOURCE_EXHAUSTED_* if the task was aborted by the driver
+ */
+ IFencedExecutionCallback executeFenced(in Request request, in ParcelFileDescriptor[] waitFor,
+ in boolean measureTiming, in long deadline, in long loopTimeoutDuration, in long duration,
+ out @nullable ParcelFileDescriptor syncFence);
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/IPreparedModelCallback.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/IPreparedModelCallback.aidl
new file mode 100644
index 0000000..adb4218
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/IPreparedModelCallback.aidl
@@ -0,0 +1,51 @@
+/*
+ * Copyright (C) 2020 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+
+package android.hardware.neuralnetworks;
+
+import android.hardware.neuralnetworks.ErrorStatus;
+import android.hardware.neuralnetworks.IPreparedModel;
+
+/**
+ * IPreparedModelCallback must be used to return a prepared model produced by an asynchronous task
+ * launched from IDevice::prepareModel*.
+ */
+@VintfStability
+interface IPreparedModelCallback {
+ /**
+ * Notify must be invoked immediately after the asynchronous task holding this callback has
+ * finished preparing the model. If the model was successfully prepared, the method must be
+ * invoked with ErrorStatus::NONE and the prepared model. If the model was not able to be
+ * successfully prepared, the method must be invoked with the appropriate ErrorStatus and
+ * nullptr as the IPreparedModel. If the asynchronous task holding this callback fails to launch
+ * or if the model provided to IDevice::prepareModel is invalid, notify method must be invoked
+ * with the appropriate error as well as nullptr for the IPreparedModel.
+ *
+ * @param status Error status returned from the asynchronous model preparation task; must be:
+ * - NONE if the asynchronous task successfully prepared the model
+ * - DEVICE_UNAVAILABLE if driver is offline or busy
+ * - GENERAL_FAILURE if the asynchronous task resulted in an unspecified error
+ * - INVALID_ARGUMENT if one of the input arguments to prepareModel is invalid
+ * - MISSED_DEADLINE_* if the preparation is aborted because the model cannot be
+ * prepared by the deadline
+ * - RESOURCE_EXHAUSTED_* if the task was aborted by the driver
+ * @param preparedModel A model that has been asynchronously prepared for execution. If the
+ * model was unable to be prepared due to an error, nullptr must be passed
+ * in place of the IPreparedModel object.
+ */
+ void notify(in ErrorStatus status, in IPreparedModel preparedModel);
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/IPreparedModelParcel.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/IPreparedModelParcel.aidl
new file mode 100644
index 0000000..f198c3f
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/IPreparedModelParcel.aidl
@@ -0,0 +1,28 @@
+/*
+ * Copyright (C) 2020 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+
+package android.hardware.neuralnetworks;
+
+import android.hardware.neuralnetworks.IPreparedModel;
+
+/**
+ * A parcelable for passing a vector of IPreparedModel objects.
+ */
+@VintfStability
+parcelable IPreparedModelParcel {
+ IPreparedModel preparedModel;
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/Memory.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/Memory.aidl
new file mode 100644
index 0000000..8ecb067
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/Memory.aidl
@@ -0,0 +1,31 @@
+/*
+ * Copyright (C) 2020 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package android.hardware.neuralnetworks;
+import android.hardware.common.NativeHandle;
+
+import android.os.ParcelFileDescriptor;
+
+/**
+ * A type that is used to pass pieces of shared memory between processes.
+ * The type structure mimics hidl_memory type from HIDL.
+ */
+@VintfStability
+parcelable Memory {
+ NativeHandle handle;
+ long size;
+ String name;
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/Model.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/Model.aidl
new file mode 100644
index 0000000..3bb7318
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/Model.aidl
@@ -0,0 +1,70 @@
+/*
+ * Copyright (C) 2020 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+
+package android.hardware.neuralnetworks;
+
+import android.hardware.neuralnetworks.ExtensionNameAndPrefix;
+import android.hardware.neuralnetworks.Subgraph;
+import android.hardware.neuralnetworks.Memory;
+
+/**
+ * A Neural Network Model.
+ *
+ * This includes not only the execution graph, but also constant data such as weights or scalars
+ * added at construction time. The only information that may not be known is the shape of the input
+ * tensors.
+ */
+@VintfStability
+parcelable Model {
+ /**
+ * The top-level subgraph.
+ */
+ Subgraph main;
+ /**
+ * Referenced subgraphs.
+ *
+ * Each subgraph is referenced by the main subgraph or at least one other referenced subgraph.
+ *
+ * There must be no reference cycles.
+ */
+ Subgraph[] referenced;
+ /**
+ * A byte buffer containing operand data that were copied into the model.
+ *
+ * An operand's value must be located here if and only if Operand::lifetime equals
+ * OperandLifeTime::CONSTANT_COPY.
+ */
+ byte[] operandValues;
+ /**
+ * A collection of shared memory pools containing operand values.
+ *
+ * An operand's value must be located here if and only if Operand::lifetime equals
+ * OperandLifeTime::CONSTANT_POOL.
+ */
+ Memory[] pools;
+ /**
+ * 'true' indicates TENSOR_FLOAT32 may be calculated with range and/or precision as low as that
+ * of the IEEE 754 16-bit floating-point format.
+ * 'false' indicates TENSOR_FLOAT32 must be calculated using at least the range and precision of
+ * the IEEE 754 32-bit floating-point format.
+ */
+ boolean relaxComputationFloat32toFloat16;
+ /**
+ * The mapping between extension names and prefixes of operand and operation type values.
+ */
+ ExtensionNameAndPrefix[] extensionNameToPrefix;
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/NumberOfCacheFiles.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/NumberOfCacheFiles.aidl
new file mode 100644
index 0000000..1ca2676
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/NumberOfCacheFiles.aidl
@@ -0,0 +1,27 @@
+/*
+ * Copyright (C) 2020 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package android.hardware.neuralnetworks;
+
+/**
+ * Structure indicating how many files for model and numDataCache cache the driver needs to cache a
+ * single prepared model.
+ */
+@VintfStability
+parcelable NumberOfCacheFiles {
+ int numModelCache;
+ int numDataCache;
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/Operand.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/Operand.aidl
new file mode 100644
index 0000000..243a89d
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/Operand.aidl
@@ -0,0 +1,113 @@
+/*
+ * Copyright (C) 2020 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+
+package android.hardware.neuralnetworks;
+
+import android.hardware.neuralnetworks.DataLocation;
+import android.hardware.neuralnetworks.OperandExtraParams;
+import android.hardware.neuralnetworks.OperandLifeTime;
+import android.hardware.neuralnetworks.OperandType;
+
+/**
+ * Describes one operand of the model's graph.
+ */
+@VintfStability
+parcelable Operand {
+ /**
+ * The data type.
+ *
+ * Besides the values listed in {@link OperandType}, any value above
+ * {@link IDevice::OPERAND_TYPE_BASE_MAX} is possible and should be interpreted as an extension
+ * type according to {@link Model::extensionNameToPrefix}.
+ */
+ OperandType type;
+ /**
+ * Dimensions of the operand.
+ *
+ * For a scalar operand, dimensions.size() must be 0.
+ *
+ * A tensor operand with all dimensions specified has "fully specified" dimensions. Whenever
+ * possible (i.e., whenever the dimensions are known at model construction time), a tensor
+ * operand should have (but is not required to have) fully specified dimensions, in order to
+ * enable the best possible performance.
+ *
+ * If a tensor operand's dimensions are not fully specified, the dimensions of the operand are
+ * deduced from the operand dimensions and values of the operation for which that operand is an
+ * output or from the corresponding {@link OperationType::IF} or {@link OperationType::WHILE}
+ * operation input operand dimensions in the case of referenced subgraph input operands.
+ *
+ * In the following situations, a tensor operand's dimensions must be fully specified:
+ *
+ * . The operand has lifetime CONSTANT_COPY or CONSTANT_POOL.
+ *
+ * . The operand has lifetime SUBGRAPH_INPUT and belongs to the main subgraph. Fully
+ * specified dimensions must either be present in the Operand or they must be provided in
+ * the corresponding RequestArgument.
+ * EXCEPTION: If the input is optional and omitted (by setting the hasNoValue field of the
+ * corresponding RequestArgument to true) then it need not have fully specified
+ * dimensions.
+ *
+ * A tensor operand with some number of unspecified dimensions is represented by setting each
+ * unspecified dimension to 0.
+ *
+ * A tensor operand with unspecified rank is represented by providing an empty dimensions
+ * vector.
+ */
+ int[] dimensions;
+ /**
+ * Quantized scale of the operand.
+ *
+ * Must be 0 when not applicable to an operand type.
+ *
+ * See {@link OperandType}.
+ */
+ float scale;
+ /**
+ * Quantized zero-point offset of the operand.
+ *
+ * Must be 0 when not applicable to an operand type.
+ *
+ * See {@link OperandType}.
+ */
+ int zeroPoint;
+ /**
+ * How the operand is used.
+ */
+ OperandLifeTime lifetime;
+ /**
+ * Where to find the data for this operand.
+ * If the lifetime is TEMPORARY_VARIABLE, SUBGRAPH_INPUT, SUBGRAPH_OUTPUT, or NO_VALUE:
+ * - All the fields must be 0.
+ * If the lifetime is CONSTANT_COPY:
+ * - location.poolIndex is 0.
+ * - location.offset is the offset in bytes into Model.operandValues.
+ * - location.length is set.
+ * If the lifetime is CONSTANT_POOL:
+ * - location.poolIndex is set.
+ * - location.offset is the offset in bytes into the specified pool.
+ * - location.length is set.
+ * If the lifetime is SUBGRAPH:
+ * - location.poolIndex is 0.
+ * - location.offset is the index of the referenced subgraph in {@link Model::referenced}.
+ * - location.length is 0.
+ */
+ DataLocation location;
+ /**
+ * Additional parameters specific to a particular operand type.
+ */
+ @nullable OperandExtraParams extraParams;
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/OperandExtraParams.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/OperandExtraParams.aidl
new file mode 100644
index 0000000..b0112ae
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/OperandExtraParams.aidl
@@ -0,0 +1,40 @@
+/*
+ * Copyright (C) 2020 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+
+package android.hardware.neuralnetworks;
+
+import android.hardware.neuralnetworks.SymmPerChannelQuantParams;
+
+/**
+ * Parameters specific to a particular operand type.
+ */
+@VintfStability
+union OperandExtraParams {
+ /**
+ * Symmetric per-channel quantization parameters.
+ *
+ * Only applicable to operands of type TENSOR_QUANT8_SYMM_PER_CHANNEL.
+ */
+ SymmPerChannelQuantParams channelQuant;
+ /**
+ * Extension operand parameters.
+ *
+ * The framework treats this as an opaque data blob.
+ * The format is up to individual extensions.
+ */
+ byte[] extension;
+}
\ No newline at end of file
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/OperandLifeTime.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/OperandLifeTime.aidl
new file mode 100644
index 0000000..63d1971
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/OperandLifeTime.aidl
@@ -0,0 +1,63 @@
+/*
+ * Copyright (C) 2020 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+
+package android.hardware.neuralnetworks;
+
+/**
+ * How an operand is used.
+ */
+@VintfStability
+@Backing(type="int")
+enum OperandLifeTime {
+ /**
+ * The operand is internal to the model. It's created by an operation and consumed by other
+ * operations. It must be an output operand of exactly one operation.
+ */
+ TEMPORARY_VARIABLE,
+ /**
+ * The operand is an input of a subgraph. It must not be an output operand of any operation.
+ *
+ * An operand can't be both input and output of a subgraph.
+ */
+ SUBGRAPH_INPUT,
+ /**
+ * The operand is an output of a subgraph. It must be an output operand of exactly one
+ * operation.
+ *
+ * An operand can't be both input and output of a subgraph.
+ */
+ SUBGRAPH_OUTPUT,
+ /**
+ * The operand is a constant found in Model.operandValues. It must not be an output operand of
+ * any operation.
+ */
+ CONSTANT_COPY,
+ /**
+ * The operand is a constant that was specified via a Memory object. It must not be an output
+ * operand of any operation.
+ */
+ CONSTANT_POOL,
+ /**
+ * The operand does not have a value. This is valid only for optional arguments of operations.
+ */
+ NO_VALUE,
+ /**
+ * The operand is a reference to a subgraph. It must be an input to one or more
+ * {@link OperationType::IF} or {@link OperationType::WHILE} operations.
+ */
+ SUBGRAPH,
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/OperandPerformance.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/OperandPerformance.aidl
new file mode 100644
index 0000000..9a8c2cc
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/OperandPerformance.aidl
@@ -0,0 +1,31 @@
+/*
+ * Copyright (C) 2020 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+
+package android.hardware.neuralnetworks;
+
+import android.hardware.neuralnetworks.OperandType;
+import android.hardware.neuralnetworks.PerformanceInfo;
+
+/**
+ * Driver performance when operating on a particular data type. In the case of float32 data, this is
+ * used when the calculations are not relaxed.
+ */
+@VintfStability
+parcelable OperandPerformance {
+ OperandType type;
+ PerformanceInfo info;
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/OperandType.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/OperandType.aidl
new file mode 100644
index 0000000..9274b6f
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/OperandType.aidl
@@ -0,0 +1,154 @@
+/*
+ * Copyright (C) 2020 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+
+package android.hardware.neuralnetworks;
+
+/**
+ * Operand types.
+ *
+ * The type of an operand in a model.
+ *
+ * Types prefaced with TENSOR_* must be used for tensor data (i.e., tensors
+ * with at least one dimension). Types not prefaced by TENSOR_* represent
+ * scalar values and must have no dimensions.
+ */
+@VintfStability
+@Backing(type="int")
+enum OperandType {
+ /**
+ * A 32 bit floating point scalar value.
+ */
+ FLOAT32 = 0,
+ /**
+ * A signed 32 bit integer scalar value.
+ */
+ INT32 = 1,
+ /**
+ * An unsigned 32 bit integer scalar value.
+ */
+ UINT32 = 2,
+ /**
+ * A tensor of 32 bit floating point values.
+ */
+ TENSOR_FLOAT32 = 3,
+ /**
+ * A tensor of 32 bit integer values.
+ */
+ TENSOR_INT32 = 4,
+ /**
+ * A tensor of 8 bit unsigned integers that represent real numbers.
+ *
+ * Attached to this tensor are two numbers that can be used to convert the 8 bit integer to the
+ * real value and vice versa. These two numbers are:
+ * - scale: a 32 bit floating point value greater than zero.
+ * - zeroPoint: a 32 bit integer, in range [0, 255].
+ *
+ * The formula is:
+ * real_value = (integer_value - zeroPoint) * scale.
+ */
+ TENSOR_QUANT8_ASYMM = 5,
+ /**
+ * An 8 bit boolean scalar value.
+ *
+ * Values of this operand type are either true or false. A zero value represents false; any
+ * other value represents true.
+ */
+ BOOL = 6,
+ /**
+ * A tensor of 16 bit signed integers that represent real numbers.
+ *
+ * Attached to this tensor is a number representing real value scale that is used to convert the
+ * 16 bit number to a real value in the following way:
+ * realValue = integerValue * scale.
+ *
+ * scale is a 32 bit floating point with value greater than zero.
+ */
+ TENSOR_QUANT16_SYMM = 7,
+ /**
+ * A tensor of IEEE 754 16 bit floating point values.
+ */
+ TENSOR_FLOAT16 = 8,
+ /**
+ * A tensor of 8 bit boolean values.
+ *
+ * Values of this operand type are either true or false. A zero value represents false; any
+ * other value represents true.
+ */
+ TENSOR_BOOL8 = 9,
+ /**
+ * An IEEE 754 16 bit floating point scalar value.
+ */
+ FLOAT16 = 10,
+ /**
+ * A tensor of 8 bit signed integers that represent real numbers.
+ *
+ * This tensor is associated with additional fields that can be used to convert the 8 bit signed
+ * integer to the real value and vice versa. These fields are:
+ * - channelDim: a 32 bit unsigned integer indicating channel dimension.
+ * - scales: an array of positive 32 bit floating point values.
+ * The size of the scales array must be equal to dimensions[channelDim].
+ *
+ * {@link SymmPerChannelQuantParams} must hold the parameters for an Operand of this type.
+ * The channel dimension of this tensor must not be unknown (dimensions[channelDim] != 0).
+ *
+ * The formula is:
+ * realValue[..., C, ...] =
+ * integerValue[..., C, ...] * scales[C]
+ * where C is an index in the Channel dimension.
+ */
+ TENSOR_QUANT8_SYMM_PER_CHANNEL = 11,
+ /**
+ * A tensor of 16 bit unsigned integers that represent real numbers.
+ *
+ * Attached to this tensor are two numbers that can be used to convert the 16 bit integer to the
+ * real value and vice versa. These two numbers are:
+ * - scale: a 32 bit floating point value greater than zero.
+ * - zeroPoint: a 32 bit integer, in range [0, 65535].
+ *
+ * The formula is:
+ * real_value = (integer_value - zeroPoint) * scale.
+ */
+ TENSOR_QUANT16_ASYMM = 12,
+ /**
+ * A tensor of 8 bit signed integers that represent real numbers.
+ *
+ * Attached to this tensor is a number representing real value scale that is used to convert the
+ * 8 bit number to a real value in the following way:
+ * realValue = integerValue * scale.
+ *
+ * scale is a 32 bit floating point with value greater than zero.
+ */
+ TENSOR_QUANT8_SYMM = 13,
+ /**
+ * A tensor of 8 bit signed integers that represent real numbers.
+ *
+ * Attached to this tensor are two numbers that can be used to convert the 8 bit integer to the
+ * real value and vice versa. These two numbers are:
+ * - scale: a 32 bit floating point value greater than zero.
+ * - zeroPoint: a 32 bit integer, in range [-128, 127].
+ *
+ * The formula is:
+ * real_value = (integer_value - zeroPoint) * scale.
+ */
+ TENSOR_QUANT8_ASYMM_SIGNED = 14,
+ /**
+ * A reference to a subgraph.
+ *
+ * Must have the lifetime {@link OperandLifeTime::SUBGRAPH}.
+ */
+ SUBGRAPH = 15,
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/Operation.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/Operation.aidl
new file mode 100644
index 0000000..acfb4b7
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/Operation.aidl
@@ -0,0 +1,46 @@
+/*
+ * Copyright (C) 2020 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+
+package android.hardware.neuralnetworks;
+
+import android.hardware.neuralnetworks.OperationType;
+
+/**
+ * Describes one operation of the model's graph.
+ */
+@VintfStability
+parcelable Operation {
+ /**
+ * The operation type.
+ *
+ * Besides the values listed in {@link OperationType}, any value above
+ * {@link IDevice::OPERATION_TYPE_BASE_MAX} is possible and should be interpreted as an
+ * extension type according to {@link Model::extensionNameToPrefix}.
+ */
+ OperationType type;
+ /**
+ * Describes the table that contains the indexes of the inputs of the operation. The offset is
+ * the index in the operandIndexes table.
+ */
+ int[] inputs;
+ /**
+ * Describes the table that contains the indexes of the outputs of the operation. The offset is
+ * the index in the operandIndexes table.
+ */
+ int[] outputs;
+}
+
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/OperationType.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/OperationType.aidl
new file mode 100644
index 0000000..fd9da67
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/OperationType.aidl
@@ -0,0 +1,5132 @@
+/*
+ * Copyright (C) 2020 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+
+package android.hardware.neuralnetworks;
+
+/**
+ * Operation types.
+ *
+ * The type of an operation in a model.
+ */
+@VintfStability
+@Backing(type="int")
+enum OperationType {
+ /**
+ * Adds two tensors, element-wise.
+ *
+ * Takes two input tensors of identical {@link OperandType} and compatible
+ * dimensions. The output is the sum of both input tensors, optionally
+ * modified by an activation function.
+ *
+ * Two dimensions are compatible when:
+ * 1. they are equal, or
+ * 2. one of them is 1
+ *
+ * The size of the output is the maximum size along each dimension of the
+ * input operands. It starts with the trailing dimensions, and works its
+ * way forward.
+ *
+ * Example:
+ *
+ * input1.dimension = {4, 1, 2}
+ * input2.dimension = {5, 4, 3, 1}
+ * output.dimension = {5, 4, 3, 2}
+ *
+ * Since HAL version 1.2, generic zero-sized input tensor is supported. Zero
+ * dimension is only compatible with 0 or 1. The size of the output
+ * dimension is zero if either of corresponding input dimension is zero.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ * * {@link OperandType::TENSOR_INT32} (since HAL version 1.3)
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: A tensor.
+ * * 1: A tensor of the same {@link OperandType}, and compatible dimensions
+ * as input0.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scales and zeroPoint can be different from input0 scale and zeroPoint.
+ * * 2: An {@link OperandType::INT32} scalar, and has to be one of the
+ * {@link FusedActivationFunc} values. Specifies the activation to
+ * invoke on the result.
+ * For a {@link OperandType::TENSOR_INT32} tensor,
+ * the {@link FusedActivationFunc} must be "NONE".
+ *
+ * Outputs:
+ * * 0: The sum, a tensor of the same {@link OperandType} as input0.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint can be different from inputs' scale and zeroPoint.
+ */
+ ADD = 0,
+ /**
+ * Performs a 2-D average pooling operation.
+ *
+ * The output dimensions are functions of the filter dimensions, stride, and
+ * padding.
+ *
+ * The values in the output tensor are computed as:
+ *
+ * output[b, i, j, channel] =
+ * sum_{di, dj}(
+ * input[b, strides[1] * i + di, strides[2] * j + dj, channel]
+ * ) / sum(1)
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
+ * With the default data layout NHWC, the data is stored in the order of:
+ * [batch, height, width, channels]. Alternatively, the data layout could
+ * be NCHW, the data storage order of: [batch, channels, height, width].
+ * NCHW is supported since HAL version 1.2.
+ *
+ * Both explicit padding and implicit padding are supported.
+ *
+ * Inputs (explicit padding):
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
+ * the input.
+ * Since HAL version 1.2, zero batches is supported for this tensor.
+ * * 1: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the left, in the ‘width’ dimension.
+ * * 2: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the right, in the ‘width’ dimension.
+ * * 3: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the top, in the ‘height’ dimension.
+ * * 4: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the bottom, in the ‘height’ dimension.
+ * * 5: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘width’ dimension.
+ * * 6: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘height’ dimension.
+ * * 7: An {@link OperandType::INT32} scalar, specifying the filter
+ * width.
+ * * 8: An {@link OperandType::INT32} scalar, specifying the filter
+ * height.
+ * * 9: An {@link OperandType::INT32} scalar, and has to be one of the
+ * {@link FusedActivationFunc} values. Specifies the activation to
+ * invoke on the result.
+ * * 10: An optional {@link OperandType::BOOL} scalar, default to false.
+ * Set to true to specify NCHW data layout for input0 and output0.
+ * Available since HAL version 1.2.
+ *
+ * Inputs (implicit padding):
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
+ * the input.
+ * Since HAL version 1.2, zero batches is supported for this tensor.
+ * * 1: An {@link OperandType::INT32} scalar, specifying the implicit
+ * padding scheme, has to be one of the
+ * following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
+ * * 2: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘width’ dimension.
+ * * 3: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘height’ dimension.
+ * * 4: An {@link OperandType::INT32} scalar, specifying the filter
+ * width.
+ * * 5: An {@link OperandType::INT32} scalar, specifying the filter
+ * height.
+ * * 6: An {@link OperandType::INT32} scalar, and has to be one of the
+ * {@link FusedActivationFunc} values. Specifies the activation to
+ * invoke on the result.
+ * * 7: An optional {@link OperandType::BOOL} scalar, default to false.
+ * Set to true to specify NCHW data layout for input0 and output0.
+ * Available since HAL version 1.2.
+ *
+ * Outputs:
+ * * 0: The output 4-D tensor, of shape
+ * [batches, out_height, out_width, depth].
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint must be the same as input0.
+ */
+ AVERAGE_POOL_2D = 1,
+ /**
+ * Concatenates the input tensors along the given dimension.
+ *
+ * The input tensors must have identical {@link OperandType} and the same
+ * dimensions except the dimension along the concatenation axis.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * (full support since HAL version 1.2, see the input section)
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0 ~ n-1: The list of n input tensors, of shape
+ * [D0, D1, ..., Daxis(i), ..., Dm].
+ * Before HAL version 1.2, all input tensors of
+ * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * must have the same scale and zeroPoint as the output tensor.
+ * Input tensors of
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED}
+ * are allowed to have different scale and zeroPoint.
+ * Since HAL version 1.2, zero-sized tensors are supported.
+ * * n: An {@link OperandType::INT32} scalar, specifying the
+ * concatenation axis.
+ *
+ * Outputs:
+ * * 0: The output, a tensor of the same {@link OperandType} as the input
+ * tensors. The output shape is [D0, D1, ..., sum(Daxis(i)), ..., Dm].
+ * Since HAL version 1.2, for a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint values can be different from
+ * input tensors. Before HAL version 1.2 they have to be the same as for the input tensors.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint values can be different from input tensors.
+ */
+ CONCATENATION = 2,
+ /**
+ * Performs a 2-D convolution operation.
+ *
+ * The CONV_2D op sweeps a 2-D filter that can mix channels together over a
+ * batch of images, applying the filter to each window of each image of the
+ * appropriate size.
+ *
+ * The output dimensions are functions of the filter dimensions, stride, and
+ * padding.
+ *
+ * The values in the output tensor are computed as:
+ *
+ * output[b, i, j, channel] =
+ * sum_{di, dj, k} (
+ * input[b, strides[1] * i + di, strides[2] * j + dj, k] *
+ * filter[channel, di, dj, k]
+ * ) + bias[channel]
+ *
+ * Supported tensor {@link OperandType} configurations:
+ * * 32 bit floating point:
+ * * * {@link OperandType::TENSOR_FLOAT32} for input, filter, output, and bias.
+ *
+ * * Quantized:
+ * * * {@link OperandType::TENSOR_QUANT8_ASYMM} for input, filter, and output.
+ * * * {@link OperandType::TENSOR_INT32} for bias (with scale set to
+ * * * input.scale * filter.scale).
+ *
+ * Available since HAL version 1.2:
+ * * 16 bit floating point:
+ * * * {@link OperandType::TENSOR_FLOAT16} for input, filter, output, and bias.
+ *
+ * * Quantized with symmetric per channel quantization for the filter:
+ * * * {@link OperandType::TENSOR_QUANT8_ASYMM} for input, and output.
+ * * * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter.
+ * * * {@link OperandType::TENSOR_INT32} for bias (scale set to 0.0,
+ * * * each value scaling is separate and equal to input.scale * filter.scales[channel]).
+ *
+ * Available since HAL version 1.3:
+ * * Quantized signed (since HAL version 1.3):
+ * * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} for input, filter, and output.
+ * * * {@link OperandType::TENSOR_INT32} for bias (with scale set to
+ * * * input.scale * filter.scale).
+ *
+ * * Quantized signed with filter symmetric per channel quantization (since HAL version 1.3):
+ * * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} for input, and output.
+ * * * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter.
+ * * * {@link OperandType::TENSOR_INT32} for bias (scale set to 0.0,
+ * * * each value scaling is separate and equal to input.scale * filter.scales[channel]).
+ *
+ * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
+ * With the default data layout NHWC, the data is stored in the order of:
+ * [batch, height, width, channels]. Alternatively, the data layout could
+ * be NCHW, the data storage order of: [batch, channels, height, width].
+ * NCHW is supported since HAL version 1.2.
+ *
+ * Both explicit padding and implicit padding are supported.
+ *
+ * Inputs (explicit padding):
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
+ * specifying the input.
+ * Since HAL version 1.2, zero batches is supported for this tensor.
+ * * 1: A 4-D tensor, of shape
+ * [depth_out, filter_height, filter_width, depth_in], specifying the
+ * filter.
+ * For tensor of type {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}
+ * the channel dimension (SymmPerChannelQuantParams::channelDim)
+ * must be set to 0.
+ * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
+ * tensor of type {@link OperandType::TENSOR_FLOAT32}
+ * or {@link OperandType::TENSOR_FLOAT16} the bias must be of the same type.
+ * For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * and {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED},
+ * the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint
+ * of 0 and bias_scale == input_scale * filter_scale.
+ * For filter tensor of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL},
+ * the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0
+ * and bias_scale of 0. The actual scale of each value 'i' is equal to
+ * bias_scale[i] = input_scale * filter_scale[i].
+ * * 3: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the left, in the ‘width’ dimension.
+ * * 4: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the right, in the ‘width’ dimension.
+ * * 5: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the top, in the ‘height’ dimension.
+ * * 6: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the bottom, in the ‘height’ dimension.
+ * * 7: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘width’ dimension.
+ * * 8: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘height’ dimension.
+ * * 9: An {@link OperandType::INT32} scalar, and has to be one of the
+ * {@link FusedActivationFunc} values. Specifies the activation to
+ * invoke on the result.
+ * * 10: An optional {@link OperandType::BOOL} scalar, default to false.
+ * Set to true to specify NCHW data layout for input0 and output0.
+ * Available since HAL version 1.2.
+ * * 11: An optional {@link OperandType::INT32} scalar, specifying the dilation
+ * factor for width. Defaults to 1. If set to k > 1, there will be k-1 skipped
+ * cells between each filter element on width dimension. If this input is set,
+ * input 12 (dilation factor for height) must be specified as well.
+ * Available since HAL version 1.2.
+ * * 12: An optional {@link OperandType::INT32} scalar, specifying the dilation
+ * factor for height. Defaults to 1. If set to k > 1, there will be k-1 skipped
+ * cells between each filter element on height dimension. If this input is set,
+ * input 11 (dilation factor for width) must be specified as well.
+ * Available since HAL version 1.2.
+ *
+ * Inputs (implicit padding):
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
+ * specifying the input.
+ * Since HAL version 1.2, zero batches is supported for this tensor.
+ * * 1: A 4-D tensor, of shape
+ * [depth_out, filter_height, filter_width, depth_in], specifying the
+ * filter.
+ * For tensor of type {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}
+ * the channel dimension (SymmPerChannelQuantParams::channelDim)
+ * must be set to 0.
+ * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
+ * tensor of type {@link OperandType::TENSOR_FLOAT32}
+ * or {@link OperandType::TENSOR_FLOAT16} the bias must be of the same
+ * type.
+ * For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * and {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED},
+ * the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint
+ * of 0 and bias_scale == input_scale * filter_scale.
+ * For filter tensor of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL},
+ * the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0
+ * and bias_scale of 0. The actual scale of each value 'i' is equal to
+ * bias_scale[i] = input_scale * filter_scale[i].
+ * * 3: An {@link OperandType::INT32} scalar, specifying the implicit
+ * padding scheme, has to be one of the
+ * following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
+ * * 4: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘width’ dimension.
+ * * 5: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘height’ dimension.
+ * * 6: An {@link OperandType::INT32} scalar, and has to be one of the
+ * {@link FusedActivationFunc} values. Specifies the activation to
+ * invoke on the result.
+ * * 7: An optional {@link OperandType::BOOL} scalar, default to false.
+ * Set to true to specify NCHW data layout for input0 and output0.
+ * Available since HAL version 1.2.
+ * * 8: An optional {@link OperandType::INT32} scalar, specifying the dilation
+ * factor for width. Defaults to 1. If set to k > 1, there will be k-1 skipped
+ * cells between each filter element on width dimension. If this input is set,
+ * input 9 (dilation factor for height) must be specified as well.
+ * Available since HAL version 1.2.
+ * * 9: An optional {@link OperandType::INT32} scalar, specifying the dilation
+ * factor for height. Defaults to 1. If set to k > 1, there will be k-1 skipped
+ * cells between each filter element on height dimension. If this input is set,
+ * input 8 (dilation factor for width) must be specified as well.
+ * Available since HAL version 1.2.
+ *
+ * Outputs:
+ * * 0: The output 4-D tensor, of shape
+ * [batches, out_height, out_width, depth_out].
+ * Before HAL version 1.2, for output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
+ * the following condition must be satisfied: output_scale > input_scale * filter_scale
+ */
+ CONV_2D = 3,
+ /**
+ * Performs a depthwise 2-D convolution operation.
+ *
+ * Given an input tensor of shape [batches, height, width, depth_in] and a
+ * filter tensor of shape [1, filter_height, filter_width, depth_out]
+ * containing depth_out convolutional filters of depth 1, DEPTHWISE_CONV
+ * applies a different filter to each input channel (expanding from 1
+ * channel to channel_multiplier channels for each), then concatenates the
+ * results together.
+ *
+ * The output has depth_out = depth_in * depth_multiplier channels.
+ * The output dimensions are functions of the filter dimensions, stride, and
+ * padding.
+ *
+ * The values in the output tensor are computed as:
+ *
+ * output[b, i, j, k * channel_multiplier + q] =
+ * sum_{di, dj} (
+ * input[b, strides[1] * i + di, strides[2] * j + dj, k] *
+ * filter[1, di, dj, k * channel_multiplier + q]
+ * ) + bias[k * channel_multiplier + q]
+ *
+ * Supported tensor {@link OperandType} configurations:
+ * * 32 bit floating point:
+ * * * {@link OperandType::TENSOR_FLOAT32} for input, filter, output, and bias.
+ *
+ * * Quantized:
+ * * * {@link OperandType::TENSOR_QUANT8_ASYMM} for input, filter, and output.
+ * * * {@link OperandType::TENSOR_INT32} for bias (with scale set to
+ * * * input.scale * filter.scale).
+ *
+ * Available since HAL version 1.2:
+ * * 16 bit floating point:
+ * * * {@link OperandType::TENSOR_FLOAT16} for input, filter, output, and bias.
+ *
+ * * Quantized with symmetric per channel quantization for the filter:
+ * * * {@link OperandType::TENSOR_QUANT8_ASYMM} for input, and output.
+ * * * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter.
+ * * * {@link OperandType::TENSOR_INT32} for bias (scale set to 0.0,
+ * * * each value scaling is separate and equal to input.scale * filter.scales[channel]).
+ *
+ * Available since HAL version 1.3:
+ * * Quantized signed (since HAL version 1.3):
+ * * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} for input, filter, and output.
+ * * * {@link OperandType::TENSOR_INT32} for bias (with scale set to
+ * * * input.scale * filter.scale).
+ *
+ * * Quantized signed with filter symmetric per channel quantization (since HAL version 1.3):
+ * * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} for input, and output.
+ * * * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter.
+ * * * {@link OperandType::TENSOR_INT32} for bias (scale set to 0.0,
+ * * * each value scaling is separate and equal to input.scale * filter.scales[channel]).
+ *
+ * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
+ * With the default data layout NHWC, the data is stored in the order of:
+ * [batch, height, width, channels]. Alternatively, the data layout could
+ * be NCHW, the data storage order of: [batch, channels, height, width].
+ * NCHW is supported since HAL version 1.2.
+ *
+ * Both explicit padding and implicit padding are supported.
+ *
+ * Inputs (explicit padding):
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
+ * specifying the input.
+ * * 1: A 4-D tensor, of shape [1, filter_height, filter_width, depth_out],
+ * specifying the filter.
+ * For tensor of type {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}
+ * the channel dimension (SymmPerChannelQuantParams::channelDim)
+ * must be set to 3.
+ * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
+ * tensor of type {@link OperandType::TENSOR_FLOAT32}
+ * or {@link OperandType::TENSOR_FLOAT16} the bias must be of the same type.
+ * For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * and {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED},
+ * the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint
+ * of 0 and bias_scale == input_scale * filter_scale.
+ * For filter tensor of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL},
+ * the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0
+ * and bias_scale of 0. The actual scale of each value 'i' is equal to
+ * bias_scale[i] = input_scale * filter_scale[i].
+ * * 3: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the left, in the ‘width’ dimension.
+ * * 4: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the right, in the ‘width’ dimension.
+ * * 5: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the top, in the ‘height’ dimension.
+ * * 6: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the bottom, in the ‘height’ dimension.
+ * * 7: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘width’ dimension.
+ * * 8: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘height’ dimension.
+ * * 9: An {@link OperandType::INT32} scalar, specifying the depthwise
+ * multiplier.
+ * * 10: An {@link OperandType::INT32} scalar, and has to be one of the
+ * {@link FusedActivationFunc} values. Specifies the activation to
+ * invoke on the result.
+ * * 11: An optional {@link OperandType::BOOL} scalar, default to false.
+ * Set to true to specify NCHW data layout for input0 and output0.
+ * Available since HAL version 1.2.
+ * * 12: An optional {@link OperandType::INT32} scalar, specifying the dilation
+ * factor for width. Defaults to 1. If set to k > 1, there will be k-1 skipped
+ * cells between each filter element on width dimension. If this input is set,
+ * input 13 (dilation factor for height) must be specified as well.
+ * Available since HAL version 1.2.
+ * * 13: An optional {@link OperandType::INT32} scalar, specifying the dilation
+ * factor for height. Defaults to 1. If set to k > 1, there will be k-1 skipped
+ * cells between each filter element on height dimension. If this input is set,
+ * input 12 (dilation factor for width) must be specified as well.
+ * Available since HAL version 1.2.
+ *
+ * Inputs (implicit padding):
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
+ * specifying the input.
+ * * 1: A 4-D tensor, of shape [1, filter_height, filter_width, depth_out],
+ * specifying the filter.
+ * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
+ * tensor of type {@link OperandType::TENSOR_FLOAT32}
+ * or {@link OperandType::TENSOR_FLOAT16} the bias must be of the same type.
+ * For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * and {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED},
+ * the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint
+ * of 0 and bias_scale == input_scale * filter_scale.
+ * For filter tensor of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL},
+ * the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0
+ * and bias_scale of 0. The actual scale of each value 'i' is equal to
+ * bias_scale[i] = input_scale * filter_scale[i].
+ * * 3: An {@link OperandType::INT32} scalar, specifying the implicit
+ * padding scheme, has to be one of the
+ * following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
+ * * 4: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘width’ dimension.
+ * * 5: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘height’ dimension.
+ * * 6: An {@link OperandType::INT32} scalar, specifying the depthwise
+ * multiplier.
+ * * 7: An {@link OperandType::INT32} scalar, and has to be one of the
+ * {@link FusedActivationFunc} values. Specifies the activation to
+ * invoke on the result.
+ * * 8: An optional {@link OperandType::BOOL} scalar, default to false.
+ * Set to true to specify NCHW data layout for input0 and output0.
+ * Available since HAL version 1.2.
+ * * 9: An optional {@link OperandType::INT32} scalar, specifying the dilation
+ * factor for width. Defaults to 1. If set to k > 1, there will be k-1 skipped
+ * cells between each filter element on width dimension. If this input is set,
+ * input 10 (dilation factor for height) must be specified as well.
+ * Available since HAL version 1.2.
+ * * 10: An optional {@link OperandType::INT32} scalar, specifying the dilation
+ * factor for height. Defaults to 1. If set to k > 1, there will be k-1 skipped
+ * cells between each filter element on height dimension. If this input is set,
+ * input 9 (dilation factor for width) must be specified as well.
+ * Available since HAL version 1.2.
+ *
+ * Outputs:
+ * * 0: The output 4-D tensor, of shape
+ * [batches, out_height, out_width, depth_out]. Before HAL version 1.2, for
+ * output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
+ * the following condition must be satisfied:
+ * output_scale > input_scale * filter_scale
+ */
+ DEPTHWISE_CONV_2D = 4,
+ /**
+ * Rearranges data from depth into blocks of spatial data.
+ *
+ * More specifically, this op outputs a copy of the input tensor where
+ * values from the depth dimension are moved in spatial blocks to the height
+ * and width dimensions. The value block_size indicates the input block size
+ * and how the data is moved.
+ *
+ * Chunks of data of size block_size * block_size from depth are rearranged
+ * into non-overlapping blocks of size block_size x block_size.
+ *
+ * The width of the output tensor is input_depth * block_size, whereas the
+ * height is input_height * block_size. The depth of the input tensor must
+ * be divisible by block_size * block_size
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
+ * With the default data layout NHWC, the data is stored in the order of:
+ * [batch, height, width, channels]. Alternatively, the data layout could
+ * be NCHW, the data storage order of: [batch, channels, height, width].
+ * NCHW is supported since HAL version 1.2.
+ *
+ * Inputs:
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
+ * specifying the input.
+ * * 1: An {@link OperandType::INT32} scalar, specifying the block_size.
+ * block_size must be >=1 and block_size * block_size must be a divisor
+ * of the input depth.
+ * * 2: An optional {@link OperandType::BOOL} scalar, default to false.
+ * Set to true to specify NCHW data layout for input0 and output0.
+ * Available since HAL version 1.2.
+ *
+ * Outputs:
+ * * 0: The output 4-D tensor, of shape [batch, height*block_size,
+ * width*block_size, depth/(block_size*block_size)].
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint must be the same as input0.
+ */
+ DEPTH_TO_SPACE = 5,
+ /**
+ * Dequantizes the input tensor.
+ *
+ * The formula is:
+ *
+ * output = (input - zeroPoint) * scale.
+ *
+ * Supported input tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_SYMM} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported output tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_FLOAT32}.
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: A tensor.
+ * Since HAL version 1.2, this tensor may be zero-sized.
+ *
+ * Outputs:
+ * * 0: A tensor with the same shape as input0.
+ */
+ DEQUANTIZE = 6,
+ /**
+ * Looks up sub-tensors in the input tensor.
+ *
+ * This operator takes for input a tensor of values (Values) and
+ * a one-dimensional tensor of selection indices (Lookups).
+ * The output tensor is the concatenation of sub-tensors of Values as
+ * selected by Lookups.
+ *
+ * Think of Values as being sliced along its first dimension:
+ * The entries in Lookups select which slices are concatenated together
+ * to create the output tensor.
+ *
+ * For example, if Values has shape of [40, 200, 300] and
+ * Lookups has shape of [3], all three values found in Lookups are
+ * expected to be between 0 and 39. The resulting tensor must
+ * have shape of [3, 200, 300].
+ *
+ * If a value in Lookups is out of bounds, the operation must fail
+ * and an error must be reported.
+ *
+ * Supported value tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.3)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported value tensor rank: from 2
+ *
+ * Inputs:
+ * * 0: Lookups. A 1-D tensor of {@link OperandType::TENSOR_INT32}.
+ * The values are indices into the first dimension of Values.
+ * * 1: Values. An n-D tensor, where n >= 2, from which sub-tensors are
+ * extracted.
+ *
+ * Output:
+ * * 0: A n-D tensor with the same rank and shape as the Values
+ * tensor, except for the first dimension which has the same size
+ * as Lookups' only dimension.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint must be the same as input1.
+ */
+ EMBEDDING_LOOKUP = 7,
+ /**
+ * Computes element-wise floor() on the input tensor.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: A tensor.
+ *
+ * Outputs:
+ * * 0: The output tensor, of the same {@link OperandType} and dimensions as
+ * the input tensor.
+ */
+ FLOOR = 8,
+ /**
+ * Denotes a fully (densely) connected layer, which connects all elements
+ * in the input tensor with each element in the output tensor.
+ *
+ * This layer implements the operation:
+ *
+ * outputs = activation(inputs * weights’ + bias)
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: up to 4.
+ *
+ * Inputs:
+ * * 0: A tensor of at least rank 2, specifying the input. If rank is
+ * greater than 2, then it gets flattened to a 2-D Tensor. The
+ * (flattened) 2-D Tensor is reshaped (if necessary) to
+ * [batch_size, input_size], where "input_size" corresponds to the
+ * number of inputs to the layer, matching the second dimension of
+ * weights, and "batch_size" is calculated by dividing the number of
+ * elements by "input_size".
+ * Since HAL version 1.2, zero batch_size is supported for this tensor.
+ * * 1: A 2-D tensor, specifying the weights, of shape
+ * [num_units, input_size], where "num_units" corresponds to the number
+ * of output nodes.
+ * * 2: A 1-D tensor, of shape [num_units], specifying the bias. For input
+ * tensor of {@link OperandType::TENSOR_FLOAT32}, the bias should
+ * also be of {@link OperandType::TENSOR_FLOAT32}.
+ * For input tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * and {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED},
+ * the bias should be of {@link OperandType::TENSOR_INT32},
+ * with zeroPoint of 0 and bias_scale == input_scale * filter_scale.
+ * * 3: An {@link OperandType::INT32} scalar, and has to be one of the
+ * {@link FusedActivationFunc} values. Specifies the activation to
+ * invoke on the result.
+ *
+ * Outputs:
+ * * 0: The output tensor, of shape [batch_size, num_units]. Before HAL version 1.2, for
+ * output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}, the following
+ * condition must be satisfied: output_scale > input_scale * filter_scale.
+ */
+ FULLY_CONNECTED = 9,
+ /**
+ * Looks up sub-tensors in the input tensor using a key-value map.
+ *
+ * This operator takes for input a tensor of values (Values),
+ * a one-dimensional tensor of selection values (Lookups) and
+ * a one-dimensional tensor that maps these values to Values
+ * indexes. The output tensor is the concatenation of sub-tensors of
+ * Values as selected by Lookups via Keys.
+ *
+ * Think of Values as being sliced along its outer-most dimension.
+ * The output is a concatenation of selected slices, with one slice
+ * for each entry of Lookups. The slice selected is the one at the
+ * same index as the Maps entry that matches the value in Lookups.
+ *
+ * For a hit, the corresponding sub-tensor of Values is included
+ * in the Output tensor. For a miss, the corresponding sub-tensor in
+ * Output must have zero values.
+ *
+ * For example, if Values has shape of [40, 200, 300],
+ * Keys should have a shape of [40]. If Lookups tensor has shape
+ * of [3], three slices are being concatenated, so the resulting tensor
+ * must have the shape of [3, 200, 300]. If the first entry in Lookups
+ * has the value 123456, that value must be located in Keys tensor.
+ * If the sixth entry of Keys contains 123456, the sixth slice of Values
+ * must be selected. If no entry in Keys has 123456, a slice of zeroes
+ * must be concatenated.
+ *
+ * Supported value tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
+ * Supported value tensor rank: from 2
+ *
+ * Inputs:
+ * * 0: Lookups. A 1-D {@link OperandType::TENSOR_INT32} tensor with
+ * shape [ k ].
+ * * 1: Keys. A 1-D {@link OperandType::TENSOR_INT32} tensor with shape
+ * [ n ]; Keys and Values pair represent a map, i.e., the ith element
+ * in Keys (Keys[i]) is the key to select the ith sub-tensor in Values
+ * (Values[i]), where 0 <= i <= n-1. Keys tensor *MUST* be sorted in
+ * ascending order.
+ * * 2: Values. A tensor with shape of [ n, … ]; i.e., the first dimension
+ * must be n.
+ *
+ * Outputs:
+ * * 0: Output. A tensor with shape [ k …].
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint must be the same as input2.
+ * * 1: Hits. A boolean tensor with shape [ k ] indicates whether the lookup
+ * hits (True) or not (False).
+ * Stored as {@link OperandType::TENSOR_QUANT8_ASYMM} with offset 0
+ * and scale 1.0f.
+ * A non-zero byte represents True, a hit. A zero indicates otherwise.
+ */
+ HASHTABLE_LOOKUP = 10,
+ /**
+ * Applies L2 normalization along the axis dimension.
+ *
+ * The values in the output tensor are computed as:
+ *
+ * output[batch, row, col, channel] =
+ * input[batch, row, col, channel] /
+ * sqrt(sum_{c} pow(input[batch, row, col, c], 2))
+ *
+ * By default the axis dimension is the last dimension of the input tensor.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: up to 4
+ * Tensors with rank less than 4 are only supported since HAL version 1.2.
+ *
+ * Inputs:
+ * * 0: An n-D tensor, specifying the tensor to be normalized.
+ * * 1: An optional {@link OperandType::INT32} scalar, default to -1,
+ * specifying the dimension normalization would be performed on.
+ * Negative index is used to specify axis from the end (e.g. -1 for
+ * the last axis). Must be in the range [-n, n).
+ * Available since HAL version 1.2.
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} and same shape as input0.
+ * For {@link OperandType::TENSOR_QUANT8_ASYMM},
+ * the scale must be 1.f / 128 and the zeroPoint must be 128.
+ * For {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED},
+ * the scale must be 1.f / 128 and the zeroPoint must be 0.
+ *
+ * NOTE: Before HAL version 1.3, if the elements along an axis are all zeros,
+ * the result is undefined. Since HAL version 1.3, if the elements along an axis
+ * are all zeros, the result is logical zero.
+ */
+ L2_NORMALIZATION = 11,
+ /**
+ * Performs an 2-D L2 pooling operation.
+ *
+ * The output dimensions are functions of the filter dimensions, stride, and
+ * padding.
+ *
+ * The values in the output tensor are computed as:
+ *
+ * output[b, i, j, c] =
+ * sqrt(sum_{di, dj} pow(input[b, strides[1] * i + di, strides[2] * j + dj, c], 2) /
+ * sum(1))
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
+ * With the default data layout NHWC, the data is stored in the order of:
+ * [batch, height, width, channels]. Alternatively, the data layout could
+ * be NCHW, the data storage order of: [batch, channels, height, width].
+ * NCHW is supported since HAL version 1.2.
+ *
+ * Both explicit padding and implicit padding are supported.
+ *
+ * Inputs (explicit padding):
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
+ * the input.
+ * Since HAL version 1.2, zero batches is supported for this tensor.
+ * * 1: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the left, in the ‘width’ dimension.
+ * * 2: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the right, in the ‘width’ dimension.
+ * * 3: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the top, in the ‘height’ dimension.
+ * * 4: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the bottom, in the ‘height’ dimension.
+ * * 5: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘width’ dimension.
+ * * 6: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘height’ dimension.
+ * * 7: An {@link OperandType::INT32} scalar, specifying the filter
+ * width.
+ * * 8: An {@link OperandType::INT32} scalar, specifying the filter
+ * height.
+ * * 9: An {@link OperandType::INT32} scalar, and has to be one of the
+ * {@link FusedActivationFunc} values. Specifies the activation to
+ * invoke on the result.
+ * * 10: An optional {@link OperandType::BOOL} scalar, default to false.
+ * Set to true to specify NCHW data layout for input0 and output0.
+ * Available since HAL version 1.2.
+ *
+ * Inputs (implicit padding):
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
+ * the input.
+ * Since HAL version 1.2, zero batches is supported for this tensor.
+ * * 1: An {@link OperandType::INT32} scalar, specifying the implicit
+ * padding scheme, has to be one of the
+ * following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
+ * * 2: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘width’ dimension.
+ * * 3: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘height’ dimension.
+ * * 4: An {@link OperandType::INT32} scalar, specifying the filter
+ * width.
+ * * 5: An {@link OperandType::INT32} scalar, specifying the filter
+ * height.
+ * * 6: An {@link OperandType::INT32} scalar, and has to be one of the
+ * {@link FusedActivationFunc} values. Specifies the activation to
+ * invoke on the result.
+ * * 7: An optional {@link OperandType::BOOL} scalar, default to false.
+ * Set to true to specify NCHW data layout for input0 and output0.
+ * Available since HAL version 1.2.
+ *
+ * Outputs:
+ * * 0: The output 4-D tensor, of shape
+ * [batches, out_height, out_width, depth].
+ */
+ L2_POOL_2D = 12,
+ /**
+ * Applies Local Response Normalization along the depth dimension.
+ *
+ * The 4-D input tensor is treated as a 3-D array of 1-D vectors (along the
+ * last dimension), and each vector is normalized independently. Within a
+ * given vector, each component is divided by the weighted, squared sum of
+ * inputs within depth_radius.
+ *
+ * The output is calculated using this formula:
+ *
+ * sqr_sum[a, b, c, d] = sum(
+ * pow(input[a, b, c, d - depth_radius : d + depth_radius + 1], 2))
+ * output = input / pow((bias + alpha * sqr_sum), beta)
+ *
+ * For input tensor with rank less than 4, independently normalizes each
+ * 1-D slice along specified dimension.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: up to 4
+ * Tensors with rank less than 4 are only supported since HAL version 1.2.
+ *
+ * Inputs:
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
+ * the input.
+ * * 1: An {@link OperandType::INT32} scalar, specifying the radius of
+ * the normalization window.
+ * * 2: A scalar, specifying the bias, must not be zero.
+ * For input tensor of {@link OperandType::TENSOR_FLOAT16}, the bias
+ * value must be of {@link OperandType::FLOAT16}.
+ * For input tensor of {@link OperandType::TENSOR_FLOAT32}, the bias
+ * value must be of {@link OperandType::FLOAT32}.
+ * * 3: A scalar, specifying the scale factor, alpha.
+ * For input tensor of {@link OperandType::TENSOR_FLOAT16}, the
+ * alpha value must be of {@link OperandType::FLOAT16}.
+ * For input tensor of {@link OperandType::TENSOR_FLOAT32}, the
+ * alpha value must be of {@link OperandType::FLOAT32}.
+ * * 4: A scalar, specifying the exponent, beta.
+ * For input tensor of {@link OperandType::TENSOR_FLOAT16}, the beta
+ * value must be of {@link OperandType::FLOAT16}.
+ * For input tensor of {@link OperandType::TENSOR_FLOAT32}, the beta
+ * value must be of {@link OperandType::FLOAT32}.
+ * * 5: An optional {@link OperandType::INT32} scalar, default to -1,
+ * specifying the dimension normalization would be performed on.
+ * Negative index is used to specify axis from the end (e.g. -1 for
+ * the last axis). Must be in the range [-n, n).
+ * Available since HAL version 1.2.
+ *
+ * Outputs:
+ * * 0: The output tensor of same shape as input0.
+ */
+ LOCAL_RESPONSE_NORMALIZATION = 13,
+ /**
+ * Computes sigmoid activation on the input tensor element-wise.
+ *
+ * The output is calculated using this formula:
+ *
+ * output = 1 / (1 + exp(-input))
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: up to 4.
+ *
+ * Inputs:
+ * * 0: A tensor, specifying the input.
+ * Since HAL version 1.2, this tensor may be zero-sized.
+ *
+ * Outputs:
+ * * 0: The output tensor of same shape as input0.
+ * For {@link OperandType::TENSOR_QUANT8_ASYMM},
+ * the scale must be 1.f / 256 and the zeroPoint must be 0.
+ * For {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED},
+ * the scale must be 1.f / 256 and the zeroPoint must be -128.
+ */
+ LOGISTIC = 14,
+ /**
+ * Projects an input to a bit vector via locality senstive hashing.
+ *
+ * Supported input tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ *
+ * Supported input tensor rank: from 1
+ *
+ * Inputs:
+ * * 0: Hash functions. Dim.size == 2, DataType: Float.
+ * Tensor[0].Dim[0]: 15 of hash functions.
+ * Tensor[0].Dim[1]: 16 of projected output bits generated by each
+ * hash function.
+ * If the projection type is Sparse:
+ * Tensor[0].Dim[1] + ceil(log2(Tensor[0].Dim[0])) <= 32
+ *
+ * * 1: Input. Dim.size >= 1, no restriction on DataType.
+ * * 2: Weight. Optional. Dim.size == 1, DataType: Float.
+ * If not set, each input element is considered to have the same weight
+ * of 1.0.
+ * Tensor[1].Dim[0] == Tensor[2].Dim[0]
+ * * 3: Type:
+ * Sparse:
+ * Value LSHProjectionType_SPARSE(=3) (since HAL version 1.2).
+ * Computed bit vector is considered to be sparse.
+ * Each output element is an int32 made up of multiple bits
+ * computed from hash functions.
+ *
+ * NOTE: To avoid collisions across hash functions, an offset value
+ * of k * (1 << Tensor[0].Dim[1]) will be added to each signature,
+ * where k is the index of the hash function.
+ *
+ * Value LSHProjectionType_SPARSE_DEPRECATED(=1).
+ * Legacy behavior that does not include the offset value.
+ *
+ * Dense:
+ * Value LSHProjectionType_DENSE(=2).
+ * Computed bit vector is considered to be dense. Each output
+ * element represents a bit and can take the value of either
+ * 0 or 1.
+ *
+ * Outputs:
+ * * 0: If the projection type is Sparse:
+ * Output.Dim == { Tensor[0].Dim[0] }
+ * A tensor of int32 that represents hash signatures.
+ *
+ * If the projection type is Dense:
+ * Output.Dim == { Tensor[0].Dim[0] * Tensor[0].Dim[1] }
+ * A flattened tensor that represents projected bit vectors.
+ * The offset value for sparse projections was added in HAL version 1.2.
+ */
+ LSH_PROJECTION = 15,
+ /**
+ * Performs a single time step in a Long Short-Term Memory (LSTM) layer
+ *
+ * The LSTM operation is described by the following equations.
+ *
+ * \f{eqnarray*}{
+ * i_t =& \sigma(W_{xi}x_t+W_{hi}h_{t-1}+W_{ci}C_{t-1}+b_i) & \\
+ * f_t =& \sigma(W_{xf}x_t+W_{hf}h_{t-1}+W_{cf}C_{t-1}+b_f) & \\
+ * C_t =& clip(f_t \odot C_{t-1} + i_t \odot
+ * g(W_{xc}x_t+W_{hc}h_{t-1}+b_c),\ t_{cell}) & \\
+ * o_t =& \sigma(W_{xo}x_t+W_{ho}h_{t-1}+W_{co}C_t+b_o) & \\
+ * & & \\
+ * & clip(W_{proj}(o_t \odot g(C_t))+b_{proj},\ t_{proj})
+ * & if\ there\ is\ a\ projection; \\
+ * h_t =& & \\
+ * & o_t \odot g(C_t) & otherwise. \\
+ * \f}
+ * Where:
+ * * \f$x_t\f$ is the input,
+ * * \f$i_t\f$ is the input gate,
+ * * \f$f_t\f$ is the forget gate,
+ * * \f$C_t\f$ is the cell state,
+ * * \f$o_t\f$ is the output,
+ * * \f$h_t\f$ is the output state,
+ * * \f$\sigma\f$ is the logistic sigmoid function,
+ * * \f$g\f$ is the cell input and cell output activation function, usually
+ * \f$tahn\f$,
+ * * \f$W_{xi}\f$ is the input-to-input weight matrix,
+ * * \f$W_{hi}\f$ is the recurrent to input weight matrix,
+ * * \f$W_{ci}\f$ is the cell-to-input weight matrix,
+ * * \f$b_i\f$ is the input gate bias,
+ * * \f$W_{xf}\f$ is the input-to-forget weight matrix,
+ * * \f$W_{hf}\f$ is the recurrent-to-forget weight matrix,
+ * * \f$W_{cf}\f$ is the cell-to-forget weight matrix,
+ * * \f$b_f\f$ is the forget gate bias,
+ * * \f$W_{xc}\f$ is the input-to-cell weight matrix,
+ * * \f$W_{hc}\f$ is the recurrent-to-cell weight matrix,
+ * * \f$b_c\f$ is the cell bias,
+ * * \f$W_{xo}\f$ is the input-to-output weight matrix,
+ * * \f$W_{ho}\f$ is the recurrent-to-output weight matrix,
+ * * \f$W_{co}\f$ is the cell-to-output weight matrix,
+ * * \f$b_o\f$ is the output gate bias,
+ * * \f$W_{proj}\f$ is the projection weight matrix,
+ * * \f$b_{proj}\f$ is the projection bias,
+ * * \f$t_{cell}\f$ is the threshold for clipping the cell state, and
+ * * \f$t_{proj}\f$ is the threshold for clipping the projected output.
+ * * \f$\odot\f$ is the
+ * <a href="https://en.wikipedia.org/wiki/Hadamard_product_(matrices)">
+ * Hadamard product</a> that takes two matrices and produces another
+ * matrix, each element of which is the product of the corresponding
+ * elements of the input matrices.
+ *
+ * Since HAL version 1.2 LSTM supports layer normalization.
+ * In case layer normalization is used, the inputs to internal activation
+ * functions (sigmoid and \f$g\f$) are normalized, rescaled and recentered
+ * following an approach from section 3.1 from
+ * https://arxiv.org/pdf/1607.06450.pdf
+ *
+ * The operation has the following independently optional inputs:
+ * * The cell-to-input weights (\f$W_{ci}\f$), cell-to-forget weights
+ * (\f$W_{cf}\f$) and cell-to-output weights (\f$W_{co}\f$) either all
+ * have values or neither of them have values (i.e., all set to null). If
+ * they have values, the peephole optimization is used.
+ * * The input-to-input weights (\f$W_{xi}\f$), recurrent-to-input weights
+ * (\f$W_{hi}\f$) and input gate bias (\f$b_i\f$) either all have values,
+ * or none of them have values. If they have no values, coupling of input
+ * and forget gates (CIFG) is used, in which case the input gate
+ * (\f$i_t\f$) is calculated using the following equation instead.
+ * \f{eqnarray*}{
+ * i_t = 1 - f_t
+ * \f}
+ * In case peephole optimization is used and CIFG is not used
+ * cell-to-input (\f$W_{ci}\f$) weights must be present. Otherwise, the
+ * cell-to-input weights must have no value.
+ * * The projection weights (\f$W_{proj}\f$) is required only for the
+ * recurrent projection layer, and should otherwise have no value.
+ * * The projection bias (\f$b_{proj}\f$) may (but not required to) have a
+ * value if the recurrent projection layer exists, and should otherwise
+ * have no value.
+ * * (HAL version 1.2 or later) The four layer normalization weights either all have
+ * values or none of them have values. Additionally, if CIFG is used,
+ * input layer normalization weights tensor is omitted and the other layer
+ * normalization weights either all have values or none of them have
+ * values. Layer normalization is used when the values of all the layer
+ * normalization weights are present.
+ *
+ * References:
+ *
+ * The default non-peephole non-CIFG implementation is based on:
+ * http://www.bioinf.jku.at/publications/older/2604.pdf
+ * S. Hochreiter and J. Schmidhuber. "Long Short-Term Memory". Neural
+ * Computation, 9(8):1735-1780, 1997.
+ *
+ * The peephole implementation and projection layer is based on:
+ * https://research.google.com/pubs/archive/43905.pdf
+ * Hasim Sak, Andrew Senior, and Francoise Beaufays. "Long short-term memory
+ * recurrent neural network architectures for large scale acoustic
+ * modeling." INTERSPEECH, 2014.
+ * (However, the concept of peephole optimization was introduced in work
+ * prior to this paper.)
+ *
+ * The coupling of input and forget gate (CIFG) is based on:
+ * http://arxiv.org/pdf/1503.04069.pdf
+ * Greff et al. "LSTM: A Search Space Odyssey"
+ *
+ * The layer normalization is based on:
+ * https://arxiv.org/pdf/1607.06450.pdf
+ * Jimmy Ba et al. "Layer Normalization"
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * All input and output tensors must be of the same type.
+ *
+ * Inputs:
+ * * 0: The input (\f$x_t\f$).
+ * A 2-D tensor of shape [batch_size, input_size], where “batch_size”
+ * corresponds to the batching dimension, and “input_size” is the size
+ * of the input.
+ * * 1: The input-to-input weights (\f$W_{xi}\f$). Optional.
+ * A 2-D tensor of shape [num_units, input_size], where “num_units”
+ * corresponds to the number of cell units.
+ * * 2: The input-to-forget weights (\f$W_{xf}\f$).
+ * A 2-D tensor of shape [num_units, input_size].
+ * * 3: The input-to-cell weights (\f$W_{xc}\f$).
+ * A 2-D tensor of shape [num_units, input_size].
+ * * 4: The input-to-output weights (\f$W_{xo}\f$).
+ * A 2-D tensor of shape [num_units, input_size].
+ * * 5: The recurrent-to-input weights (\f$W_{hi}\f$). Optional.
+ * A 2-D tensor of shape [num_units, output_size], where “output_size”
+ * corresponds to either the number of cell units (i.e., “num_units”),
+ * or the second dimension of the “projection_weights”, if defined.
+ * * 6: The recurrent-to-forget weights (\f$W_{hf}\f$).
+ * A 2-D tensor of shape [num_units, output_size].
+ * * 7: The recurrent-to-cell weights (\f$W_{hc}\f$).
+ * A 2-D tensor of shape [num_units, output_size].
+ * * 8: The recurrent-to-output weights (\f$W_{ho}\f$).
+ * A 2-D tensor of shape [num_units, output_size].
+ * * 9: The cell-to-input weights (\f$W_{ci}\f$). Optional.
+ * A 1-D tensor of shape [num_units].
+ * * 10:The cell-to-forget weights (\f$W_{cf}\f$). Optional.
+ * A 1-D tensor of shape [num_units].
+ * * 11:The cell-to-output weights (\f$W_{co}\f$). Optional.
+ * A 1-D tensor of shape [num_units].
+ * * 12:The input gate bias (\f$b_i\f$). Optional.
+ * A 1-D tensor of shape [num_units].
+ * * 13:The forget gate bias (\f$b_f\f$).
+ * A 1-D tensor of shape [num_units].
+ * * 14:The cell bias (\f$b_c\f$).
+ * A 1-D tensor of shape [num_units].
+ * * 15:The output gate bias (\f$b_o\f$).
+ * A 1-D tensor of shape [num_units].
+ * * 16:The projection weights (\f$W_{proj}\f$). Optional.
+ * A 2-D tensor of shape [output_size, num_units].
+ * * 17:The projection bias (\f$b_{proj}\f$). Optional.
+ * A 1-D tensor of shape [output_size].
+ * * 18:The output state (in) (\f$h_{t-1}\f$).
+ * A 2-D tensor of shape [batch_size, output_size].
+ * * 19:The cell state (in) (\f$C_{t-1}\f$).
+ * A 2-D tensor of shape [batch_size, num_units].
+ * * 20:The activation function (\f$g\f$).
+ * A value indicating the activation function:
+ * <ul>
+ * <li>0: None;
+ * <li>1: Relu;
+ * <li>3: Relu6;
+ * <li>4: Tanh;
+ * <li>6: Sigmoid.
+ * </ul>
+ * * 21:The clipping threshold (\f$t_{cell}\f$) for the cell state, such
+ * that values are bound within [-cell_clip, cell_clip]. If set to 0.0
+ * then clipping is disabled.
+ * Until HAL version 1.2 this scalar must be of type {@link
+ * OperandType::FLOAT32}. Since HAL version 1.2, if all the input
+ * tensors have type {@link OperandType::TENSOR_FLOAT32}, this
+ * scalar must be of the type {@link OperandType::FLOAT32},
+ * otherwise if all the input tensors have the type {@link
+ * OperandType::TENSOR_FLOAT16}, this scalar must be of type {@link
+ * OperandType::FLOAT16}.
+ * * 22:The clipping threshold (\f$t_{proj}\f$) for the output from the
+ * projection layer, such that values are bound within
+ * [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled.
+ * Until HAL version 1.2 this scalar must be of type {@link
+ * OperandType::FLOAT32}. Since HAL version 1.2, if all the input
+ * tensors have type {@link OperandType::TENSOR_FLOAT32}, this
+ * scalar must be of the type {@link OperandType::FLOAT32},
+ * otherwise if all the input tensors have the type {@link
+ * OperandType::TENSOR_FLOAT16}, this scalar must be of type {@link
+ * OperandType::FLOAT16}.
+ * Since HAL version 1.2 there are additional inputs to this op:
+ * * 23:The input layer normalization weights.
+ * A 1-D tensor of shape [num_units]. Used to rescale normalized inputs
+ * to activation at input gate.
+ * * 24:The forget layer normalization weights.
+ * A 1-D tensor of shape [num_units]. Used to rescale normalized inputs
+ * to activation at forget gate.
+ * * 25:The cell layer normalization weights.
+ * A 1-D tensor of shape [num_units]. Used to rescale normalized inputs
+ * to activation at cell gate.
+ * * 26:The output layer normalization weights.
+ * A 1-D tensor of shape [num_units]. Used to rescale normalized inputs
+ * to activation at output gate.
+ *
+ * Outputs:
+ * * 0: The scratch buffer.
+ * A 2-D tensor of shape [batch_size, num_units * 3] with CIFG, or
+ * [batch_size, num_units * 4] without CIFG.
+ * * 1: The output state (out) (\f$h_t\f$).
+ * A 2-D tensor of shape [batch_size, output_size].
+ * * 2: The cell state (out) (\f$C_t\f$).
+ * A 2-D tensor of shape [batch_size, num_units].
+ * * 3: The output (\f$o_t\f$).
+ * A 2-D tensor of shape [batch_size, output_size]. This is effectively
+ * the same as the current “output state (out)” value.
+ */
+ LSTM = 16,
+ /**
+ * Performs an 2-D max pooling operation.
+ *
+ * The output dimensions are functions of the filter dimensions, stride, and
+ * padding.
+ *
+ * The values in the output tensor are computed as:
+ *
+ * output[b, i, j, channel] =
+ * max_{di, dj} (
+ * input[b, strides[1] * i + di, strides[2] * j + dj, channel]
+ * )
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
+ * With the default data layout NHWC, the data is stored in the order of:
+ * [batch, height, width, channels]. Alternatively, the data layout could
+ * be NCHW, the data storage order of: [batch, channels, height, width].
+ * NCHW is supported since HAL version 1.2.
+ *
+ * Both explicit padding and implicit padding are supported.
+ *
+ * Inputs (explicit padding):
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
+ * the input.
+ * Since HAL version 1.2, zero batches is supported for this tensor.
+ * * 1: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the left, in the ‘width’ dimension.
+ * * 2: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the right, in the ‘width’ dimension.
+ * * 3: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the top, in the ‘height’ dimension.
+ * * 4: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the bottom, in the ‘height’ dimension.
+ * * 5: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘width’ dimension.
+ * * 6: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘height’ dimension.
+ * * 7: An {@link OperandType::INT32} scalar, specifying the filter
+ * width.
+ * * 8: An {@link OperandType::INT32} scalar, specifying the filter
+ * height.
+ * * 9: An {@link OperandType::INT32} scalar, and has to be one of the
+ * {@link FusedActivationFunc} values. Specifies the activation to
+ * invoke on the result.
+ * * 10: An optional {@link OperandType::BOOL} scalar, default to false.
+ * Set to true to specify NCHW data layout for input0 and output0.
+ * Available since HAL version 1.2.
+ *
+ * Inputs (implicit padding):
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
+ * the input.
+ * Since HAL version 1.2, zero batches is supported for this tensor.
+ * * 1: An {@link OperandType::INT32} scalar, specifying the implicit
+ * padding scheme, has to be one of the
+ * following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
+ * * 2: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘width’ dimension.
+ * * 3: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘height’ dimension.
+ * * 4: An {@link OperandType::INT32} scalar, specifying the filter
+ * width.
+ * * 5: An {@link OperandType::INT32} scalar, specifying the filter
+ * height.
+ * * 6: An {@link OperandType::INT32} scalar, and has to be one of the
+ * {@link FusedActivationFunc} values. Specifies the activation to
+ * invoke on the result.
+ * * 7: An optional {@link OperandType::BOOL} scalar, default to false.
+ * Set to true to specify NCHW data layout for input0 and output0.
+ * Available since HAL version 1.2.
+ *
+ * Outputs:
+ * * 0: The output 4-D tensor, of shape
+ * [batches, out_height, out_width, depth].
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint must be the same as input0.
+ */
+ MAX_POOL_2D = 17,
+ /**
+ * Multiplies two tensors, element-wise.
+ *
+ * Takes two input tensors of identical {@link OperandType} and compatible
+ * dimensions. The output is the product of both input tensors, optionally
+ * modified by an activation function.
+ *
+ * Two dimensions are compatible when:
+ * 1. they are equal, or
+ * 2. one of them is 1
+ *
+ * The size of the resulting output is the maximum size along each dimension
+ * of the input operands. It starts with the trailing dimensions, and works
+ * its way forward.
+ *
+ * Since HAL version 1.2, generic zero-sized input tensor is supported. Zero
+ * dimension is only compatible with 0 or 1. The size of the output
+ * dimension is zero if either of corresponding input dimension is zero.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ * * {@link OperandType::TENSOR_INT32} (since HAL version 1.3)
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: A tensor.
+ * * 1: A tensor of the same {@link OperandType}, and compatible dimensions
+ * as input0.
+ * * 2: An {@link OperandType::INT32} scalar, and has to be one of the
+ * {@link FusedActivationFunc} values. Specifies the activation to
+ * invoke on the result.
+ * For a {@link OperandType::TENSOR_INT32} tensor,
+ * the {@link FusedActivationFunc} must be "NONE".
+ *
+ * Outputs:
+ * * 0: The product, a tensor of the same {@link OperandType} as input0.
+ * For output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * and {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED},
+ * the following condition must be satisfied:
+ * output_scale > input1_scale * input2_scale.
+ */
+ MUL = 18,
+ /**
+ * Computes rectified linear activation on the input tensor element-wise.
+ *
+ * The output is calculated using this formula:
+ *
+ * output = max(0, input)
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: up to 4.
+ *
+ * Inputs:
+ * * 0: A tensor, specifying the input.
+ * Since HAL version 1.2, this tensor may be zero-sized.
+ *
+ * Outputs:
+ * * 0: The output tensor of same shape as input0.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint must be the same as input0.
+ */
+ RELU = 19,
+ /**
+ * Computes rectified linear 1 activation on the input tensor element-wise.
+ *
+ * The output is calculated using this formula:
+ *
+ * output = min(1.f, max(-1.f, input))
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: up to 4.
+ *
+ * Inputs:
+ * * 0: A tensor, specifying the input.
+ * Since HAL version 1.2, this tensor may be zero-sized.
+ *
+ * Outputs:
+ * * 0: The output tensor of the same shape as input0.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint must be the same as input0.
+ */
+ RELU1 = 20,
+ /**
+ * Computes rectified linear 6 activation on the input tensor element-wise.
+ *
+ * The output is calculated using this formula:
+ *
+ * output = min(6, max(0, input))
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: up to 4.
+ *
+ * Inputs:
+ * * 0: A tensor, specifying the input.
+ * Since HAL version 1.2, this tensor may be zero-sized.
+ *
+ * Outputs:
+ * * 0: The output tensor of same shape as input0.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint must be the same as input0.
+ */
+ RELU6 = 21,
+ /**
+ * Reshapes a tensor.
+ *
+ * Given tensor, this operation returns a tensor that has the same values as
+ * tensor, but with a newly specified shape.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: up to 4.
+ *
+ * Inputs:
+ * * 0: A tensor, specifying the tensor to be reshaped.
+ * * 1: A 1-D tensor of {@link OperandType::TENSOR_INT32}, defining the
+ * shape of the output tensor. The number of elements implied by shape
+ * must be the same as the number of elements in the input tensor.
+ *
+ * If one component of shape is the special value -1, the size of that
+ * dimension is computed so that the total size remains constant. In
+ * particular, a shape of [-1] flattens into 1-D. At most one component
+ * of shape can be -1.
+ *
+ * Outputs:
+ * * 0: The output tensor, of shape specified by the input shape.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint must be the same as input0.
+ */
+ RESHAPE = 22,
+ /**
+ * Resizes images to given size using the bilinear interpretation.
+ *
+ * Resized images must be distorted if their output aspect ratio is not the
+ * same as input aspect ratio. The corner pixels of output may not be the
+ * same as corner pixels of input.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
+ * With the default data layout NHWC, the data is stored in the order of:
+ * [batch, height, width, channels]. Alternatively, the data layout could
+ * be NCHW, the data storage order of: [batch, channels, height, width].
+ * NCHW is supported since HAL version 1.2.
+ *
+ * Both resizing by shape and resizing by scale are supported.
+ *
+ * Inputs (resizing by shape):
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
+ * the input.
+ * Since HAL version 1.2, zero batches is supported for this tensor.
+ * * 1: An {@link OperandType::INT32} scalar, specifying the output
+ * width of the output tensor.
+ * * 2: An {@link OperandType::INT32} scalar, specifying the output
+ * height of the output tensor.
+ * * 3: An optional {@link OperandType::BOOL} scalar, default to false.
+ * Set to true to specify NCHW data layout for input0 and output0.
+ * Available since HAL version 1.2.
+ * * 4: Align corners. An optional {@link OperandType::BOOL}
+ * scalar, default to false. If True, the centers of the 4 corner
+ * pixels of the input and output tensors are aligned, preserving the
+ * values at the corner pixels.
+ * Available since HAL version 1.3.
+ * * 5: Half pixel centers. An optional {@link OperandType::BOOL}
+ * scalar, default to false. If True, the pixel centers are assumed to
+ * be at (0.5, 0.5). This is the default behavior of image.resize in
+ * TF 2.0. If this parameter is True, then align_corners parameter
+ * must be False.
+ * Available since HAL version 1.3.
+ *
+ * Inputs (resizing by scale, since HAL version 1.2):
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
+ * the input. Zero batches is supported for this tensor.
+ * * 1: A scalar, specifying width_scale, the scaling factor of the width
+ * dimension from the input tensor to the output tensor. The output
+ * width is calculated as new_width = floor(width * width_scale).
+ * The scalar must be of {@link OperandType::FLOAT16} if input0 is
+ * of {@link OperandType::TENSOR_FLOAT16} and of
+ * {@link OperandType::FLOAT32} otherwise.
+ * * 2: A scalar, specifying height_scale, the scaling factor of the height
+ * dimension from the input tensor to the output tensor. The output
+ * height is calculated as new_height = floor(height * height_scale).
+ * The scalar must be of {@link OperandType::FLOAT16} if input0 is
+ * of {@link OperandType::TENSOR_FLOAT16} and of
+ * {@link OperandType::FLOAT32} otherwise.
+ * * 3: An optional {@link OperandType::BOOL} scalar, default to false.
+ * Set to true to specify NCHW data layout for input0 and output0.
+ * * 4: Align corners. An optional {@link OperandType::BOOL}
+ * scalar, default to false. If True, the centers of the 4 corner
+ * pixels of the input and output tensors are aligned, preserving the
+ * values at the corner pixels.
+ * Available since HAL version 1.3.
+ * * 5: Half pixel centers. An optional {@link OperandType::BOOL}
+ * scalar, default to false. If True, the pixel centers are assumed to
+ * be at (0.5, 0.5). This is the default behavior of image.resize in
+ * TF 2.0. If this parameter is True, then align_corners parameter
+ * must be False.
+ * Available since HAL version 1.3.
+ *
+ * Outputs:
+ * * 0: The output 4-D tensor, of shape
+ * [batches, new_height, new_width, depth].
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint must be the same as input0.
+ */
+ RESIZE_BILINEAR = 23,
+ /**
+ * A basic recurrent neural network layer.
+ *
+ * This layer implements the operation:
+ * outputs = state = activation(inputs * input_weights +
+ * state * recurrent_weights + bias)
+ *
+ * Where:
+ * * “input_weights” is a weight matrix that multiplies the inputs;
+ * * “recurrent_weights” is a weight matrix that multiplies the current
+ * “state” which itself is the output from the previous time step
+ * computation;
+ * * “bias” is a bias vector (added to each output vector in the batch);
+ * * “activation” is the function passed as the “fused_activation_function”
+ * argument (if not “NONE”).
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * The input tensors must all be the same type.
+ *
+ * Inputs:
+ * * 0: input.
+ * A 2-D tensor of shape [batch_size, input_size], where “batch_size”
+ * corresponds to the batching dimension, and “input_size” is the size
+ * of the input.
+ * * 1: weights.
+ * A 2-D tensor of shape [num_units, input_size], where “num_units”
+ * corresponds to the number of units.
+ * * 2: recurrent_weights.
+ * A 2-D tensor of shape [num_units, num_units], with columns
+ * corresponding to the weights from each unit.
+ * * 3: bias.
+ * A 1-D tensor of shape [num_units].
+ * * 4: hidden state (in).
+ * A 2-D tensor of shape [batch_size, num_units].
+ * * 5: fused_activation_function.
+ * An optional {@link FusedActivationFunc} value indicating the
+ * activation function. If “NONE” is specified then it results in a
+ * linear activation.
+ *
+ * Outputs:
+ * * 0: hidden state (out).
+ * A 2-D tensor of shape [batch_size, num_units].
+ *
+ * * 1: output.
+ * A 2-D tensor of shape [batch_size, num_units]. This is effectively
+ * the same as the current state value.
+ */
+ RNN = 24,
+ /**
+ * Computes the softmax activation on the input tensor element-wise, per
+ * batch, by normalizing the input vector so the maximum coefficient is
+ * zero.
+ *
+ * The output is calculated using this formula:
+ *
+ * output[batch, i] =
+ * exp((input[batch, i] - max(input[batch, :])) * beta) /
+ * sum_{k}{exp((input[batch, k] - max(input[batch, :])) * beta)}
+ *
+ * For input tensor with rank other than 2, the activation will be applied
+ * independently on each 1-D slice along specified dimension.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: up to 4.
+ * Tensors with rank other than 2 or 4 are only supported since HAL version 1.2.
+ *
+ * Inputs:
+ * * 0: A 2-D or 4-D tensor, specifying the tensor to be reshaped.
+ * Since HAL version 1.2, this tensor may be zero-sized.
+ * * 1: A scalar, specifying the positive scaling factor for the exponent,
+ * beta. If input0 is of {@link OperandType::TENSOR_FLOAT32},
+ * {@link OperandType::TENSOR_QUANT8_ASYMM} or
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED}, the scalar
+ * must be of {@link OperandType::FLOAT32}.
+ * If input0 is of {@link OperandType::TENSOR_FLOAT16}, then the
+ * scalar must be of {@link OperandType::FLOAT16}.
+ * * 2: An optional {@link OperandType::INT32} scalar, default to -1,
+ * specifying the dimension the activation would be performed on.
+ * Negative index is used to specify axis from the end (e.g. -1 for
+ * the last axis). Must be in the range [-n, n).
+ * Available since HAL version 1.2.
+ *
+ * Outputs:
+ * * 0: The output tensor of same shape as input0.
+ * For {@link OperandType::TENSOR_QUANT8_ASYMM},
+ * the scale must be 1.f / 256 and the zeroPoint must be 0.
+ * For {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED},
+ * the scale must be 1.f / 256 and the zeroPoint must be -128.
+ */
+ SOFTMAX = 25,
+ /**
+ * Rearranges blocks of spatial data, into depth.
+ *
+ * More specifically, this op outputs a copy of the input tensor where
+ * values from the height and width dimensions are moved to the depth
+ * dimension. The value block_size indicates the input block size and how
+ * the data is moved.
+ *
+ * Chunks of data of size block_size * block_size from depth are rearranged
+ * into non-overlapping blocks of size block_size x block_size.
+ *
+ * The depth of the output tensor is input_depth * block_size * block_size.
+ * The input tensor's height and width must be divisible by block_size.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
+ * With the default data layout NHWC, the data is stored in the order of:
+ * [batch, height, width, channels]. Alternatively, the data layout could
+ * be NCHW, the data storage order of: [batch, channels, height, width].
+ * NCHW is supported since HAL version 1.2.
+ *
+ * Inputs:
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
+ * specifying the input.
+ * * 1: An {@link OperandType::INT32} scalar, specifying the block_size.
+ * block_size must be >=1 and block_size must be a divisor of both the
+ * input height and width.
+ * * 2: An optional {@link OperandType::BOOL} scalar, default to false.
+ * Set to true to specify NCHW data layout for input0 and output0.
+ * Available since HAL version 1.2.
+ *
+ * Outputs:
+ * * 0: The output 4-D tensor, of shape [batches, height/block_size,
+ * width/block_size, depth_in*block_size*block_size].
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint must be the same as input0.
+ */
+ SPACE_TO_DEPTH = 26,
+ /**
+ * SVDF op is a kind of stateful layer derived from the notion that a
+ * densely connected layer that's processing a sequence of input frames can
+ * be approximated by using a singular value decomposition of each of its
+ * nodes. The implementation is based on:
+ *
+ * https://research.google.com/pubs/archive/43813.pdf
+ *
+ * P. Nakkiran, R. Alvarez, R. Prabhavalkar, C. Parada.
+ * “Compressing Deep Neural Networks using a Rank-Constrained Topology”.
+ * INTERSPEECH, 2015.
+ *
+ * It processes the incoming input using a 2-stage filtering mechanism:
+ * * stage 1 performs filtering on the "features" dimension, whose outputs
+ * get pushed into a memory of fixed-size memory_size.
+ * * stage 2 performs filtering on the "time" dimension of the memory_size
+ * memoized outputs of stage 1.
+ *
+ * Specifically, for rank 1, this layer implements the operation:
+ *
+ * memory = push(conv1d(inputs, weights_feature, feature_dim,
+ * "PADDING_VALID"));
+ * outputs = activation(memory * weights_time + bias);
+ *
+ * Where:
+ * * “weights_feature” is a weights matrix that processes the inputs (by
+ * convolving the input with every “feature filter”), and whose outputs
+ * get pushed, stacked in order, into the fixed-size “memory” (the oldest
+ * entry gets dropped);
+ * * “weights_time” is a weights matrix that processes the “memory” (by a
+ * batched matrix multiplication on the num_units);
+ * * “bias” is an optional bias vector (added to each output vector in the
+ * batch); and
+ * * “activation” is the function passed as the “fused_activation_function”
+ * argument (if not “NONE”).
+ *
+ * Each rank adds a dimension to the weights matrices by means of stacking
+ * the filters.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * All input tensors must be the same type.
+ *
+ * Inputs:
+ * * 0: input.
+ * A 2-D tensor of shape [batch_size, input_size], where “batch_size”
+ * corresponds to the batching dimension, and “input_size” is the size
+ * of the input.
+ * * 1: weights_feature.
+ * A 2-D tensor of shape [num_units, input_size], where “num_units”
+ * corresponds to the number of units.
+ * * 2: weights_time.
+ * A 2-D tensor of shape [num_units, memory_size], where “memory_size”
+ * corresponds to the fixed-size of the memory.
+ * * 3: bias.
+ * An optional 1-D tensor of shape [num_units].
+ * * 4: state (in).
+ * A 2-D tensor of shape [batch_size, (memory_size - 1) * num_units * rank].
+ * * 5: rank.
+ * The rank of the SVD approximation.
+ * * 6: fused_activation_function.
+ * An optional {@link FusedActivationFunc} value indicating the
+ * activation function. If “NONE” is specified then it results in a
+ * linear activation.
+ *
+ * Outputs:
+ * * 0: state (out).
+ * A 2-D tensor of the same {@link OperandType} as the inputs, with shape
+ * [batch_size, (memory_size - 1) * num_units * rank].
+ * * 1: output.
+ * A 2-D tensor of the same {@link OperandType} as the inputs, with shape
+ * [batch_size, num_units].
+ */
+ SVDF = 27,
+ /**
+ * Computes hyperbolic tangent of input tensor element-wise.
+ *
+ * The output is calculated using this formula:
+ *
+ * output = tanh(input)
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: up to 4.
+ *
+ * Inputs:
+ * * 0: A tensor, specifying the input.
+ * Since HAL version 1.2, this tensor may be zero-sized.
+ *
+ * Outputs:
+ * * 0: The output tensor of same shape as input0.
+ * For {@link OperandType::TENSOR_QUANT8_ASYMM},
+ * the scale must be 1.f / 128 and the zeroPoint must be 128.
+ * For {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED},
+ * the scale must be 1.f / 128 and the zeroPoint must be 0.
+ */
+ TANH = 28,
+ /**
+ * BatchToSpace for N-dimensional tensors.
+ *
+ * This operation reshapes the batch dimension (dimension 0) into M + 1
+ * dimensions of shape block_shape + [batch], interleaves these blocks back
+ * into the grid defined by the spatial dimensions [1, ..., M], to obtain a
+ * result with the same rank as the input.
+ *
+ * This is the reverse of SpaceToBatch.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
+ * With the default data layout NHWC, the data is stored in the order of:
+ * [batch, height, width, channels]. Alternatively, the data layout could
+ * be NCHW, the data storage order of: [batch, channels, height, width].
+ * NCHW is supported since HAL version 1.2.
+ *
+ * Inputs:
+ * * 0: An n-D tensor, specifying the tensor to be reshaped
+ * * 1: A 1-D Tensor of {@link OperandType::TENSOR_INT32}, the block
+ * sizes for each spatial dimension of the input tensor. All values
+ * must be >= 1.
+ * * 2: An optional {@link OperandType::BOOL} scalar, default to false.
+ * Set to true to specify NCHW data layout for input0 and output0.
+ * Available since API level 29.
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} as input0.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint must be the same as input0.
+ */
+ BATCH_TO_SPACE_ND = 29,
+ /**
+ * Element-wise division of two tensors.
+ *
+ * Takes two input tensors of identical {@link OperandType} and compatible
+ * dimensions. The output is the result of dividing the first input tensor
+ * by the second, optionally modified by an activation function.
+ *
+ * For inputs of {@link OperandType::TENSOR_INT32}, performs
+ * "floor division" ("//" in Python). For example,
+ * 5 // 2 = 2
+ * -5 // 2 = -3
+ *
+ * Two dimensions are compatible when:
+ * 1. they are equal, or
+ * 2. one of them is 1
+ *
+ * The size of the output is the maximum size along each dimension of the
+ * input operands. It starts with the trailing dimensions, and works its way
+ * forward.
+ *
+ * Example:
+ * input1.dimension = {4, 1, 2}
+ * input2.dimension = {5, 4, 3, 1}
+ * output.dimension = {5, 4, 3, 2}
+ *
+ * Since HAL version 1.2, generic zero-sized input tensor is supported. Zero
+ * dimension is only compatible with 0 or 1. The size of the output
+ * dimension is zero if either of corresponding input dimension is zero.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32} (since HAL version 1.3)
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: An n-D tensor, specifying the first input.
+ * * 1: A tensor of the same {@link OperandType}, and compatible dimensions
+ * as input0.
+ * * 2: An {@link OperandType::INT32} scalar, and has to be one of the
+ * {@link FusedActivationFunc} values. Specifies the activation to
+ * invoke on the result.
+ * For a {@link OperandType::TENSOR_INT32} tensor,
+ * the {@link FusedActivationFunc} must be "NONE".
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} as input0.
+ */
+ DIV = 30,
+ /**
+ * Computes the mean of elements across dimensions of a tensor.
+ *
+ * Reduces the input tensor along the given dimensions to reduce. Unless
+ * keep_dims is true, the rank of the tensor is reduced by 1 for each entry
+ * in axis. If keep_dims is true, the reduced dimensions are retained with
+ * length 1.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: A tensor, specifying the input.
+ * * 1: A 1-D Tensor of {@link OperandType::TENSOR_INT32}. The dimensions
+ * to reduce. Must be in the range
+ * [-rank(input_tensor), rank(input_tensor)).
+ *
+ * NOTE: When the operation was introduced, the documentation
+ * incorrectly stated that if dimensions were empty, the operation
+ * would reduce across all dimensions. This behavior was never
+ * implemented.
+ *
+ * * 2: An {@link OperandType::INT32} scalar, keep_dims. If positive,
+ * retains reduced dimensions with length 1.
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} as input0.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint must be the same as input0.
+ * If all dimensions are reduced and keep_dims is false, the output
+ * shape is [1].
+ */
+ MEAN = 31,
+ /**
+ * Pads a tensor.
+ *
+ * This operation pads a tensor according to the specified paddings.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ * (full support since HAL version 1.2, see the output section)
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: An n-D tensor, specifying the tensor to be padded.
+ * * 1: A 2-D Tensor of {@link OperandType::TENSOR_INT32}, the paddings
+ * for each spatial dimension of the input tensor. The shape of the
+ * tensor must be {rank(input0), 2}.
+ * padding[i, 0] specifies the number of elements to be padded in the
+ * front of dimension i.
+ * padding[i, 1] specifies the number of elements to be padded after the
+ * end of dimension i.
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} as input0. The
+ * output tensor has the same rank as input0, and each
+ * dimension of the output tensor has the same size as the
+ * corresponding dimension of the input tensor plus the size
+ * of the padding:
+ * output0.dimension[i] =
+ * padding[i, 0] + input0.dimension[i] + padding[i, 1]
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint must be the same as input0.
+ *
+ * NOTE: Before HAL version 1.2, the pad value for
+ * {@link OperandType::TENSOR_QUANT8_ASYMM} is undefined.
+ * Since HAL version 1.2, the pad value is always the logical zero.
+ */
+ PAD = 32,
+ /**
+ * SpaceToBatch for N-Dimensional tensors.
+ *
+ * This operation divides "spatial" dimensions [1, ..., M] of the input into
+ * a grid of blocks of shape block_shape, and interleaves these blocks with
+ * the "batch" dimension (0) such that in the output, the spatial dimensions
+ * [1, ..., M] correspond to the position within the grid, and the batch
+ * dimension combines both the position within a spatial block and the
+ * original batch position. Prior to division into blocks, the spatial
+ * dimensions of the input are optionally zero padded according to paddings.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ * (full support since HAL version 1.2, see the output section)
+ *
+ * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
+ * With the default data layout NHWC, the data is stored in the order of:
+ * [batch, height, width, channels]. Alternatively, the data layout could
+ * be NCHW, the data storage order of: [batch, channels, height, width].
+ * NCHW is supported since HAL version 1.2.
+ *
+ * Inputs:
+ * * 0: An n-D tensor, specifying the input.
+ * * 1: A 1-D Tensor of {@link OperandType::TENSOR_INT32}, the block
+ * sizes for each spatial dimension of the input tensor. All values
+ * must be >= 1.
+ * * 2: A 2-D Tensor of {@link OperandType::TENSOR_INT32}, the paddings
+ * for each spatial dimension of the input tensor. All values must be
+ * >= 0. The shape of the tensor must be {M, 2}, where M is the number
+ * of spatial dimensions.
+ * padding[i, 0] specifies the number of element to be padded in the
+ * front of dimension i.
+ * padding[i, 1] specifies the number of element to be padded after the
+ * end of dimension i.
+ * * 3: An optional {@link OperandType::BOOL} scalar, default to false.
+ * Set to true to specify NCHW data layout for input0 and output0.
+ * Available since HAL version 1.2.
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} as input0.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint must be the same as input0.
+ *
+ * NOTE: Before HAL version 1.2, the pad value for
+ * {@link OperandType::TENSOR_QUANT8_ASYMM} is undefined.
+ * Since HAL version 1.2, the pad value is always the logical zero.
+ */
+ SPACE_TO_BATCH_ND = 33,
+ /**
+ * Removes dimensions of size 1 from the shape of a tensor.
+ *
+ * Given a tensor input, this operation returns a tensor of the same
+ * {@link OperandType} with all dimensions of size 1 removed. If you don't
+ * want to remove all size 1 dimensions, you can remove specific size 1
+ * dimensions by specifying the axes (input1).
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: An n-D tensor, the tensor to be squeezed.
+ * * 1: An optional 1-D tensor of {@link OperandType::TENSOR_INT32}. The
+ * dimensions to squeeze. If specified only squeezes the dimensions
+ * listed. Otherwise, squeezes all dimensions. The dimension index
+ * starts at 0. An error must be reported if squeezing a dimension that
+ * is not 1.
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} as input0. Contains the
+ * same data as input, but has one or more dimensions of size 1
+ * removed.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint must be the same as input0.
+ * If all input dimensions are equal to 1 and are to be squeezed, the
+ * output shape is [1].
+ */
+ SQUEEZE = 34,
+ /**
+ * Extracts a strided slice of a tensor.
+ *
+ * Roughly speaking, this op extracts a slice of size (end - begin) / stride
+ * from the given input tensor. Starting at the location specified by begin
+ * the slice continues by adding stride to the index until all dimensions
+ * are not less than end. Note that a stride can be negative, which causes a
+ * reverse slice.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: An n-D tensor, specifying the tensor to be sliced.
+ * * 1: begin, a 1-D tensor of {@link OperandType::TENSOR_INT32}. The
+ * starts of the dimensions of the input tensor to be sliced. The
+ * length must be of rank(input0).
+ * * 2: end, a 1-D tensor of {@link OperandType::TENSOR_INT32}. The
+ * ends of the dimensions of the input tensor to be sliced. The length
+ * must be of rank(input0).
+ * * 3: strides, a 1-D tensor of {@link OperandType::TENSOR_INT32}. The
+ * strides of the dimensions of the input tensor to be sliced. The
+ * length must be of rank(input0). The entries must be non-zero.
+ * * 4: begin_mask, an {@link OperandType::INT32} scalar. If the ith bit
+ * of begin_mask is set, begin[i] is ignored and the fullest possible
+ * range in that dimension is used instead.
+ * * 5: end_mask, an {@link OperandType::INT32} scalar. If the ith bit of
+ * end_mask is set, end[i] is ignored and the fullest possible range in
+ * that dimension is used instead.
+ * * 6: shrink_axis_mask, an {@link OperandType::INT32} scalar. If the
+ * ith bit of shrink_axis_mask is set, the ith dimension specification
+ * shrinks the dimensionality by 1, taking on the value at index
+ * begin[i]. In this case, the ith specification must define a
+ * slice of size 1, e.g. begin[i] = x, end[i] = x + 1.
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} as input0 and rank (n - k),
+ * where k is the number of bits set in shrink_axis_mask.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint must be the same as input0.
+ * If shrink_axis_mask is true for all input dimensions, the output
+ * shape is [1].
+ */
+ STRIDED_SLICE = 35,
+ /**
+ * Element-wise subtraction of two tensors.
+ *
+ * Takes two input tensors of identical {@link OperandType} and compatible
+ * dimensions. The output is the result of subtracting the second input
+ * tensor from the first one, optionally modified by an activation function.
+ *
+ * Two dimensions are compatible when:
+ * 1. they are equal, or
+ * 2. one of them is 1
+ *
+ * The size of the output is the maximum size along each dimension of the
+ * input operands. It starts with the trailing dimensions, and works its way
+ * forward.
+ *
+ * Example:
+ * input1.dimension = {4, 1, 2}
+ * input2.dimension = {5, 4, 3, 1}
+ * output.dimension = {5, 4, 3, 2}
+ *
+ * Since HAL version 1.2, generic zero-sized input tensor is supported. Zero
+ * dimension is only compatible with 0 or 1. The size of the output
+ * dimension is zero if either of corresponding input dimension is zero.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ * * {@link OperandType::TENSOR_INT32} (since HAL version 1.3)
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: An n-D tensor, specifying the first input.
+ * * 1: A tensor of the same {@link OperandType}, and compatible dimensions
+ * as input0.
+ * * 2: An {@link OperandType::INT32} scalar, and has to be one of the
+ * {@link FusedActivationFunc} values. Specifies the activation to
+ * invoke on the result.
+ * For a {@link OperandType::TENSOR_INT32} tensor,
+ * the {@link FusedActivationFunc} must be "NONE".
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} as input0.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint can be different from inputs' scale and zeroPoint.
+ */
+ SUB = 36,
+ /**
+ * Transposes the input tensor, permuting the dimensions according to the
+ * perm tensor.
+ *
+ * The returned tensor's dimension i corresponds to the input dimension
+ * perm[i]. If perm is not given, it is set to (n-1...0), where n is the
+ * rank of the input tensor. Hence by default, this operation performs a
+ * regular matrix transpose on 2-D input Tensors.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: An n-D tensor, specifying the tensor to be transposed.
+ * Since HAL version 1.2, this tensor may be zero-sized.
+ * * 1: An optional 1-D Tensor of {@link OperandType::TENSOR_INT32},
+ * the permutation of the dimensions of the input tensor.
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} as input0.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint must be the same as input0.
+ */
+ TRANSPOSE = 37,
+ /**
+ * Computes the absolute value of a tensor, element-wise.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32} (since HAL version 1.3)
+ *
+ * Supported tensor rank: from 1.
+ *
+ * Inputs:
+ * * 0: A tensor.
+ *
+ * Outputs:
+ * * 0: The output tensor of same shape as input0.
+ */
+ ABS = 38,
+ /**
+ * Returns the index of the largest element along an axis.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: from 1
+ *
+ * Inputs:
+ * * 0: An n-D tensor specifying the input. Must be non-empty.
+ * * 1: An {@link OperandType::INT32} scalar specifying the axis to
+ * reduce across. Negative index is used to specify axis from the
+ * end (e.g. -1 for the last axis). Must be in the range [-n, n).
+ *
+ * Outputs:
+ * * 0: An (n - 1)-D {@link OperandType::TENSOR_INT32} tensor.
+ * If input is 1-dimensional, the output shape is [1].
+ */
+ ARGMAX = 39,
+ /**
+ * Returns the index of the smallest element along an axis.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: from 1
+ *
+ * Inputs:
+ * * 0: An n-D tensor specifying the input. Must be non-empty.
+ * * 1: An {@link OperandType::INT32} scalar specifying the axis to
+ * reduce across. Negative index is used to specify axis from the
+ * end (e.g. -1 for the last axis). Must be in the range [-n, n).
+ *
+ * Outputs:
+ * * 0: An (n - 1)-D {@link OperandType::TENSOR_INT32} tensor.
+ * If input is 1-dimensional, the output shape is [1].
+ */
+ ARGMIN = 40,
+ /**
+ * Transform axis-aligned bounding box proposals using bounding box deltas.
+ *
+ * Given the positions of bounding box proposals and the corresponding
+ * bounding box deltas for each class, return the refined bounding box
+ * regions. The resulting bounding boxes are cliped against the edges of
+ * the image.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT16_ASYMM}
+ *
+ * Inputs:
+ * * 0: A 2-D Tensor of shape [num_rois, 4], specifying the locations of the
+ * bounding box proposals, each line with format [x1, y1, x2, y2].
+ * For tensor of type {@link OperandType::TENSOR_QUANT16_ASYMM},
+ * the zeroPoint must be 0 and the scale must be 0.125. Zero num_rois
+ * is supported for this tensor.
+ * * 1: A 2-D Tensor of shape [num_rois, num_classes * 4], specifying the
+ * bounding box delta for each region of interest and each class. The
+ * bounding box deltas are organized in the following order
+ * [dx, dy, dw, dh], where dx and dy is the relative correction factor
+ * for the center position of the bounding box with respect to the width
+ * and height, dw and dh is the log-scale relative correction factor
+ * for the width and height. For input0 of type
+ * {@link OperandType::TENSOR_QUANT16_ASYMM}, this tensor should be
+ * of {@link OperandType::TENSOR_QUANT8_ASYMM} or
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED}. Zero num_rois is
+ * supported for this tensor.
+ * * 2: An 1-D {@link OperandType::TENSOR_INT32} tensor, of shape
+ * [num_rois], specifying the batch index of each box. Boxes with
+ * the same batch index are grouped together. Zero num_rois is
+ * supported for this tensor.
+ * * 3: A 2-D Tensor of shape [batches, 2], specifying the information of
+ * each image in the batch, each line with format
+ * [image_height, image_width].
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} as input0, with shape
+ * [num_rois, num_classes * 4], specifying the coordinates of each
+ * output bounding box for each class, with format [x1, y1, x2, y2].
+ * For type of {@link OperandType::TENSOR_QUANT16_ASYMM}, the
+ * scale must be 0.125 and the zero point must be 0.
+ */
+ AXIS_ALIGNED_BBOX_TRANSFORM = 41,
+ /**
+ * A recurrent neural network layer that applies an LSTM cell to a
+ * sequence of inputs in forward and backward directions.
+ *
+ * The op supports cross-linking via an auxiliary input. Regular cell feeds
+ * one input into the two RNN cells in the following way:
+ *
+ * INPUT (INPUT_REVERSED)
+ * | |
+ * ---------------------
+ * | FW_LSTM BW_LSTM |
+ * ---------------------
+ * | |
+ * FW_OUT BW_OUT
+ *
+ * An op with cross-linking takes two inputs and feeds them into the RNN
+ * cells in the following way:
+ *
+ * AUX_INPUT (AUX_INPUT_REVERSED)
+ * | |
+ * INPUT | (INPUT_R'D.)|
+ * | | | |
+ * -----------------------
+ * | \ / \ / |
+ * | FW_LSTM BW_LSTM |
+ * -----------------------
+ * | |
+ * FW_OUT BW_OUT
+ *
+ * The cross-linking mode is enabled iff auxiliary input and auxiliary
+ * weights are present. While stacking this op on top of itself, this
+ * allows to connect both forward and backward outputs from previous cell
+ * to the next cell's input.
+ *
+ * Since HAL version 1.3 parallel linking mode is supported. The mode is
+ * enabled if auxiliary input is present but auxiliary weights are omitted.
+ * In this case, the cell feeds inputs into the RNN in the following way:
+ *
+ * INPUT (AUX_INPUT_REVERSED)
+ * | |
+ * ---------------------
+ * | FW_LSTM BW_LSTM |
+ * ---------------------
+ * | |
+ * FW_OUT BW_OUT
+ *
+ * While stacking this op on top of itself, this allows to connect both
+ * forward and backward outputs from previous cell to the next cell's
+ * corresponding inputs.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: 3, either time-major or batch-major.
+ *
+ * All input and output tensors must be of the same type.
+ *
+ * Inputs:
+ * * 0: The input.
+ * A 3-D tensor of shape:
+ * If time-major: [max_time, batch_size, input_size]
+ * If batch-major: [batch_size, max_time, input_size]
+ * where "max_time" is the number of timesteps (sequence length),
+ * "batch_size" corresponds to the batching dimension, and
+ * "input_size" is the size of the input.
+ * * 1: The forward input-to-input weights. Optional.
+ * A 2-D tensor of shape [fw_num_units, input_size], where “fw_num_units”
+ * corresponds to the number of forward cell units.
+ * * 2: The forward input-to-forget weights.
+ * A 2-D tensor of shape [fw_num_units, input_size].
+ * * 3: The forward input-to-cell weights.
+ * A 2-D tensor of shape [fw_num_units, input_size].
+ * * 4: The forward input-to-output weights.
+ * A 2-D tensor of shape [fw_num_units, input_size].
+ * * 5: The forward recurrent-to-input weights. Optional.
+ * A 2-D tensor of shape [fw_num_units, fw_output_size], where “fw_output_size”
+ * corresponds to either the number of cell units (i.e., fw_num_units),
+ * or the second dimension of the “fw_projection_weights”, if defined.
+ * * 6: The forward recurrent-to-forget weights.
+ * A 2-D tensor of shape [fw_num_units, fw_output_size].
+ * * 7: The forward recurrent-to-cell weights.
+ * A 2-D tensor of shape [fw_num_units, fw_output_size].
+ * * 8: The forward recurrent-to-output weights.
+ * A 2-D tensor of shape [fw_num_units, fw_output_size].
+ * * 9: The forward cell-to-input weights. Optional.
+ * A 1-D tensor of shape [fw_num_units].
+ * * 10: The forward cell-to-forget weights. Optional.
+ * A 1-D tensor of shape [fw_num_units].
+ * * 11: The forward cell-to-output weights. Optional.
+ * A 1-D tensor of shape [fw_num_units].
+ * * 12: The forward input gate bias. Optional.
+ * A 1-D tensor of shape [fw_num_units].
+ * * 13: The forward forget gate bias.
+ * A 1-D tensor of shape [fw_num_units].
+ * * 14: The forward cell gate bias.
+ * A 1-D tensor of shape [fw_num_units].
+ * * 15: The forward output gate bias.
+ * A 1-D tensor of shape [fw_num_units].
+ * * 16: The forward projection weights. Optional.
+ * A 2-D tensor of shape [fw_output_size, fw_num_units].
+ * * 17: The forward projection bias. Optional.
+ * A 1-D tensor of shape [fw_output_size].
+ * * 18: The backward input-to-input weights. Optional.
+ * A 2-D tensor of shape [bw_num_units, input_size], where “bw_num_units”
+ * corresponds to the number of backward cell units.
+ * * 19: The backward input-to-forget weights.
+ * A 2-D tensor of shape [bw_num_units, input_size].
+ * * 20: The backward input-to-cell weights.
+ * A 2-D tensor of shape [bw_num_units, input_size].
+ * * 21: The backward input-to-output weights.
+ * A 2-D tensor of shape [bw_num_units, input_size].
+ * * 22: The backward recurrent-to-input weights. Optional.
+ * A 2-D tensor of shape [bw_num_units, bw_output_size], where “bw_output_size”
+ * corresponds to either the number of cell units (i.e., “bw_num_units”),
+ * or the second dimension of the “bw_projection_weights”, if defined.
+ * * 23: The backward recurrent-to-forget weights.
+ * A 2-D tensor of shape [bw_num_units, bw_output_size].
+ * * 24: The backward recurrent-to-cell weights.
+ * A 2-D tensor of shape [bw_num_units, bw_output_size].
+ * * 25: The backward recurrent-to-output weights.
+ * A 2-D tensor of shape [bw_num_units, bw_output_size].
+ * * 26: The backward cell-to-input weights. Optional.
+ * A 1-D tensor of shape [bw_num_units].
+ * * 27: The backward cell-to-forget weights. Optional.
+ * A 1-D tensor of shape [bw_num_units].
+ * * 28: The backward cell-to-output weights. Optional.
+ * A 1-D tensor of shape [bw_num_units].
+ * * 29: The backward input gate bias. Optional.
+ * A 1-D tensor of shape [bw_num_units].
+ * * 30: The backward forget gate bias.
+ * A 1-D tensor of shape [bw_num_units].
+ * * 31: The backward cell gate bias.
+ * A 1-D tensor of shape [bw_num_units].
+ * * 32: The backward output gate bias.
+ * A 1-D tensor of shape [bw_num_units].
+ * * 33: The backward projection weights. Optional.
+ * A 2-D tensor of shape [bw_output_size, bw_num_units].
+ * * 34: The backward projection bias. Optional.
+ * A 1-D tensor of shape [bw_output_size].
+ * * 35: The forward input activation state.
+ * A 2-D tensor of shape [batch_size, bw_output_size].
+ * * 36: The forward input cell state.
+ * A 2-D tensor of shape [batch_size, bw_num_units].
+ * * 37: The backward input activation state.
+ * A 2-D tensor of shape [batch_size, bw_output_size].
+ * * 38: The backward input cell state.
+ * A 2-D tensor of shape [batch_size, bw_num_units].
+ * * 39: The auxiliary input. Optional.
+ * A 3-D tensor of shape [max_time, batch_size, aux_input_size],
+ * where “batch_size” corresponds to the batching dimension, and
+ * “aux_input_size” is the size of the auxiliary input. Optional. See
+ * the docs above for the usage modes explanation.
+ * * 40: The forward auxiliary input-to-input weights.
+ * Optional. See the docs above for the usage modes explanation.
+ * A 2-D tensor of shape [fw_num_units, aux_input_size].
+ * * 41: The forward auxiliary input-to-forget weights.
+ * Optional. See the docs above for the usage modes explanation.
+ * A 2-D tensor of shape [fw_num_units, aux_input_size].
+ * * 42: The forward auxiliary input-to-cell weights.
+ * Optional. See the docs above for the usage modes explanation.
+ * A 2-D tensor of shape [fw_num_units, aux_input_size].
+ * * 43: The forward auxiliary input-to-output weights.
+ * Optional. See the docs above for the usage modes explanation.
+ * A 2-D tensor of shape [fw_num_units, aux_input_size].
+ * * 44: The backward auxiliary input-to-input weights.
+ * Optional. See the docs above for the usage modes explanation.
+ * A 2-D tensor of shape [bw_num_units, aux_input_size].
+ * * 45: The backward auxiliary input-to-forget weights.
+ * Optional. See the docs above for the usage modes explanation.
+ * A 2-D tensor of shape [bw_num_units, aux_input_size].
+ * * 46: The backward auxiliary input-to-cell weights.
+ * Optional. See the docs above for the usage modes explanation.
+ * A 2-D tensor of shape [bw_num_units, aux_input_size].
+ * * 47: The backward auxiliary input-to-output weights.
+ * Optional. See the docs above for the usage modes explanation.
+ * A 2-D tensor of shape [bw_num_units, aux_input_size].
+ * * 48: The activation function.
+ * A value indicating the activation function:
+ * <ul>
+ * <li>0: None;
+ * <li>1: Relu;
+ * <li>3: Relu6;
+ * <li>4: Tanh;
+ * <li>6: Sigmoid.
+ * </ul>
+ * * 49: The clipping threshold for the cell state, such
+ * that values are bound within [-cell_clip, cell_clip]. If set to 0.0
+ * then clipping is disabled.
+ * If all the input tensors have type {@link OperandType::TENSOR_FLOAT32},
+ * this scalar must be of the type {@link OperandType::FLOAT32},
+ * otherwise if all the input tensors have the type
+ * {@link OperandType::TENSOR_FLOAT16}, this scalar must be
+ * of type {@link OperandType::FLOAT16}.
+ * * 50: The clipping threshold for the output from the
+ * projection layer, such that values are bound within
+ * [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled.
+ * If all the input tensors have type {@link OperandType::TENSOR_FLOAT32},
+ * this scalar must be of the type {@link OperandType::FLOAT32},
+ * otherwise if all the input tensors have the type
+ * {@link OperandType::TENSOR_FLOAT16}, this scalar must be
+ * of type {@link OperandType::FLOAT16}.
+ * * 51: merge_outputs
+ * An {@link OperandType::BOOL} scalar specifying if the outputs
+ * from forward and backward cells should be merged.
+ * * 52: time_major
+ * An {@link OperandType::BOOL} scalar specifying the shape format
+ * of input and output tensors.
+ * * 53: The forward input layer normalization weights. Optional.
+ * A 1-D tensor of shape [fw_num_units]. Used to rescale normalized inputs
+ * to activation at input gate.
+ * * 54: The forward forget layer normalization weights. Optional.
+ * A 1-D tensor of shape [fw_num_units]. Used to rescale normalized inputs
+ * to activation at forget gate.
+ * * 55: The forward cell layer normalization weights. Optional.
+ * A 1-D tensor of shape [fw_num_units]. Used to rescale normalized inputs
+ * to activation at cell gate.
+ * * 56: The forward output layer normalization weights. Optional.
+ * A 1-D tensor of shape [fw_num_units]. Used to rescale normalized inputs
+ * to activation at output gate.
+ * * 57: The backward input layer normalization weights. Optional.
+ * A 1-D tensor of shape [bw_num_units]. Used to rescale normalized inputs
+ * to activation at input gate.
+ * * 58: The backward forget layer normalization weights. Optional.
+ * A 1-D tensor of shape [bw_num_units]. Used to rescale normalized inputs
+ * to activation at forget gate.
+ * * 59: The backward cell layer normalization weights. Optional.
+ * A 1-D tensor of shape [bw_num_units]. Used to rescale normalized inputs
+ * to activation at cell gate.
+ * * 60: The backward output layer normalization weights. Optional.
+ * A 1-D tensor of shape [bw_num_units]. Used to rescale normalized inputs
+ * to activation at output gate.
+ *
+ * Outputs:
+ * * 0: The forward output.
+ * A 3-D tensor of shape:
+ * If time-major and not merge_outputs:
+ * [max_time, batch_size, fw_output_size]
+ * If time-major and merge_outputs:
+ * [max_time, batch_size, fw_output_size + bw_output_size]
+ * If batch-major and not merge_outputs:
+ * [batch_size, max_time, fw_output_size]
+ * If batch-major and merge_outputs:
+ * [batch_size, max_time, fw_output_size + bw_output_size]
+ * * 1: The backward output. Unused if merge_outputs is true.
+ * A 3-D tensor of shape:
+ * If time-major: [max_time, batch_size, bw_output_size]
+ * If batch-major: [batch_size, max_time, bw_output_size]
+ * * 2: The forward activation state output.
+ * A 2-D tensor of shape [batch_size, fw_output_size] containing an
+ * activation state from the last time step in the sequence. This
+ * output is optional and can be omitted. If this output is present
+ * then outputs 3-5 must be present as well.
+ * Available since HAL version 1.3.
+ * * 3: The forward cell state output.
+ * A tensor of shape [batch_size, fw_cell_size] containing a cell state
+ * from the last time step in the sequence. This output is optional
+ * and can be omitted. If this output is present
+ * then outputs 2, 4, 5 must be present as well.
+ * Available since HAL version 1.3.
+ * * 4: The backward activation state output.
+ * A 2-D tensor of shape [batch_size, bw_output_size] containing an
+ * activation state from the last time step in the sequence. This
+ * output is optional and can be omitted. If this output is present
+ * then outputs 2, 3, 5 must be present as well.
+ * Available since HAL version 1.3.
+ * * 5: The backward cell state output.
+ * A tensor of shape [batch_size, bw_cell_size] containing a cell state
+ * from the last time step in the sequence. This output is optional
+ * and can be omitted. If this output is present
+ * then outputs 2-4 must be present as well.
+ * Available since HAL version 1.3.
+ */
+ BIDIRECTIONAL_SEQUENCE_LSTM = 42,
+ /**
+ * A recurrent neural network layer that applies a basic RNN cell to a
+ * sequence of inputs in forward and backward directions.
+ *
+ * This Op unrolls the input along the sequence dimension, and implements
+ * the following operation for each element in the sequence s =
+ * 1...sequence_length:
+ * fw_outputs[s] = fw_state = activation(inputs[s] * fw_input_weights’ +
+ * fw_state * fw_recurrent_weights’ + fw_bias)
+ *
+ * And for each element in sequence t = sequence_length : 1
+ * bw_outputs[t] = bw_state = activation(inputs[t] * bw_input_weights’ +
+ * bw_state * bw_recurrent_weights’ + bw_bias)
+ *
+ * Where:
+ * * “{fw,bw}_input_weights” is a weight matrix that multiplies the inputs;
+ * * “{fw,bw}_recurrent_weights” is a weight matrix that multiplies the
+ * current “state” which itself is the output from the previous time step
+ * computation;
+ * * “{fw,bw}_bias” is a bias vector (added to each output vector in the
+ * batch);
+ * * “activation” is the function passed as the “fused_activation_function”
+ * argument (if not “NONE”).
+ *
+ * The op supports cross-linking via an auxiliary input. Regular cell feeds
+ * one input into the two RNN cells in the following way:
+ *
+ * INPUT (INPUT_REVERSED)
+ * | |
+ * ---------------------
+ * | FW_RNN BW_RNN |
+ * ---------------------
+ * | |
+ * FW_OUT BW_OUT
+ *
+ * An op with cross-linking takes two inputs and feeds them into the RNN
+ * cells in the following way:
+ *
+ * AUX_INPUT (AUX_INPUT_REVERSED)
+ * | |
+ * INPUT | (INPUT_R'D.)|
+ * | | | |
+ * -----------------------
+ * | \ / \ / |
+ * | FW_RNN BW_RNN |
+ * -----------------------
+ * | |
+ * FW_OUT BW_OUT
+ *
+ * The cross-linking mode is enabled iff auxiliary input and auxiliary
+ * weights are present. While stacking this op on top of itself, this
+ * allows to connect both forward and backward outputs from previous cell
+ * to the next cell's input.
+ *
+ * Since HAL version 1.3 parallel linking mode is supported. The mode is
+ * enabled if auxiliary input is present but auxiliary weights are omitted.
+ * In this case, the cell feeds inputs into the RNN in the following way:
+ *
+ * INPUT (AUX_INPUT_REVERSED)
+ * | |
+ * ---------------------
+ * | FW_RNN BW_RNN |
+ * ---------------------
+ * | |
+ * FW_OUT BW_OUT
+ *
+ * While stacking this op on top of itself, this allows to connect both
+ * forward and backward outputs from previous cell to the next cell's
+ * corresponding inputs.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * The input tensors must all be the same type.
+ *
+ * Inputs:
+ * * 0: input.
+ * A 3-D tensor. The shape is defined by the input 6 (timeMajor). If
+ * it is set to true, then the input has a shape [maxTime, batchSize,
+ * inputSize], otherwise the input has a shape [batchSize, maxTime,
+ * inputSize].
+ * * 1: fwWeights.
+ * A 2-D tensor of shape [fwNumUnits, inputSize].
+ * * 2: fwRecurrentWeights.
+ * A 2-D tensor of shape [fwNumUnits, fwNumUnits].
+ * * 3: fwBias.
+ * A 1-D tensor of shape [fwNumUnits].
+ * * 4: fwHiddenState.
+ * A 2-D tensor of shape [batchSize, fwNumUnits]. Specifies a hidden
+ * state input for the first time step of the computation.
+ * * 5: bwWeights.
+ * A 2-D tensor of shape [bwNumUnits, inputSize].
+ * * 6: bwRecurrentWeights.
+ * A 2-D tensor of shape [bwNumUnits, bwNumUnits].
+ * * 7: bwBias.
+ * A 1-D tensor of shape [bwNumUnits].
+ * * 8: bwHiddenState
+ * A 2-D tensor of shape [batchSize, bwNumUnits]. Specifies a hidden
+ * state input for the first time step of the computation.
+ * * 9: auxInput.
+ * A 3-D tensor. The shape is defined by the input 6 (timeMajor). If
+ * it is set to true, then the input has a shape [maxTime, batchSize,
+ * auxInputSize], otherwise the input has a shape [batchSize, maxTime,
+ * auxInputSize]. Can be omitted. See the docs above for the usage
+ * modes explanation.
+ * * 10:fwAuxWeights.
+ * A 2-D tensor of shape [fwNumUnits, auxInputSize]. Can be omitted.
+ * See the docs above for the usage modes explanation.
+ * * 11:bwAuxWeights.
+ * A 2-D tensor of shape [bwNumUnits, auxInputSize]. Can be omitted.
+ * See the docs above for the usage modes explanation.
+ * * 12:fusedActivationFunction.
+ * A {@link FusedActivationFunc} value indicating the activation function. If
+ * “NONE” is specified then it results in a linear activation.
+ * * 13:timeMajor
+ * An {@link OperandType::BOOL} scalar specifying the shape format
+ * of input and output tensors.
+ * * 14:mergeOutputs
+ * An {@link OperandType::BOOL} scalar specifying if the outputs
+ * from forward and backward cells are separate (if set to false) or
+ * concatenated (if set to true).
+ * Outputs:
+ * * 0: fwOutput.
+ * A 3-D tensor. The first two dimensions of the shape are defined by
+ * the input 6 (timeMajor) and the third dimension is defined by the
+ * input 14 (mergeOutputs). If timeMajor is set to true, then the first
+ * two dimensions are [maxTime, batchSize], otherwise they are set to
+ * [batchSize, maxTime]. If mergeOutputs is set to true, then the third
+ * dimension is equal to (fwNumUnits + bwNumUnits), otherwise it is set
+ * to fwNumUnits.
+ * * 1: bwOutput.
+ * A 3-D tensor. If the input 14 (mergeOutputs) is set to true, then
+ * this tensor is not produced. The shape is defined by the input 6
+ * (timeMajor). If it is set to true, then the shape is set to
+ * [maxTime, batchSize, bwNumUnits], otherwise the shape is set to
+ * [batchSize, maxTime, bwNumUnits].
+ * * 2: The forward hidden state output.
+ * A 2-D tensor of shape [batchSize, fwNumUnits] containing a hidden
+ * state from the last time step in the sequence. This output is
+ * optional and can be omitted. If this output is present then output
+ * 3 must be present as well.
+ * Available since HAL version 1.3.
+ * * 3: The backward hidden state output.
+ * A 2-D tensor of shape [batchSize, bwNumUnits] containing a hidden
+ * state from the last time step in the sequence. This output is
+ * optional and can be omitted. If this output is present then output
+ * 2 must be present as well.
+ * Available since HAL version 1.3.
+ */
+ BIDIRECTIONAL_SEQUENCE_RNN = 43,
+ /**
+ * Greedily selects a subset of bounding boxes in descending order of score.
+ *
+ * This op applies NMS algorithm to each class. In each loop of execution,
+ * the box with maximum score gets selected and removed from the pending set.
+ * The scores of the rest of boxes are lowered according to the
+ * intersection-over-union (IOU) overlapping with the previously selected
+ * boxes and a specified NMS kernel method. Any boxes with score less
+ * than a threshold are removed from the pending set.
+ *
+ * Three NMS kernels are supported:
+ * * Hard: score_new = score_old * (1 if IoU < threshold else 0)
+ * * Linear: score_new = score_old * (1 if IoU < threshold else 1 - IoU)
+ * * Gaussian: score_new = score_old * exp(- IoU^2 / sigma)
+ *
+ * Axis-aligned bounding boxes are represented by its upper-left corner
+ * coordinate (x1,y1) and lower-right corner coordinate (x2,y2). A valid
+ * bounding box should satisfy x1 <= x2 and y1 <= y2.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Inputs:
+ * * 0: A 2-D Tensor of shape [num_rois, num_classes], specifying the score
+ * of each bounding box proposal. The boxes are grouped by batches in the
+ * first dimension. Zero num_rois is supported for this tensor.
+ * * 1: A 2-D Tensor specifying the bounding boxes of shape
+ * [num_rois, num_classes * 4], organized in the order [x1, y1, x2, y2].
+ * The boxes are grouped by batches in the first dimension. The sequential
+ * order of the boxes corresponds with input0. For input0 of type
+ * {@link OperandType::TENSOR_QUANT8_ASYMM}, this tensor should be of
+ * {@link OperandType::TENSOR_QUANT16_ASYMM}, with zeroPoint of 0 and
+ * scale of 0.125.
+ * For input0 of type {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED},
+ * this tensor should be of {@link OperandType::TENSOR_QUANT16_ASYMM},
+ * with zeroPoint of -128 and scale of 0.125.
+ * Zero num_rois is supported for this tensor.
+ * * 2: A 1-D {@link OperandType::TENSOR_INT32} tensor, of shape
+ * [num_rois], specifying the batch index of each box. Boxes with
+ * the same batch index are grouped together.
+ * * 3: An {@link OperandType::FLOAT32} scalar, score_threshold. Boxes
+ * with scores lower than the threshold are filtered before sending
+ * to the NMS algorithm.
+ * * 4: An {@link OperandType::INT32} scalar, specifying the maximum
+ * number of selected bounding boxes for each image. Set to a negative
+ * value for unlimited number of output bounding boxes.
+ * * 5: An {@link OperandType::INT32} scalar, specifying the NMS
+ * kernel method, options are 0:hard, 1:linear, 2:gaussian.
+ * * 6: An {@link OperandType::FLOAT32} scalar, specifying the IoU
+ * threshold in hard and linear NMS kernel. This field is ignored if
+ * gaussian kernel is selected.
+ * * 7: An {@link OperandType::FLOAT32} scalar, specifying the sigma in
+ * gaussian NMS kernel. This field is ignored if gaussian kernel is
+ * not selected.
+ * * 8: An {@link OperandType::FLOAT32} scalar, nms_score_threshold.
+ * Boxes with scores lower than the threshold are dropped during the
+ * score updating phase in soft NMS.
+ *
+ * Outputs:
+ * * 0: A 1-D Tensor of the same {@link OperandType} as input0, with shape
+ * [num_output_rois], specifying the score of each output box. The boxes
+ * are grouped by batches, but the sequential order in each batch is not
+ * guaranteed. For type of {@link OperandType::TENSOR_QUANT8_ASYMM},
+ * guaranteed. For type of {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * or {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED},
+ * the scale and zero point must be the same as input0.
+ * * 1: A 2-D Tensor of the same {@link OperandType} as input1, with shape
+ * [num_output_rois, 4], specifying the coordinates of each
+ * output bounding box with the same format as input1. The sequential
+ * order of the boxes corresponds with output0. For type of
+ * {@link OperandType::TENSOR_QUANT16_ASYMM}, the scale must be
+ * 0.125 and the zero point must be 0.
+ * * 2: A 1-D {@link OperandType::TENSOR_INT32} tensor, of shape
+ * [num_output_rois], specifying the class of each output box. The
+ * sequential order of the boxes corresponds with output0.
+ * * 3: A 1-D {@link OperandType::TENSOR_INT32} tensor, of shape
+ * [num_output_rois], specifying the batch index of each box. Boxes
+ * with the same batch index are grouped together.
+ */
+ BOX_WITH_NMS_LIMIT = 44,
+ /**
+ * Casts a tensor to a type.
+ *
+ * This operation ignores the scale and zeroPoint of quanized tensors,
+ * e.g. it treats a {@link OperandType::TENSOR_QUANT8_ASYMM} input
+ * as a tensor of uint8 values.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * Since HAL version 1.3, casting tensors of the following
+ * {@link OperandType} to the same {@link OperandType} is supported:
+ * * {@link OperandType::TENSOR_BOOL8}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT16_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT16_SYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED}
+ * * {@link OperandType::TENSOR_QUANT8_SYMM}
+ *
+ * Supported tensor rank: from 1
+ *
+ * Inputs:
+ * * 0: A tensor.
+ *
+ * Outputs:
+ * * 0: A tensor with the same shape as input0.
+ */
+ CAST = 45,
+ /**
+ * Shuffle the channels of the input tensor.
+ *
+ * Given an input tensor and a integer value of num_groups, CHANNEL_SHUFFLE
+ * divide the channel dimension into num_groups groups, and reorganize the
+ * channels by grouping channels with the same index in each group.
+ *
+ * Along the channel dimension, the output is calculated using this formula:
+ *
+ * output_channel[k * num_groups + g] = input_channel[g * group_size + k]
+ *
+ * where group_size = num_channels / num_groups
+ *
+ * The number of channels must be divisible by num_groups.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: An n-D tensor, specifying the tensor to be shuffled.
+ * * 1: An {@link OperandType::INT32} scalar, specifying the number of
+ * groups.
+ * * 2: An {@link OperandType::INT32} scalar, specifying the dimension
+ * channel shuffle would be performed on. Negative index is used to
+ * specify axis from the end (e.g. -1 for the last axis). Must be in
+ * the range [-n, n).
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} and same shape as input0.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint must be the same as input0.
+ */
+ CHANNEL_SHUFFLE = 46,
+ /**
+ * Apply postprocessing steps to bounding box detections.
+ *
+ * Bounding box detections are generated by applying transformation on a set
+ * of predefined anchors with the bounding box deltas from bounding box
+ * regression. A final step of hard NMS is applied to limit the number of
+ * returned boxes.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * Inputs:
+ * * 0: A 3-D Tensor of shape [batches, num_anchors, num_classes], specifying
+ * the score of each anchor with each class. Class 0 for each
+ * [batches, num_anchors, 0] is background and will be ignored.
+ * * 1: A 3-D Tensor of shape [batches, num_anchors, length_box_encoding], with
+ * the first four values in length_box_encoding specifying the bounding
+ * box deltas. The box deltas are encoded in the order of [dy, dx, dh, dw],
+ * where dy and dx is the linear-scale relative correction factor for the
+ * center position of the bounding box with respect to the width and height,
+ * dh and dw is the log-scale relative correction factor for the width and
+ * height. All the entries in length_box_encoding beyond the first four
+ * values are ignored in this operation.
+ * * 2: A 2-D Tensor of shape [num_anchors, 4], specifying the shape of each
+ * predefined anchor, with format [ctr_y, ctr_x, h, w], where ctr_y and
+ * ctr_x are the center position of the box, and h and w are the height
+ * and the width.
+ * * 3: An {@link OperandType::FLOAT32} scalar, specifying the scaling
+ * factor for dy in bounding box deltas.
+ * * 4: An {@link OperandType::FLOAT32} scalar, specifying the scaling
+ * factor for dx in bounding box deltas.
+ * * 5: An {@link OperandType::FLOAT32} scalar, specifying the scaling
+ * factor for dh in bounding box deltas.
+ * * 6: An {@link OperandType::FLOAT32} scalar, specifying the scaling
+ * factor for dw in bounding box deltas.
+ * * 7: An {@link OperandType::BOOL} scalar, set to true to use regular
+ * multi-class NMS algorithm that do NMS separately for each class,
+ * set to false for a faster algorithm that only do one single NMS
+ * using the highest class score..
+ * * 8: An {@link OperandType::INT32} scalar, max_num_detections, specifying
+ * the maximum number of boxes for the output. Boxes with the lowest
+ * scores are discarded to meet the limit.
+ * * 9: An {@link OperandType::INT32} scalar, only used when input7 is
+ * set to false, specifying the maximum number of classes per detection.
+ * * 10: An {@link OperandType::INT32} scalar, only used when input7 is
+ * set to true, specifying the maximum number of detections when
+ * applying NMS algorithm for each single class.
+ * * 11: A scalar, score_threshold. Boxes with scores lower than the
+ * threshold are filtered before sending to the NMS algorithm. The
+ * scalar must be of {@link OperandType::FLOAT16} if input0 is of
+ * {@link OperandType::TENSOR_FLOAT16} and of
+ * {@link OperandType::FLOAT32} if input0 is of
+ * {@link OperandType::TENSOR_FLOAT32}.
+ * * 12: A scalar, specifying the IoU threshold for hard NMS. The scalar
+ * must be of {@link OperandType::FLOAT16} if input0 is of
+ * {@link OperandType::TENSOR_FLOAT16} and of
+ * {@link OperandType::FLOAT32} if input0 is of
+ * {@link OperandType::TENSOR_FLOAT32}.
+ * * 13: An {@link OperandType::BOOL} scalar, set to true to include
+ * background class in the list of label map for the output, set
+ * to false to not include the background. When the background
+ * class is included, it has label 0 and the output classes start
+ * at 1 in the label map, otherwise, the output classes start at 0.
+ *
+ * Outputs:
+ * * 0: A 2-D tensor of the same {@link OperandType} as input0, with shape
+ * [batches, max_num_detections], specifying the score of each output
+ * detections.
+ * * 1: A 3-D tensor of shape [batches, max_num_detections, 4], specifying the
+ * coordinates of each output bounding box, with format
+ * [y1, x1, y2, x2].
+ * * 2: A 2-D {@link OperandType::TENSOR_INT32} tensor, of shape
+ * [batches, max_num_detections], specifying the class label for each
+ * output detection.
+ * * 3: An 1-D {@link OperandType::TENSOR_INT32} tensor, of shape [batches],
+ * specifying the number of valid output detections for each batch.
+ */
+ DETECTION_POSTPROCESSING = 47,
+ /**
+ * For input tensors x and y, computes x == y elementwise.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_BOOL8}
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: from 1
+ *
+ * This operation supports broadcasting.
+ *
+ * Inputs:
+ * * 0: A tensor.
+ * * 1: A tensor of the same {@link OperandType} and dimensions compatible
+ * with input0.
+ *
+ * Outputs:
+ * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
+ */
+ EQUAL = 48,
+ /**
+ * Computes exponential of x element-wise.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: from 1.
+ *
+ * Inputs:
+ * * 0: A tensor.
+ *
+ * Outputs:
+ * * 0: The output tensor of same shape as input0.
+ */
+ EXP = 49,
+ /**
+ * Inserts a dimension of 1 into a tensor's shape.
+ *
+ * Given a tensor input, this operation inserts a dimension of 1 at the
+ * given dimension index of input's shape. The dimension index starts at
+ * zero; if you specify a negative dimension index, it is counted backward
+ * from the end.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: from 1
+ *
+ * Inputs:
+ * * 0: An n-D tensor.
+ * * 1: An {@link OperandType::INT32} scalar specifying the dimension
+ * index to expand. Must be in the range [-(n + 1), (n + 1)).
+ *
+ * Outputs:
+ * * 0: An (n + 1)-D tensor with the same {@link OperandType} and data as
+ * input0.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint must be the same as input0.
+ */
+ EXPAND_DIMS = 50,
+ /**
+ * Gathers values along an axis.
+ *
+ * Produces an output tensor with shape
+ * input0.dimension[:axis] + indices.dimension + input0.dimension[axis + 1:]
+ * where:
+ * # Vector indices (output is rank(input0)).
+ * output[a_0, ..., a_n, i, b_0, ..., b_n] =
+ * input0[a_0, ..., a_n, indices[i], b_0, ..., b_n]
+ *
+ * # Higher rank indices (output is rank(input0) + rank(indices) - 1).
+ * output[a_0, ..., a_n, i, ..., j, b_0, ... b_n] =
+ * input0[a_0, ..., a_n, indices[i, ..., j], b_0, ..., b_n]
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: from 1
+ *
+ * Inputs:
+ * * 0: An n-D tensor from which to gather values.
+ * * 1: An {@link OperandType::INT32} scalar specifying the axis.
+ * Negative index is used to specify axis from the end
+ * (e.g. -1 for the last axis). Must be in the range [-n, n).
+ * * 2: A k-D tensor {@link OperandType::TENSOR_INT32} of indices.
+ * The values must be in the bounds of the corresponding dimensions
+ * of input0.
+ *
+ * Outputs:
+ * * 0: An (n + k - 1)-D tensor with the same {@link OperandType} as input0.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint must be the same as input0.
+ */
+ GATHER = 51,
+ /**
+ * Generate aixs-aligned bounding box proposals.
+ *
+ * Bounding box proposals are generated by applying transformation on a set
+ * of predefined anchors with the bounding box deltas from bounding box
+ * regression. A final step of hard NMS is applied to limit the number of
+ * returned boxes.
+ *
+ * Axis-aligned bounding boxes are represented by its upper-left corner
+ * coordinate (x1,y1) and lower-right corner coordinate (x2,y2). A valid
+ * bounding box should satisfy x1 <= x2 and y1 <= y2.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Inputs:
+ * * 0: A 4-D Tensor specifying the score of each anchor at each
+ * location. With "NHWC" data layout, the tensor shape is
+ * [batches, height, width, num_anchors]. With "NCHW" data layout,
+ * the tensor shape is [batches, num_anchors, height, width].
+ * * 1: A 4-D Tensor specifying the bounding box deltas. With "NHWC" data
+ * layout, the tensor shape is [batches, height, width, num_anchors * 4].
+ * With "NCHW" data layout, the tensor shape is
+ * [batches, num_anchors * 4, height, width]. The box deltas are encoded
+ * in the order of [dx, dy, dw, dh], where dx and dy is the linear-scale
+ * relative correction factor for the center position of the bounding box
+ * with respect to the width and height, dw and dh is the log-scale
+ * relative correction factor for the width and height. The last
+ * dimensions is the channel dimension.
+ * * 2: A 2-D Tensor of shape [num_anchors, 4], specifying the shape of each
+ * predefined anchor, with format [x1, y1, x2, y2]. For input0 of type
+ * {@link OperandType::TENSOR_QUANT8_ASYMM} or
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED}, this tensor should be of
+ * {@link OperandType::TENSOR_QUANT16_SYMM}, with scale of 0.125.
+ * * 3: A 2-D Tensor of shape [batches, 2], specifying the size of
+ * each image in the batch, with format [image_height, image_width].
+ * For input0 of type {@link OperandType::TENSOR_QUANT8_ASYMM} or
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED}, this
+ * tensor should be of {@link OperandType::TENSOR_QUANT16_SYMM}, with
+ * scale of 0.125.
+ * * 4: An {@link OperandType::FLOAT32} scalar, specifying the ratio
+ * from the height of original image to the height of feature map.
+ * * 5: An {@link OperandType::FLOAT32} scalar, specifying the ratio
+ * from the width of original image to the width of feature map.
+ * * 6: An {@link OperandType::INT32} scalar, specifying the maximum
+ * number of boxes before going into the hard NMS algorithm. Boxes
+ * with the lowest scores are discarded to meet the limit. Set to
+ * a non-positive value for unlimited number.
+ * * 7: An {@link OperandType::INT32} scalar, specifying the maximum
+ * number of boxes returning from the hard NMS algorithm. Boxes
+ * with the lowest scores are discarded to meet the limit. Set to
+ * a non-positive value for unlimited number.
+ * * 8: An {@link OperandType::FLOAT32} scalar, specifying the IoU
+ * threshold for hard NMS.
+ * * 9: An {@link OperandType::FLOAT32} scalar, min_size. Boxes with
+ * height or width lower than the absolute threshold are filtered out.
+ * * 10: An {@link OperandType::BOOL} scalar, set to true to specify
+ * NCHW data layout for input0 and input1. Set to false for NHWC.
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} as input0, of shape
+ * [num_output_rois], specifying the score of each output box.
+ * The boxes are grouped by batches, but the sequential order in
+ * each batch is not guaranteed. For type of
+ * {@link OperandType::TENSOR_QUANT8_ASYMM} or
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED}, the scale and zero
+ * point must be the same as input0.
+ * * 1: A tensor of the same {@link OperandType} as input3, of shape
+ * [num_output_rois, 4], specifying the coordinates of each output
+ * bounding box for each class, with format [x1, y1, x2, y2].
+ * The sequential order of the boxes corresponds with output0.
+ * For type of {@link OperandType::TENSOR_QUANT16_ASYMM}, the
+ * scale must be 0.125 and the zero point must be 0.
+ * * 2: A 1-D {@link OperandType::TENSOR_INT32} tensor, of shape
+ * [num_output_rois], specifying the batch index of each box. Boxes
+ * with the same batch index are grouped together.
+ */
+ GENERATE_PROPOSALS = 52,
+ /**
+ * For input tensors x and y, computes x > y elementwise.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_BOOL8}
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: from 1
+ *
+ * This operation supports broadcasting.
+ *
+ * Inputs:
+ * * 0: A tensor.
+ * * 1: A tensor of the same {@link OperandType} and dimensions compatible
+ * with input0.
+ *
+ * Outputs:
+ * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
+ */
+ GREATER = 53,
+ /**
+ * For input tensors x and y, computes x >= y elementwise.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_BOOL8}
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: from 1
+ *
+ * This operation supports broadcasting.
+ *
+ * Inputs:
+ * * 0: A tensor.
+ * * 1: A tensor of the same {@link OperandType} and dimensions compatible
+ * with input0.
+ *
+ * Outputs:
+ * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
+ */
+ GREATER_EQUAL = 54,
+ /**
+ * Performs a grouped 2-D convolution operation.
+ *
+ * Given an input tensor of shape [batches, height, width, depth_in] and a
+ * filter tensor of shape [depth_out, filter_height, filter_width, depth_group]
+ * containing depth_out convolutional filters of depth depth_group, GROUPED_CONV
+ * applies a group of different filters to each input channel group, then
+ * concatenates the results together.
+ *
+ * Specifically, the input channels are divided into num_groups groups, each with
+ * depth depth_group, i.e. depth_in = num_groups * depth_group. The convolutional
+ * filters are also divided into num_groups groups, i.e. depth_out is divisible
+ * by num_groups. GROUPED_CONV applies each group of filters to the corresponding
+ * input channel group, and the result are concatenated together.
+ *
+ * The output dimensions are functions of the filter dimensions, stride, and
+ * padding.
+ *
+ * The values in the output tensor are computed as:
+ *
+ * output[b, i, j, g * channel_multiplier + q] =
+ * sum_{di, dj, dk} (
+ * input[b, strides[1] * i + di, strides[2] * j + dj,
+ * g * depth_group + dk] *
+ * filter[g * channel_multiplier + q, di, dj, dk]
+ * ) + bias[channel]
+ *
+ * where channel_multiplier = depth_out / num_groups
+ *
+ * Supported tensor {@link OperandType} configurations:
+ * * 16 bit floating point:
+ * * * {@link OperandType::TENSOR_FLOAT16} for input, filter, output, and bias.
+ *
+ * * 32 bit floating point:
+ * * * {@link OperandType::TENSOR_FLOAT32} for input, filter, output, and bias.
+ *
+ * * Quantized:
+ * * * {@link OperandType::TENSOR_QUANT8_ASYMM} for input, filter, and output.
+ * * * {@link OperandType::TENSOR_INT32} for bias (with scale set to
+ * * * input.scale * filter.scale).
+ *
+ * * Quantized signed (since HAL version 1.3):
+ * * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} for input, filter, and output.
+ * * * {@link OperandType::TENSOR_INT32} for bias (with scale set to
+ * * * input.scale * filter.scale).
+ *
+ * * Quantized with symmetric per channel quantization for the filter:
+ * * * {@link OperandType::TENSOR_QUANT8_ASYMM} for input, and output.
+ * * * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter.
+ * * * {@link OperandType::TENSOR_INT32} for bias (scale set to 0.0,
+ * * * each value scaling is separate and equal to input.scale * filter.scales[channel]).
+ *
+ * * Quantized signed with filter symmetric per channel quantization (since HAL version 1.3):
+ * * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} for input, and output.
+ * * * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter.
+ * * * {@link OperandType::TENSOR_INT32} for bias (scale set to 0.0,
+ * * * each value scaling is separate and equal to input.scale * filter.scales[channel]).
+ *
+ * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
+ * With the default data layout NHWC, the data is stored in the order of:
+ * [batch, height, width, channels]. Alternatively, the data layout could
+ * be NCHW, the data storage order of: [batch, channels, height, width].
+ *
+ * Both explicit padding and implicit padding are supported.
+ *
+ * Inputs (explicit padding):
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
+ * specifying the input, where depth_in = num_groups * depth_group.
+ * * 1: A 4-D tensor, of shape
+ * [depth_out, filter_height, filter_width, depth_group], specifying
+ * the filter, where depth_out must be divisible by num_groups. For
+ * tensor of type {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}
+ * the channel dimension (channelDim at
+ * {@link SymmPerChannelQuantParams}) must be set to 0.
+ * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
+ * tensor of type {@link OperandType::TENSOR_FLOAT32} or
+ * {@link OperandType::TENSOR_FLOAT16}, the bias must be of the same type.
+ * For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED}
+ * the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint
+ * of 0 and bias_scale == input_scale * filter_scale. For filter tensor
+ * of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}, the bias
+ * should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of
+ * 0 and bias_scale of 0. The actual scale of each value 'i' is equal to
+ * bias_scale[i] = input_scale * filter_scale[i].
+ * * 3: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the left, in the ‘width’ dimension.
+ * * 4: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the right, in the ‘width’ dimension.
+ * * 5: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the top, in the ‘height’ dimension.
+ * * 6: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the bottom, in the ‘height’ dimension.
+ * * 7: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘width’ dimension.
+ * * 8: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘height’ dimension.
+ * * 9: An {@link OperandType::INT32} scalar, specifying the number of
+ * groups.
+ * * 10: An {@link OperandType::INT32} scalar, and has to be one of the
+ * {@link FusedActivationFunc} values. Specifies the activation to
+ * invoke on the result.
+ * * 11: An {@link OperandType::BOOL} scalar, set to true to specify
+ * NCHW data layout for input0 and output0. Set to false for NHWC.
+ *
+ * Inputs (implicit padding):
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
+ * specifying the input, where depth_in = num_groups * depth_group.
+ * * 1: A 4-D tensor, of shape
+ * [depth_out, filter_height, filter_width, depth_group], specifying
+ * the filter, where depth_out must be divisible by num_groups. For
+ * tensor of type {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}
+ * the channel dimension (SymmPerChannelQuantParams::channelDim)
+ * must be set to 0.
+ * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
+ * tensor of type {@link OperandType::TENSOR_FLOAT32} or
+ * {@link OperandType::TENSOR_FLOAT16}, the bias must be of the same
+ * {@link OperandType::TENSOR_FLOAT16}, the bias must be of the same type.
+ * For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED}
+ * the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint
+ * of 0 and bias_scale == input_scale * filter_scale. For filter tensor
+ * of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}, the bias
+ * should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of
+ * 0 and bias_scale of 0. The actual scale of each value 'i' is equal to
+ * bias_scale[i] = input_scale * filter_scale[i].
+ * * 3: An {@link OperandType::INT32} scalar, specifying the implicit
+ * padding scheme, has to be one of the
+ * following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
+ * * 4: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘width’ dimension.
+ * * 5: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘height’ dimension.
+ * * 6: An {@link OperandType::INT32} scalar, specifying the number of
+ * groups.
+ * * 7: An {@link OperandType::INT32} scalar, and has to be one of the
+ * {@link FusedActivationFunc} values. Specifies the activation to
+ * invoke on the result.
+ * * 8: An {@link OperandType::BOOL} scalar, set to true to specify
+ * NCHW data layout for input0 and output0. Set to false for NHWC.
+ *
+ * Outputs:
+ * * 0: The output 4-D tensor, of shape
+ * [batches, out_height, out_width, depth_out].
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint can be different from inputs' scale and zeroPoint.
+ */
+ GROUPED_CONV_2D = 55,
+ /**
+ * Localize the maximum keypoints from heatmaps.
+ *
+ * This operation approximates the accurate maximum keypoint scores and
+ * indices after bicubic upscaling by using Taylor expansion up to the
+ * quadratic term.
+ *
+ * The bounding box is represented by its upper-left corner coordinate
+ * (x1,y1) and lower-right corner coordinate (x2,y2) in the original image.
+ * A valid bounding box should satisfy x1 <= x2 and y1 <= y2.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
+ * With the default data layout NHWC, the data is stored in the order of:
+ * [batch, height, width, channels]. Alternatively, the data layout could
+ * be NCHW, the data storage order of: [batch, channels, height, width].
+ *
+ * Inputs:
+ * * 0: A 4-D Tensor of shape
+ * [num_boxes, heatmap_size, heatmap_size, num_keypoints],
+ * specifying the heatmaps, the height and width of heatmaps should
+ * be the same, and must be greater than or equal to 2.
+ * * 1: A 2-D Tensor of shape [num_boxes, 4], specifying the bounding boxes,
+ * each with format [x1, y1, x2, y2]. For input0 of type
+ * {@link OperandType::TENSOR_QUANT8_ASYMM}, this tensor should
+ * be of {@link OperandType::TENSOR_QUANT16_ASYMM}, with zeroPoint
+ * of 0 and scale of 0.125.
+ * For input0 of type
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED}, this tensor
+ * should be of {@link OperandType::TENSOR_QUANT16_ASYMM}, with
+ * zeroPoint of -128 and scale of 0.125.
+ * * 2: An {@link OperandType::BOOL} scalar, set to true to specify
+ * NCHW data layout for input0. Set to false for NHWC.
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} as input0, with shape
+ * [num_boxes, num_keypoints], specifying score of the keypoints.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} or
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint can be different from input0 scale and zeroPoint.
+ * * 1: A tensor of the same {@link OperandType} as input1, with shape
+ * [num_boxes, num_keypoints, 2], specifying the location of
+ * the keypoints, the second dimension is organized as
+ * [keypoint_x, keypoint_y].
+ * For type of {@link OperandType::TENSOR_QUANT16_ASYMM}, the
+ * scale must be 0.125 and the zero point must be 0.
+ */
+ HEATMAP_MAX_KEYPOINT = 56,
+ /**
+ * Applies instance normalization to the input tensor.
+ *
+ * The values in the output tensor are computed as:
+ *
+ * output[b, h, w, c] =
+ * (input[b, h, w, c] - mean[b, c]) * gamma /
+ * sqrt(var[b, c] + epsilon) + beta
+ *
+ * Where the mean and variance are computed across the spatial dimensions:
+ *
+ * mean[b, c] =
+ * sum_{h, w}(input[b, h, w, c]) / sum(1)
+ *
+ * var[b, c] =
+ * sum_{h, w}(pow(input[b, h, w, c] - mean[b, c], 2)) / sum(1)
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
+ * With the default data layout NHWC, the data is stored in the order of:
+ * [batch, height, width, channels]. Alternatively, the data layout could
+ * be NCHW, the data storage order of: [batch, channels, height, width].
+ *
+ * Inputs:
+ * * 0: An n-D tensor, specifying the tensor to be normalized.
+ * * 1: A scalar, specifying gamma, the scale applied to the normalized
+ * tensor. The scalar must be of {@link OperandType::FLOAT16} if
+ * input0 is of {@link OperandType::TENSOR_FLOAT16} and of
+ * {@link OperandType::FLOAT32} if input0 is of
+ * {@link OperandType::TENSOR_FLOAT32}.
+ * * 2: A scalar, specifying beta, the offset applied to the normalized
+ * tensor. The scalar must be of {@link OperandType::FLOAT16} if
+ * input0 is of {@link OperandType::TENSOR_FLOAT16} and of
+ * {@link OperandType::FLOAT32} if input0 is of
+ * {@link OperandType::TENSOR_FLOAT32}.
+ * * 3: A scalar, specifying epsilon, the small value added to variance to
+ * avoid dividing by zero. The scalar must be of {@link OperandType::FLOAT16} if
+ * input0 is of {@link OperandType::TENSOR_FLOAT16} and of
+ * {@link OperandType::FLOAT32} if input0 is of
+ * {@link OperandType::TENSOR_FLOAT32}.
+ * * 4: An {@link OperandType::BOOL} scalar, set to true to specify
+ * NCHW data layout for input0 and output0. Set to false for NHWC.
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} and same shape as input0.
+ */
+ INSTANCE_NORMALIZATION = 57,
+ /**
+ * For input tensors x and y, computes x < y elementwise.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_BOOL8}
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: from 1
+ *
+ * This operation supports broadcasting.
+ *
+ * Inputs:
+ * * 0: A tensor.
+ * * 1: A tensor of the same {@link OperandType} and dimensions compatible
+ * with input0.
+ *
+ * Outputs:
+ * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
+ */
+ LESS = 58,
+ /**
+ * For input tensors x and y, computes x <= y elementwise.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_BOOL8}
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: from 1
+ *
+ * This operation supports broadcasting.
+ *
+ * Inputs:
+ * * 0: A tensor.
+ * * 1: A tensor of the same {@link OperandType} and dimensions compatible
+ * with input0.
+ *
+ * Outputs:
+ * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
+ */
+ LESS_EQUAL = 59,
+ /**
+ * Computes natural logarithm of x element-wise.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: from 1.
+ *
+ * Inputs:
+ * * 0: A tensor.
+ *
+ * Outputs:
+ * * 0: The output tensor of same shape as input0.
+ */
+ LOG = 60,
+ /**
+ * Returns the truth value of x AND y element-wise.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_BOOL8}
+ *
+ * Supported tensor rank: from 1
+ *
+ * This operation supports broadcasting.
+ *
+ * Inputs:
+ * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
+ * * 1: A tensor of {@link OperandType::TENSOR_BOOL8} and dimensions
+ * compatible with input0.
+ *
+ * Outputs:
+ * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
+ */
+ LOGICAL_AND = 61,
+ /**
+ * Computes the truth value of NOT x element-wise.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_BOOL8}
+ *
+ * Supported tensor rank: from 1.
+ *
+ * Inputs:
+ * * 0: A tensor.
+ *
+ * Outputs:
+ * * 0: The output tensor of same shape as input0.
+ */
+ LOGICAL_NOT = 62,
+ /**
+ * Returns the truth value of x OR y element-wise.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_BOOL8}
+ *
+ * Supported tensor rank: from 1
+ *
+ * This operation supports broadcasting.
+ *
+ * Inputs:
+ * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
+ * * 1: A tensor of {@link OperandType::TENSOR_BOOL8} and dimensions
+ * compatible with input0.
+ *
+ * Outputs:
+ * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
+ */
+ LOGICAL_OR = 63,
+ /**
+ * Computes the log softmax activations given logits.
+ *
+ * The output is calculated using this formula:
+ *
+ * output = logits * beta - log(reduce_sum(exp(logits * beta), axis))
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: from 1.
+ *
+ * Inputs:
+ * * 0: A tensor specifying the input logits.
+ * * 1: A scalar, specifying the positive scaling factor for the exponent,
+ * beta.
+ * For input tensor of {@link OperandType::TENSOR_FLOAT16}, the beta
+ * value must be of {@link OperandType::FLOAT16}.
+ * For input tensor of {@link OperandType::TENSOR_FLOAT32}, the beta
+ * value must be of {@link OperandType::FLOAT32}.
+ * * 2: An {@link OperandType::INT32} scalar specifying the axis to
+ * reduce across. Negative index is used to specify axis from the
+ * end (e.g. -1 for the last axis). Must be in the range [-n, n).
+ *
+ * Outputs:
+ * * 0: The output tensor of the same {@link OperandType} and shape as
+ * input0.
+ */
+ LOG_SOFTMAX = 64,
+ /**
+ * Returns the element-wise maximum of two tensors.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: from 1.
+ *
+ * Inputs:
+ * * 0: A tensor.
+ * * 1: A tensor of the same {@link OperandType} and compatible dimensions
+ * with input0.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scales and zeroPoint can be different from input0 scale and zeroPoint.
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} as input0.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint can be different from inputs' scale and zeroPoint.
+ */
+ MAXIMUM = 65,
+ /**
+ * Returns the element-wise minimum of two tensors.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: from 1.
+ *
+ * Inputs:
+ * * 0: A tensor.
+ * * 1: A tensor of the same {@link OperandType} and compatible dimensions
+ * with input0.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scales and zeroPoint can be different from input0 scale and zeroPoint.
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} as input0.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint can be different from inputs' scale and zeroPoint.
+ */
+ MINIMUM = 66,
+ /**
+ * Computes numerical negative value element-wise.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ *
+ * Supported tensor rank: from 1.
+ *
+ * Inputs:
+ * * 0: A tensor.
+ *
+ * Outputs:
+ * * 0: The output tensor of same shape as input0.
+ */
+ NEG = 67,
+ /**
+ * For input tensors x and y, computes x != y elementwise.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_BOOL8}
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: from 1
+ *
+ * This operation supports broadcasting.
+ *
+ * Inputs:
+ * * 0: A tensor.
+ * * 1: A tensor of the same {@link OperandType} and dimensions compatible
+ * with input0.
+ *
+ * Outputs:
+ * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
+ */
+ NOT_EQUAL = 68,
+ /**
+ * Pads a tensor with the given constant value according to the specified
+ * paddings.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: An n-D tensor, specifying the tensor to be padded.
+ * * 1: A 2-D Tensor of {@link OperandType::TENSOR_INT32}, the paddings
+ * for each spatial dimension of the input tensor. The shape of the
+ * tensor must be {rank(input0), 2}.
+ * padding[i, 0] specifies the number of elements to be padded in the
+ * front of dimension i.
+ * padding[i, 1] specifies the number of elements to be padded after
+ * the end of dimension i.
+ * * 2: An scalar specifying the value to use for padding input0.
+ * For input tensor of {@link OperandType::TENSOR_FLOAT16}, the
+ * pad value must be of {@link OperandType::FLOAT16}.
+ * For input tensor of {@link OperandType::TENSOR_FLOAT32}, the
+ * pad value must be of {@link OperandType::FLOAT32}.
+ * For input tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED},
+ * the pad value must be of {@link OperandType::INT32}. The
+ * scale and zeroPoint are assumed to be the same as in input0.
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} as input0. The
+ * output tensor has the same rank as input0, and each
+ * dimension of the output tensor has the same size as the
+ * corresponding dimension of the input tensor plus the size
+ * of the padding:
+ * output0.dimension[i] =
+ * padding[i, 0] + input0.dimension[i] + padding[i, 1]
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint must be the same as input0.
+ */
+ PAD_V2 = 69,
+ /**
+ * Computes the power of one value to another.
+ *
+ * Given a tensor base and a tensor exponent, this operation computes
+ * base^exponent elementwise.
+ *
+ * This operations supports broadcasting. The size of the output is the
+ * maximum size along each dimension of the input operands. It starts with
+ * the trailing dimensions, and works its way forward.
+ *
+ * For example:
+ * base.dimension = {4, 1, 2}
+ * exponent.dimension = {5, 4, 3, 1}
+ * output.dimension = {5, 4, 3, 2}
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: from 1
+ *
+ * Inputs:
+ * * 0: A tensor specifying the base.
+ * * 1: A tensor specifying the exponent.
+ *
+ * Outputs:
+ * * 0: An output tensor.
+ */
+ POW = 70,
+ /**
+ * Parametric Rectified Linear Unit.
+ *
+ * It follows: f(x) = alpha * x for x < 0, f(x) = x for x >= 0, where alpha
+ * is a learned array with the same {@link OperandType} and compatible
+ * dimensions as input x.
+ *
+ * Two dimensions are compatible when:
+ * 1. they are equal, or
+ * 2. one of them is 1
+ *
+ * The size of the output is the maximum size along each dimension of the
+ * input operands. It starts with the trailing dimensions, and works its way
+ * forward.
+ *
+ * Example:
+ * input.dimension = {4, 1, 2}
+ * alpha.dimension = {5, 4, 3, 1}
+ * output.dimension = {5, 4, 3, 2}
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: from 1
+ *
+ * Inputs:
+ * * 0: A tensor, specifying the input.
+ * * 1: A tensor of the same {@link OperandType}, and compatible dimensions
+ * as input0, specifying the alpha.
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} as input0.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scales and zeroPoint can be different from input0 scale and zeroPoint.
+ */
+ PRELU = 71,
+ /**
+ * Quantizes the input tensor.
+ *
+ * The formula for {@link OperandType::TENSOR_QUANT8_ASYMM} output tensor is:
+ *
+ * output = max(0, min(255, round(input / scale) + zeroPoint)
+ *
+ * The formula for {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} output
+ * tensor is:
+ *
+ * output = max(-128, min(127, round(input / scale) + zeroPoint)
+ *
+ * Supported input tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * Supported output tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: from 1
+ *
+ * Inputs:
+ * * 0: A tensor, may be zero-sized.
+ *
+ * Outputs:
+ * * 0: The output tensor of same shape as input0, but with
+ * {@link OperandType::TENSOR_QUANT8_ASYMM} or.
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED}.
+ */
+ QUANTIZE = 72,
+ /**
+ * A version of quantized LSTM, using 16 bit quantization for internal
+ * state.
+ *
+ * There is no projection layer, so cell state size is equal to the output
+ * size.
+ *
+ * Inputs:
+ * * 0: A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * and shape [numBatches, inputSize] specifying the input to the LSTM
+ * cell. Tensor is quantized with a fixed quantization range of
+ * [-1, 127/128] (scale = 1/128, zeroPoint = 128).
+ * * 1: The input-to-input weights.
+ * A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * and shape [outputSize, inputSize] specifying input-to-input part of
+ * weights for fully-connected layer inside the LSTM cell.
+ * Quantization zero point and scale must be the same across all the
+ * weights.
+ * * 2: The input-to-forget weights.
+ * A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * and shape [outputSize, inputSize] specifying input-to-forget part of
+ * weights for fully-connected layer inside the LSTM cell.
+ * Quantization zero point and scale must be the same across all the
+ * weights.
+ * * 3: The input-to-cell weights.
+ * A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * and shape [outputSize, inputSize] specifying input-to-cell part of
+ * weights for fully-connected layer inside the LSTM cell.
+ * Quantization zero point and scale must be the same across all the
+ * weights.
+ * * 4: The input-to-output weights.
+ * A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * and shape [outputSize, inputSize] specifying input-to-output part of
+ * weights for fully-connected layer inside the LSTM cell.
+ * Quantization zero point and scale must be the same across all the
+ * weights.
+ * * 5: The recurrent-to-input weights.
+ * A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * and shape [outputSize, outputSize] specifying recurrent-to-input part
+ * of weights for fully-connected layer inside the LSTM cell.
+ * Quantization zero point and scale must be the same across all the
+ * weights.
+ * * 6: The recurrent-to-forget weights.
+ * A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * and shape [outputSize, outputSize] specifying recurrent-to-forget
+ * part of weights for fully-connected layer inside the LSTM cell.
+ * Quantization zero point and scale must be the same across all the
+ * weights.
+ * * 7: The recurrent-to-cell weights.
+ * A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * and shape [outputSize, outputSize] specifying recurrent-to-cell part
+ * of weights for fully-connected layer inside the LSTM cell.
+ * Quantization zero point and scale must be the same across all the
+ * weights.
+ * * 8: The recurrent-to-output weights.
+ * A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * and shape [outputSize, outputSize] specifying recurrent-to-output
+ * part of weights for fully-connected layer inside the LSTM cell.
+ * Quantization zero point and scale must be the same across all the
+ * weights.
+ * * 9: The input gate bias.
+ * A 1-D tensor of type {@link OperandType::TENSOR_INT32} and shape
+ * [outputSize] specifying the bias for the fully-connected layer
+ * inside the LSTM cell. Bias is quantized with scale being a product
+ * of input and weights scales and zeroPoint equal to 0.
+ * * 10:The forget gate bias.
+ * A 1-D tensor of type {@link OperandType::TENSOR_INT32} and shape
+ * [outputSize] specifying the bias for the fully-connected layer
+ * inside the LSTM cell. Bias is quantized with scale being a product
+ * of input and weights scales and zeroPoint equal to 0.
+ * * 11:The cell bias.
+ * A 1-D tensor of type {@link OperandType::TENSOR_INT32} and shape
+ * [outputSize] specifying the bias for the fully-connected layer
+ * inside the LSTM cell. Bias is quantized with scale being a product
+ * of input and weights scales and zeroPoint equal to 0.
+ * * 12:The output gate bias.
+ * A 1-D tensor of type {@link OperandType::TENSOR_INT32} and shape
+ * [outputSize] specifying the bias for the fully-connected layer
+ * inside the LSTM cell. Bias is quantized with scale being a product
+ * of input and weights scales and zeroPoint equal to 0.
+ * * 13: A 2-D tensor of type {@link OperandType::TENSOR_QUANT16_SYMM}
+ * and shape [numBatches, outputSize] specifying the cell state from the
+ * previous time step of the LSTM cell. It is quantized using a
+ * quantization range of [-2^4, 2^4 * 32767/32768] (scale = 2^4 /
+ * 32768, zeroPoint = 0).
+ * * 14: A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * and shape [numBathes, outputSize] specifying the output of the LSTM
+ * cell from previous time-step. Tensor is quantized with a fixed
+ * quantization range of [-1, 127/128] (scale = 1/128, zeroPoint =
+ * 128).
+ *
+ *
+ * Outputs:
+ * * 0: A 2-D tensor of type {@link OperandType::TENSOR_QUANT16_SYMM}
+ * and shape [numBatches, outputSize] which contains a cell state from
+ * the current time step. Tensor is quantized using a quantization
+ * range of [-2^4, 2^4 * 32767/32768] (scale = 2^4 / 32768, zeroPoint =
+ * 0).
+ * * 1: A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * and shape [numBathes, outputSize] which contains the output value.
+ * Tensor is quantized with a fixed quantization range of [-1, 127/128]
+ * (scale = 1/128, zeroPoint = 128).
+ */
+ QUANTIZED_16BIT_LSTM = 73,
+ /**
+ * Draws samples from a multinomial distribution.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * Inputs:
+ * * 0: A 2-D tensor with shape [batches, classes], specifying the
+ * unnormalized log-probabilities for all classes.
+ * * 1: A scalar {@link OperandType::INT32}, specifying the number of
+ * independent samples to draw for each row slice.
+ * * 2: A 1-D {@link OperandType::TENSOR_INT32} tensor with shape [2],
+ * specifying seeds used to initialize the random distribution. If both
+ * provided seeds are 0, both will be randomly generated.
+ * Outputs:
+ * * 0: A 2-D {@link OperandType::TENSOR_INT32} tensor with shape
+ * [batches, samples], containing the drawn samples.
+ */
+ RANDOM_MULTINOMIAL = 74,
+ /**
+ * Reduces a tensor by computing the "logical and" of elements along given
+ * dimensions.
+ *
+ * If keep_dims is true, the reduced dimensions are
+ * retained with length 1. Otherwise, the rank of the tensor is reduced by
+ * 1 for each entry in dimensions.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_BOOL8}
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: An n-D tensor.
+ * * 1: A 1-D tensor of {@link OperandType::TENSOR_INT32}. The dimensions
+ * to reduce. Dimension values must be in the range [-n, n).
+ * * 2: An {@link OperandType::BOOL} scalar, keep_dims. If true,
+ * retains reduced dimensions with length 1.
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} as input0.
+ * If all dimensions are reduced and keep_dims is false, the output
+ * shape is [1].
+ */
+ REDUCE_ALL = 75,
+ /**
+ * Reduces a tensor by computing the "logical or" of elements along given
+ * dimensions.
+ *
+ * If keep_dims is true, the reduced dimensions are
+ * retained with length 1. Otherwise, the rank of the tensor is reduced by
+ * 1 for each entry in dimensions.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_BOOL8}
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: An n-D tensor.
+ * * 1: A 1-D tensor of {@link OperandType::TENSOR_INT32}. The dimensions
+ * to reduce. Dimension values must be in the range [-n, n).
+ * * 2: An {@link OperandType::BOOL} scalar, keep_dims. If true,
+ * retains reduced dimensions with length 1.
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} as input0.
+ * If all dimensions are reduced and keep_dims is false, the output
+ * shape is [1].
+ */
+ REDUCE_ANY = 76,
+ /**
+ * Reduces a tensor by computing the maximum of elements along given
+ * dimensions.
+ *
+ * If keep_dims is true, the reduced dimensions are
+ * retained with length 1. Otherwise, the rank of the tensor is reduced by
+ * 1 for each entry in dimensions.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: An n-D tensor.
+ * * 1: A 1-D tensor of {@link OperandType::TENSOR_INT32}. The dimensions
+ * to reduce. Dimension values must be in the range [-n, n).
+ * * 2: An {@link OperandType::BOOL} scalar, keep_dims. If true,
+ * retains reduced dimensions with length 1.
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} as input0.
+ * If all dimensions are reduced and keep_dims is false, the output
+ * shape is [1].
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint must be the same as input0.
+ */
+ REDUCE_MAX = 77,
+ /**
+ * Reduces a tensor by computing the minimum of elements along given
+ * dimensions.
+ *
+ * If keep_dims is true, the reduced dimensions are
+ * retained with length 1. Otherwise, the rank of the tensor is reduced by
+ * 1 for each entry in dimensions.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: An n-D tensor.
+ * * 1: A 1-D tensor of {@link OperandType::TENSOR_INT32}. The dimensions
+ * to reduce. Dimension values must be in the range [-n, n).
+ * * 2: An {@link OperandType::BOOL} scalar, keep_dims. If true,
+ * retains reduced dimensions with length 1.
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} as input0.
+ * If all dimensions are reduced and keep_dims is false, the output
+ * shape is [1].
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint must be the same as input0.
+ */
+ REDUCE_MIN = 78,
+ /**
+ * Reduces a tensor by multiplying elements along given dimensions.
+ *
+ * If keep_dims is true, the reduced dimensions are
+ * retained with length 1. Otherwise, the rank of the tensor is reduced by
+ * 1 for each entry in dimensions.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: An n-D tensor.
+ * * 1: A 1-D tensor of {@link OperandType::TENSOR_INT32}. The dimensions
+ * to reduce. Dimension values must be in the range [-n, n).
+ * * 2: An {@link OperandType::BOOL} scalar, keep_dims. If true,
+ * retains reduced dimensions with length 1.
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} as input0.
+ * If all dimensions are reduced and keep_dims is false, the output
+ * shape is [1].
+ */
+ REDUCE_PROD = 79,
+ /**
+ * Reduces a tensor by summing elements along given dimensions.
+ *
+ * If keep_dims is true, the reduced dimensions are
+ * retained with length 1. Otherwise, the rank of the tensor is reduced by
+ * 1 for each entry in dimensions.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: An n-D tensor.
+ * * 1: A 1-D tensor of {@link OperandType::TENSOR_INT32}. The dimensions
+ * to reduce. Dimension values must be in the range [-n, n).
+ * * 2: An {@link OperandType::BOOL} scalar, keep_dims. If true,
+ * retains reduced dimensions with length 1.
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} as input0.
+ * If all dimensions are reduced and keep_dims is false, the output
+ * shape is [1].
+ */
+ REDUCE_SUM = 80,
+ /**
+ * Select and scale the feature map of each region of interest to a unified
+ * output size by average pooling sampling points from bilinear interpolation.
+ *
+ * The region of interest is represented by its upper-left corner coordinate
+ * (x1,y1) and lower-right corner coordinate (x2,y2) in the original image.
+ * A spatial scaling factor is applied to map into feature map coordinate.
+ * A valid region of interest should satisfy x1 <= x2 and y1 <= y2.
+ *
+ * No rounding is applied in this operation. The sampling points are unified
+ * distributed in the pooling bin and their values are calculated by bilinear
+ * interpolation.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
+ * With the default data layout NHWC, the data is stored in the order of:
+ * [batch, height, width, channels]. Alternatively, the data layout could
+ * be NCHW, the data storage order of: [batch, channels, height, width].
+ *
+ * Inputs:
+ * * 0: A 4-D tensor, specifying the feature map.
+ * * 1: A 2-D Tensor of shape [num_rois, 4], specifying the locations of
+ * the regions of interest, each line with format [x1, y1, x2, y2].
+ * For input0 of type {@link OperandType::TENSOR_QUANT8_ASYMM},
+ * this tensor should be of {@link OperandType::TENSOR_QUANT16_ASYMM},
+ * with zeroPoint of 0 and scale of 0.125. Zero num_rois is
+ * supported for this tensor.
+ * * 2: An 1-D {@link OperandType::TENSOR_INT32} tensor, of shape
+ * [num_rois], specifying the batch index of each box. Boxes with
+ * the same batch index are grouped together. Zero num_rois is
+ * supported for this tensor.
+ * * 3: An {@link OperandType::INT32} scalar, specifying the output
+ * height of the output tensor.
+ * * 4: An {@link OperandType::INT32} scalar, specifying the output
+ * width of the output tensor.
+ * * 5: An {@link OperandType::FLOAT32} scalar, specifying the ratio
+ * from the height of original image to the height of feature map.
+ * * 6: An {@link OperandType::FLOAT32} scalar, specifying the ratio
+ * from the width of original image to the width of feature map.
+ * * 7: An {@link OperandType::INT32} scalar, specifying the number of
+ * sampling points in height dimension used to compute the output.
+ * Set to 0 for adaptive value of ceil(roi_height/out_height).
+ * * 8: An {@link OperandType::INT32} scalar, specifying the number of
+ * sampling points in width dimension used to compute the output.
+ * Set to 0 for adaptive value of ceil(roi_width/out_width).
+ * * 9: An {@link OperandType::BOOL} scalar, set to true to specify
+ * NCHW data layout for input0 and output0. Set to false for NHWC.
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} as input0. The output
+ * shape is [num_rois, out_height, out_width, depth].
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint can be different from the input0 scale and zeroPoint.
+ */
+ ROI_ALIGN = 81,
+ /**
+ * Select and scale the feature map of each region of interest to a unified
+ * output size by max-pooling.
+ *
+ * The region of interest is represented by its upper-left corner coordinate
+ * (x1,y1) and lower-right corner coordinate (x2,y2) in the original image.
+ * A spatial scaling factor is applied to map into feature map coordinate.
+ * A valid region of interest should satisfy x1 <= x2 and y1 <= y2.
+ *
+ * Rounding is applied in this operation to ensure integer boundary for
+ * regions of interest and pooling bins.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
+ * With the default data layout NHWC, the data is stored in the order of:
+ * [batch, height, width, channels]. Alternatively, the data layout could
+ * be NCHW, the data storage order of: [batch, channels, height, width].
+ *
+ * Inputs:
+ * * 0: A 4-D tensor, specifying the feature map.
+ * * 1: A 2-D Tensor of shape [num_rois, 4], specifying the locations of
+ * the regions of interest, each line with format [x1, y1, x2, y2].
+ * For input0 of type {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * this tensor should be of {@link OperandType::TENSOR_QUANT16_ASYMM},
+ * with zeroPoint of 0 and scale of 0.125.
+ * * 2: An 1-D {@link OperandType::TENSOR_INT32} tensor, of shape
+ * [num_rois], specifying the batch index of each box. Boxes with
+ * the same batch index are grouped together.
+ * * 3: An {@link OperandType::INT32} scalar, specifying the output
+ * height of the output tensor.
+ * * 4: An {@link OperandType::INT32} scalar, specifying the output
+ * width of the output tensor.
+ * * 5: An {@link OperandType::FLOAT32} scalar, specifying the ratio
+ * from the height of original image to the height of feature map.
+ * * 6: An {@link OperandType::FLOAT32} scalar, specifying the ratio
+ * from the width of original image to the width of feature map.
+ * * 7: An {@link OperandType::BOOL} scalar, set to true to specify
+ * NCHW data layout for input0 and output0. Set to false for NHWC.
+ *
+ * Outputs:
+ * * 0: A tensor of the same {@link OperandType} as input0. The output
+ * shape is [num_rois, out_height, out_width, depth].
+ * For input0 of type {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint must be the same as input0.
+ */
+ ROI_POOLING = 82,
+ /**
+ * Computes reciprocal of square root of x element-wise.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: from 1.
+ *
+ * Inputs:
+ * * 0: A tensor.
+ *
+ * Outputs:
+ * * 0: The output tensor of same shape as input0.
+ */
+ RSQRT = 83,
+ /**
+ * Using a tensor of booleans c and input tensors x and y select values
+ * elementwise from both input tensors:
+ *
+ * O[i] = C[i] ? x[i] : y[i].
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: from 1
+ *
+ * Inputs:
+ * * 0: A tensor of type {@link OperandType::TENSOR_BOOL8} acting as a
+ * mask that chooses, based on the value at each element, whether the
+ * corresponding element in the output should be taken from input1 (if
+ * true) or input2 (if false).
+ * * 1: An input tensor of the same shape as input0.
+ * * 2: An input tensor of the same shape and type as input1.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * and {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scales and zeroPoint can be different from input1 scale and zeroPoint.
+ *
+ * Outputs:
+ * * 0: A tensor of the same type and shape as input1 and input2.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+ * the scale and zeroPoint can be different from inputs' scale and zeroPoint.
+ */
+ SELECT = 84,
+ /**
+ * Computes sin of x element-wise.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: from 1.
+ *
+ * Inputs:
+ * * 0: A tensor.
+ *
+ * Outputs:
+ * * 0: The output tensor of same shape as input0.
+ */
+ SIN = 85,
+ /**
+ * Extracts a slice of specified size from the input tensor starting at a
+ * specified location.
+ *
+ * The starting location is specified as a 1-D tensor containing offsets
+ * for each dimension. The size is specified as a 1-D tensor containing
+ * either size of a slice along corresponding dimension or -1. In the latter
+ * case, all the remaining elements in dimension are included in the slice.
+ *
+ * A sum of begin offset and a size of a slice must not exceed size of a
+ * corresponding dimension.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: from 1
+ *
+ * Inputs:
+ * * 0: An n-D tensor to take slice from, may be zero-sized.
+ * * 1: A 1-D tensor of type {@link OperandType::TENSOR_INT32} specifying
+ * the beginning indices of the slice in each dimension.
+ * * 2: A 1-D tensor of type {@link OperandType::TENSOR_INT32} specifying
+ * the size of the slice in each dimension.
+ *
+ * Outputs:
+ * * 0: An n-D tensor of the same type as the input containing the slice.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * its scale and zeroPoint has to be same as the input0 scale and zeroPoint.
+ */
+ SLICE = 86,
+ /**
+ * Splits a tensor along a given axis into num_splits subtensors.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: from 1
+ *
+ * Inputs:
+ * * 0: An n-D tensor to split.
+ * * 1: An {@link OperandType::INT32} scalar specifying the axis along
+ * which to split.
+ * * 2: An {@link OperandType::INT32} scalar indicating the number of
+ * splits along given axis. Must evenly divide axis size.
+ *
+ * Outputs:
+ * * 0 ~ (num_splits - 1): Resulting subtensors.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint must be the same as input0.
+ */
+ SPLIT = 87,
+ /**
+ * Computes square root of x element-wise.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: from 1.
+ *
+ * Inputs:
+ * * 0: A tensor.
+ *
+ * Outputs:
+ * * 0: The output tensor of same shape as input0.
+ */
+ SQRT = 88,
+ /**
+ * Constructs a tensor by tiling a given tensor.
+ *
+ * This operation creates a new tensor by replicating `input` `multiples`
+ * times. The output tensor's i-th dimension has `input.dims(i) * multiples[i]`
+ * elements, and the values of `input` are replicated `multiples[i]` times
+ * along the i-th dimension.
+ * For example, tiling `[a b c d]` by `[2]` produces `[a b c d a b c d]`.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: from 1
+ *
+ * Inputs:
+ * * 0: input, an n-D tensor specifying the input.
+ * * 1: multiples, a 1-D tensor of {@link OperandType::TENSOR_INT32}.
+ * The length of multiples must be n.
+ *
+ * Outputs:
+ * * 0: A tiled tensor of the same {@link OperandType} and rank as `input`.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint must be the same as input0.
+ */
+ TILE = 89,
+ /**
+ * Finds values and indices of the k largest entries for the last dimension.
+ *
+ * Resulting values in each dimensions are sorted in descending order. If
+ * two values are equal, the one with larger index appears first.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: from 1
+ *
+ * Inputs:
+ * * 0: input, an n-D tensor specifying the input.
+ * * 1: k, an {@link OperandType::INT32} scalar, specifying the number of
+ * top elements to look for along the last dimension.
+ *
+ * Outputs:
+ * * 0: An n-D tensor of the same type as the input, containing the k
+ * largest elements along each last dimensional slice.
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint must be the same as input0.
+ * * 1: An n-D tensor of type {@link OperandType::TENSOR_INT32}
+ * containing the indices of values within the last dimension of input.
+ */
+ TOPK_V2 = 90,
+ /**
+ * Performs the transpose of 2-D convolution operation.
+ *
+ * This operation is sometimes called "deconvolution" after Deconvolutional
+ * Networks, but is actually the transpose (gradient) of
+ * {@link OperandType::CONV_2D} rather than an actual deconvolution.
+ *
+ * The output dimensions are functions of the filter dimensions, stride, and
+ * padding.
+ *
+ * Supported tensor {@link OperandType} configurations:
+ * * 16 bit floating point:
+ * * * {@link OperandType::TENSOR_FLOAT16} for input, filter, output, and bias.
+ *
+ * * 32 bit floating point:
+ * * * {@link OperandType::TENSOR_FLOAT32} for input, filter, output, and bias.
+ *
+ * * Quantized:
+ * * * {@link OperandType::TENSOR_QUANT8_ASYMM} for input, filter, and output.
+ * * * {@link OperandType::TENSOR_INT32} for bias (with scale set to
+ * * * input.scale * filter.scale).
+ *
+ * * Quantized with symmetric per channel quantization for the filter:
+ * * * {@link OperandType::TENSOR_QUANT8_ASYMM} for input, and output.
+ * * * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter.
+ * * * {@link OperandType::TENSOR_INT32} for bias (scale set to 0.0,
+ * * * each value scaling is separate and equal to input.scale * filter.scales[channel]).
+ *
+ * Available since HAL version 1.3:
+ * * Quantized signed (since HAL version 1.3):
+ * * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} for input, filter, and output.
+ * * * {@link OperandType::TENSOR_INT32} for bias (with scale set to
+ * * * input.scale * filter.scale).
+ *
+ * * Quantized signed with filter symmetric per channel quantization (since HAL version 1.3):
+ * * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} for input, and output.
+ * * * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter.
+ * * * {@link OperandType::TENSOR_INT32} for bias (scale set to 0.0,
+ * * * each value scaling is separate and equal to input.scale * filter.scales[channel]).
+ *
+ * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
+ * With the default data layout NHWC, the data is stored in the order of:
+ * [batch, height, width, channels]. Alternatively, the data layout could
+ * be NCHW, the data storage order of: [batch, channels, height, width].
+ *
+ * Both explicit padding and implicit padding are supported.
+ *
+ * Inputs (explicit padding):
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
+ * specifying the input.
+ * * 1: A 4-D tensor, of shape
+ * [depth_out, filter_height, filter_width, depth_in], specifying the
+ * filter. For tensor of type
+ * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} the channel
+ * dimension (SymmPerChannelQuantParams::channelDim) must be set to 0.
+ * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
+ * tensor of type {@link OperandType::TENSOR_FLOAT32} or
+ * {@link OperandType::TENSOR_FLOAT16}, the bias must be of the
+ * same type.
+ * For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * and {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED},
+ * the bias should be of {@link OperandType::TENSOR_INT32},
+ * with zeroPoint of 0 and bias_scale == input_scale * filter_scale.
+ * For filter tensor of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL},
+ * the bias must be of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0
+ * and bias_scale of 0. The actual scale of each value 'i' is equal to
+ * bias_scale[i] = input_scale * filter_scale[i].
+ * * 3: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the left, in the ‘width’ dimension.
+ * * 4: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the right, in the ‘width’ dimension.
+ * * 5: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the top, in the ‘height’ dimension.
+ * * 6: An {@link OperandType::INT32} scalar, specifying the padding on
+ * the bottom, in the ‘height’ dimension.
+ * * 7: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘width’ dimension.
+ * * 8: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘height’ dimension.
+ * * 9: An {@link OperandType::INT32} scalar, and has to be one of the
+ * {@link FusedActivationFunc} values. Specifies the activation to
+ * invoke on the result.
+ * * 10: An {@link OperandType::BOOL} scalar, set to true to specify
+ * NCHW data layout for input0 and output0. Set to false for NHWC.
+ *
+ * Inputs (implicit padding):
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
+ * specifying the input.
+ * * 1: A 4-D tensor, of shape
+ * [depth_out, filter_height, filter_width, depth_in], specifying the
+ * filter. For tensor of type
+ * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} the channel
+ * dimension (SymmPerChannelQuantParams::channelDim) must be set to 0.
+ * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
+ * tensor of type {@link OperandType::TENSOR_FLOAT32} or
+ * {@link OperandType::TENSOR_FLOAT16}, the bias should be of the
+ * same type.
+ * For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * and {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED},
+ * the bias should be of {@link OperandType::TENSOR_INT32},
+ * with zeroPoint of 0 and bias_scale == input_scale * filter_scale.
+ * For filter tensor of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL},
+ * the bias must be of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0
+ * and bias_scale of 0. The actual scale of each value 'i' is equal to
+ * bias_scale[i] = input_scale * filter_scale[i].
+ * * 3: An {@link OperandType::TENSOR_INT32} tensor, specifying the output
+ * tensor shape.
+ * * 4: An {@link OperandType::INT32} scalar, specifying the implicit
+ * padding scheme, has to be one of the
+ * following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
+ * * 5: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘width’ dimension.
+ * * 6: An {@link OperandType::INT32} scalar, specifying the stride when
+ * walking through input in the ‘height’ dimension.
+ * * 7: An {@link OperandType::INT32} scalar, and has to be one of the
+ * {@link FusedActivationFunc} values. Specifies the activation to
+ * invoke on the result.
+ * * 8: An {@link OperandType::BOOL} scalar, set to true to specify
+ * NCHW data layout for input0 and output0. Set to false for NHWC.
+ *
+ * Outputs:
+ * * 0: The output 4-D tensor, of shape
+ * [batches, out_height, out_width, depth_out].
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint can be different from inputs' scale and zeroPoint.
+ */
+ TRANSPOSE_CONV_2D = 91,
+ /**
+ * A recurrent neural network specified by an LSTM cell.
+ *
+ * Performs (fully) dynamic unrolling of input.
+ *
+ * This Op unrolls the input along the time dimension, and implements the
+ * following operation for each element in the sequence
+ * s = 1...sequence_length:
+ * outputs[s] = projection(state = activation(LSTMOp(inputs[s])))
+ *
+ * Where LSTMOp is the LSTM op as in {@link OperandType::LSTM},
+ * the "projection" is an optional projection layer from state and output
+ * and the “activation” is the function passed as the
+ * “fused_activation_function” argument (if not “NONE”).
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: 3, either time-major or batch-major.
+ *
+ * All input and output tensors must be of the same type.
+ *
+ * Inputs:
+ * * 0: The input (\f$x_t\f$).
+ * A 3-D tensor of shape:
+ * If time-major: [max_time, batch_size, input_size]
+ * If batch-major: [batch_size, max_time, input_size]
+ * where “max_time” is the number of timesteps (sequence length),
+ * “batch_size” corresponds to the batching dimension, and
+ * “input_size” is the size of the input.
+ * * 1: The input-to-input weights (\f$W_{xi}\f$). Optional.
+ * A 2-D tensor of shape [num_units, input_size], where “num_units”
+ * corresponds to the number of cell units.
+ * * 2: The input-to-forget weights (\f$W_{xf}\f$).
+ * A 2-D tensor of shape [num_units, input_size].
+ * * 3: The input-to-cell weights (\f$W_{xc}\f$).
+ * A 2-D tensor of shape [num_units, input_size].
+ * * 4: The input-to-output weights (\f$W_{xo}\f$).
+ * A 2-D tensor of shape [num_units, input_size].
+ * * 5: The recurrent-to-input weights (\f$W_{hi}\f$). Optional.
+ * A 2-D tensor of shape [num_units, output_size], where “output_size”
+ * corresponds to either the number of cell units (i.e., “num_units”),
+ * or the second dimension of the “projection_weights”, if defined.
+ * * 6: The recurrent-to-forget weights (\f$W_{hf}\f$).
+ * A 2-D tensor of shape [num_units, output_size].
+ * * 7: The recurrent-to-cell weights (\f$W_{hc}\f$).
+ * A 2-D tensor of shape [num_units, output_size].
+ * * 8: The recurrent-to-output weights (\f$W_{ho}\f$).
+ * A 2-D tensor of shape [num_units, output_size].
+ * * 9: The cell-to-input weights (\f$W_{ci}\f$). Optional.
+ * A 1-D tensor of shape [num_units].
+ * * 10:The cell-to-forget weights (\f$W_{cf}\f$). Optional.
+ * A 1-D tensor of shape [num_units].
+ * * 11:The cell-to-output weights (\f$W_{co}\f$). Optional.
+ * A 1-D tensor of shape [num_units].
+ * * 12:The input gate bias (\f$b_i\f$). Optional.
+ * A 1-D tensor of shape [num_units].
+ * * 13:The forget gate bias (\f$b_f\f$).
+ * A 1-D tensor of shape [num_units].
+ * * 14:The cell bias (\f$b_c\f$).
+ * A 1-D tensor of shape [num_units].
+ * * 15:The output gate bias (\f$b_o\f$).
+ * A 1-D tensor of shape [num_units].
+ * * 16:The projection weights (\f$W_{proj}\f$). Optional.
+ * A 2-D tensor of shape [output_size, num_units].
+ * * 17:The projection bias (\f$b_{proj}\f$). Optional.
+ * A 1-D tensor of shape [output_size].
+ * * 18:The output state (in) (\f$h_{t-1}\f$).
+ * A 2-D tensor of shape [batch_size, output_size].
+ * * 19:The cell state (in) (\f$C_{t-1}\f$).
+ * A 2-D tensor of shape [batch_size, num_units].
+ * * 20:The activation function (\f$g\f$).
+ * A value indicating the activation function:
+ * <ul>
+ * <li>0: None;
+ * <li>1: Relu;
+ * <li>3: Relu6;
+ * <li>4: Tanh;
+ * <li>6: Sigmoid.
+ * </ul>
+ * * 21:The clipping threshold (\f$t_{cell}\f$) for the cell state, such
+ * that values are bound within [-cell_clip, cell_clip]. If set to 0.0
+ * then clipping is disabled.
+ * * 22:The clipping threshold (\f$t_{proj}\f$) for the output from the
+ * projection layer, such that values are bound within
+ * [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled.
+ * * 23:Time-major if true, batch-major if false.
+ * * 24:The input layer normalization weights. Optional.
+ * A 1-D tensor of shape [num_units]. Used to rescale normalized inputs
+ * to activation at input gate.
+ * * 25:The forget layer normalization weights. Optional.
+ * A 1-D tensor of shape [num_units]. Used to rescale normalized inputs
+ * to activation at forget gate.
+ * * 26:The cell layer normalization weights. Optional.
+ * A 1-D tensor of shape [num_units]. Used to rescale normalized inputs
+ * to activation at cell gate.
+ * * 27:The output layer normalization weights. Optional.
+ * A 1-D tensor of shape [num_units]. Used to rescale normalized inputs
+ * to activation at output gate.
+ *
+ * Outputs:
+ * * 0: The output (\f$o_t\f$).
+ * A 3-D tensor of shape:
+ * If time-major: [max_time, batch_size, output_size]
+ * If batch-major: [batch_size, max_time, output_size]
+ * * 1: A tensor of shape [batch_size, output_size] containing a hidden
+ * state from the last time step in the sequence. This output is
+ * optional and can be omitted. If this output is present then
+ * output #2 must be present as well.
+ * Available since HAL version 1.3.
+ * * 2: A tensor of shape [batch_size, cell_size] containing a cell state
+ * from the last time step in the sequence. This output is optional
+ * and can be omitted.
+ * Available since HAL version 1.3.
+ */
+ UNIDIRECTIONAL_SEQUENCE_LSTM = 92,
+ /**
+ * A recurrent neural network layer that applies a basic RNN cell to a
+ * sequence of inputs.
+ *
+ * This layer unrolls the input along the sequence dimension, and implements
+ * the following operation
+ * for each element in the sequence s = 1...sequence_length:
+ * outputs[s] = state = activation(inputs[s] * input_weights’ + state *
+ * recurrent_weights’ + bias)
+ *
+ * Where:
+ * * “input_weights” is a weight matrix that multiplies the inputs;
+ * * “recurrent_weights” is a weight matrix that multiplies the current
+ * “state” which itself is the output from the previous time step
+ * computation;
+ * * “bias” is a bias vector (added to each output vector in the batch);
+ * * “activation” is the function passed as the “fused_activation_function”
+ * argument (if not “NONE”).
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * The input tensors must all be the same type.
+ *
+ * Inputs:
+ * * 0: input.
+ * A 3-D tensor. The shape is defined by the input 6 (timeMajor). If
+ * it is set to 1, then the input has a shape [maxTime, batchSize,
+ * inputSize], otherwise the input has a shape [batchSize, maxTime,
+ * inputSize].
+ * * 1: weights.
+ * A 2-D tensor of shape [numUnits, inputSize].
+ * * 2: recurrent_weights.
+ * A 2-D tensor of shape [numUnits, numUnits].
+ * * 3: bias.
+ * A 1-D tensor of shape [numUnits].
+ * * 4: hidden state
+ * A 2-D tensor of shape [batchSize, numUnits]. Specifies a hidden
+ * state input for the first time step of the computation.
+ * * 5: fusedActivationFunction.
+ * A {@link FusedActivationFunc} value indicating the activation function. If
+ * “NONE” is specified then it results in a linear activation.
+ * * 6: timeMajor
+ * An {@link OperandType::INT32} scalar specifying the shape format
+ * of input and output tensors. Must be set to either 0 or 1.
+ * Outputs:
+ * * 0: output.
+ * A 3-D tensor. The shape is defined by the input 6 (timeMajor). If
+ * it is set to 1, then the output has a shape [maxTime, batchSize,
+ * numUnits], otherwise the output has a shape [batchSize, maxTime,
+ * numUnits].
+ * * 1: A tensor of shape [batchSize, numUnits] containing hidden state
+ * from the last time step in the sequence. This output is optional
+ * and can be omitted.
+ * Available since HAL version 1.3.
+ */
+ UNIDIRECTIONAL_SEQUENCE_RNN = 93,
+ /**
+ * Resizes images to given size using the nearest neighbor interpretation.
+ *
+ * Resized images must be distorted if their output aspect ratio is not the
+ * same as input aspect ratio. The corner pixels of output may not be the
+ * same as corner pixels of input.
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ *
+ * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
+ * With the default data layout NHWC, the data is stored in the order of:
+ * [batch, height, width, channels]. Alternatively, the data layout could
+ * be NCHW, the data storage order of: [batch, channels, height, width].
+ *
+ * Both resizing by shape and resizing by scale are supported.
+ *
+ * Inputs (resizing by shape):
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
+ * the input. Zero batches is supported for this tensor.
+ * * 1: An {@link OperandType::INT32} scalar, specifying the output
+ * width of the output tensor.
+ * * 2: An {@link OperandType::INT32} scalar, specifying the output
+ * height of the output tensor.
+ * * 3: An {@link OperandType::BOOL} scalar, default to false.
+ * Set to true to specify NCHW data layout for input0 and output0.
+ * * 4: Align corners. An optional {@link OperandType::BOOL}
+ * scalar, default to false. If True, the centers of the 4 corner
+ * pixels of the input and output tensors are aligned, preserving the
+ * values at the corner pixels.
+ * Available since HAL version 1.3.
+ * * 5: Half pixel centers. An optional {@link OperandType::BOOL}
+ * scalar, default to false. If True, the pixel centers are assumed to
+ * be at (0.5, 0.5). This is the default behavior of image.resize in
+ * TF 2.0. If this parameter is True, then align_corners parameter
+ * must be False.
+ * Available since HAL version 1.3.
+ *
+ * Inputs (resizing by scale):
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
+ * the input. Zero batches is supported for this tensor.
+ * * 1: A scalar, specifying width_scale, the scaling factor of the width
+ * dimension from the input tensor to the output tensor. The output
+ * width is calculated as new_width = floor(width * width_scale).
+ * The scalar must be of {@link OperandType::FLOAT16} if input0 is
+ * of {@link OperandType::TENSOR_FLOAT16} and of
+ * {@link OperandType::FLOAT32} otherwise.
+ * * 2: A scalar, specifying height_scale, the scaling factor of the height
+ * dimension from the input tensor to the output tensor. The output
+ * height is calculated as new_height = floor(height * height_scale).
+ * The scalar must be of {@link OperandType::FLOAT16} if input0 is
+ * of {@link OperandType::TENSOR_FLOAT16} and of
+ * {@link OperandType::FLOAT32} otherwise.
+ * * 3: An {@link OperandType::BOOL} scalar, default to false.
+ * Set to true to specify NCHW data layout for input0 and output0.
+ * * 4: Align corners. An optional {@link OperandType::BOOL}
+ * scalar, default to false. If True, the centers of the 4 corner
+ * pixels of the input and output tensors are aligned, preserving the
+ * values at the corner pixels.
+ * Available since HAL version 1.3.
+ * * 5: Half pixel centers. An optional {@link OperandType::BOOL}
+ * scalar, default to false. If True, the pixel centers are assumed to
+ * be at (0.5, 0.5). This is the default behavior of image.resize in
+ * TF 2.0. If this parameter is True, then align_corners parameter
+ * must be False.
+ * Available since HAL version 1.3.
+ *
+ * Outputs:
+ * * 0: The output 4-D tensor, of shape
+ * [batches, new_height, new_width, depth].
+ * For a {@link OperandType::TENSOR_QUANT8_ASYMM} and
+ * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
+ * the scale and zeroPoint must be the same as input0.
+ */
+ RESIZE_NEAREST_NEIGHBOR = 94,
+ /**
+ * Quantized version of {@link OperationType::LSTM}.
+ *
+ * The input and the output use asymmetric quantized types, while the rest
+ * use symmetric ones.
+ *
+ * Inputs:
+ * * 0: The input to the LSTM cell.
+ * Type: {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED}
+ * Shape: [batchSize, inputSize]
+ * * 1: The input-to-input weights. Optional.
+ * Type: {@link OperandType::TENSOR_QUANT8_SYMM}
+ * Shape: [numUnits, inputSize]
+ * * 2: The input-to-forget weights.
+ * Type: {@link OperandType::TENSOR_QUANT8_SYMM}
+ * Shape: [numUnits, inputSize]
+ * * 3: The input-to-cell weights.
+ * Type: {@link OperandType::TENSOR_QUANT8_SYMM}
+ * Shape: [numUnits, inputSize]
+ * * 4: The input-to-output weights.
+ * Type: {@link OperandType::TENSOR_QUANT8_SYMM}
+ * Shape: [numUnits, inputSize]
+ * * 5: The recurrent-to-input weights. Optional.
+ * Type: {@link OperandType::TENSOR_QUANT8_SYMM}
+ * Shape: [numUnits, outputSize]
+ * * 6: The recurrent-to-forget weights.
+ * Type: {@link OperandType::TENSOR_QUANT8_SYMM}
+ * Shape: [numUnits, outputSize]
+ * * 7: The recurrent-to-cell weights.
+ * Type: {@link OperandType::TENSOR_QUANT8_SYMM}
+ * Shape: [numUnits, outputSize]
+ * * 8: The recurrent-to-output weights.
+ * Type: {@link OperandType::TENSOR_QUANT8_SYMM}
+ * Shape: [numUnits, outputSize]
+ * * 9: The cell-to-input weights (for peephole). Optional.
+ * Type: {@link OperandType::TENSOR_QUANT16_SYMM}
+ * Shape: [numUnits]
+ * * 10: The cell-to-forget weights (for peephole). Optional.
+ * Type: {@link OperandType::TENSOR_QUANT16_SYMM}
+ * Shape: [numUnits]
+ * * 11: The cell-to-output weights (for peephole). Optional.
+ * Type: {@link OperandType::TENSOR_QUANT16_SYMM}
+ * Shape: [numUnits]
+ * * 12: The input gate bias. Quantized with scale being the
+ * product of input and weights scales and zeroPoint equal to 0.
+ * Optional.
+ * Type: {@link OperandType::TENSOR_INT32}
+ * Shape: [numUnits]
+ * * 13: The forget gate bias. Quantized with scale being the
+ * product of input and weights scales and zeroPoint equal to 0.
+ * Type: {@link OperandType::TENSOR_INT32}
+ * Shape: [numUnits]
+ * * 14: The cell bias. Quantized with scale being the
+ * product of input and weights scales and zeroPoint equal to 0.
+ * Type: {@link OperandType::TENSOR_INT32}
+ * Shape: [numUnits]
+ * * 15: The output gate bias. Quantized with scale being the
+ * product of input and weights scales and zeroPoint equal to 0.
+ * Type: {@link OperandType::TENSOR_INT32}
+ * Shape: [numUnits]
+ * * 16: The projection weights. Optional.
+ * Type: {@link OperandType::TENSOR_QUANT8_SYMM}
+ * Shape: [outputSize, numUnits]
+ * * 17: The projection bias. Quantized with scale being the
+ * product of input and weights scales and zeroPoint equal to 0.
+ * Optional.
+ * Type: {@link OperandType::TENSOR_INT32}
+ * Shape: [outputSize]
+ * * 18: The output from the previous time step.
+ * Type: {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED}
+ * Shape: [batchSize, outputSize]
+ * * 19: The cell state from the previous time step.
+ * Type: {@link OperandType::TENSOR_QUANT16_SYMM}
+ * Shape: [batchSize, numUnits]
+ * * 20: The input layer normalization weights. Used to rescale
+ * normalized inputs to activation at input gate. Optional.
+ * Type: {@link OperandType::TENSOR_QUANT16_SYMM}
+ * Shape: [numUnits]
+ * * 21: The forget layer normalization weights. Used to
+ * rescale normalized inputs to activation at forget gate. Optional.
+ * Type: {@link OperandType::TENSOR_QUANT16_SYMM}
+ * Shape: [numUnits]
+ * * 22: The cell layer normalization weights. Used to rescale
+ * normalized inputs to activation at cell gate. Optional.
+ * Type: {@link OperandType::TENSOR_QUANT16_SYMM}
+ * Shape: [numUnits]
+ * * 23: The output layer normalization weights. Used to
+ * rescale normalized inputs to activation at output gate. Optional.
+ * Type: {@link OperandType::TENSOR_QUANT16_SYMM}
+ * Shape: [numUnits]
+ * * 24: The cell clip. If provided the cell state is clipped
+ * by this value prior to the cell output activation. Optional.
+ * Type: {@link OperandType::FLOAT32}.
+ * * 25: The projection clip. If provided and projection is enabled,
+ * this is used for clipping the projected values. Optional.
+ * Type: {@link OperandType::FLOAT32}.
+ * * 26: The scale of the intermediate result of matmul,
+ * i.e. input to layer normalization, at input gate.
+ * Type: {@link OperandType::FLOAT32}.
+ * * 27: The scale of the intermediate result of matmul,
+ * i.e. input to layer normalization, at forget gate.
+ * Type: {@link OperandType::FLOAT32}.
+ * * 28: The scale of the intermediate result of matmul,
+ * i.e. input to layer normalization, at cell gate.
+ * Type: {@link OperandType::FLOAT32}.
+ * * 29: The scale of the intermediate result of matmul,
+ * i.e. input to layer normalization, at output gate.
+ * Type: {@link OperandType::FLOAT32}.
+ * * 30: The zero point of the hidden state, i.e. input to
+ * projection.
+ * Type: {@link OperandType::INT32}.
+ * * 31: The scale of the hidden state, i.e. input to
+ * projection.
+ * Type: {@link OperandType::FLOAT32}.
+ *
+ * Outputs:
+ * * 0: The output state (out).
+ * Type: {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED}
+ * Shape: [batchSize, outputSize]
+ * * 1: The cell state (out).
+ * Type: {@link OperandType::TENSOR_QUANT16_SYMM}
+ * Shape: [batchSize, numUnits]
+ * * 2: The output. This is effectively the same as the current
+ * "output state (out)" value.
+ * Type: {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED}
+ * Shape: [batchSize, outputSize]
+ */
+ QUANTIZED_LSTM = 95,
+ /**
+ * Executes one of the two referenced subgraphs as determined by a boolean
+ * value.
+ *
+ * The inputs and outputs of the two referenced subgraphs must agree with the
+ * signature of this operation. That is, if the operation has (3 + n) inputs
+ * and m outputs, both subgraphs must have n inputs and m outputs with the same
+ * types, ranks, dimensions, scales,
+ * zeroPoints, and extraParams as the corresponding operation
+ * inputs and outputs.
+ * All of the operands mentioned must have fully specified dimensions.
+ *
+ * Inputs:
+ * * 0: A value of type {@link OperandType::TENSOR_BOOL8} and shape [1]
+ * that determines which of the two referenced subgraphs to execute.
+ * The operand must have fully specified dimensions.
+ * * 1: A {@link OperandType::SUBGRAPH} reference to the subgraph to be
+ * executed if the condition is true.
+ * * 2: A {@link OperandType::SUBGRAPH} reference to the subgraph to be
+ * executed if the condition is false.
+ * * 3 ~ (n + 2): Inputs to be passed to the subgraph selected for execution.
+ *
+ * Outputs:
+ * * 0 ~ (m - 1): Outputs produced by the selected subgraph.
+ */
+ IF = 96,
+ /**
+ * Executes the body subgraph until the condition subgraph outputs false.
+ *
+ * The inputs to this operation are the condition subgraph, the body subgraph,
+ * and operand values for the first iteration of the loop. The values are
+ * implicitly split into three groups of input-output, state-only, and
+ * input-only values, as described below.
+ *
+ * The outputs of this operation are the final values of input-output
+ * operands.
+ *
+ * Both the condition and body subgraph receive (m + k + n) inputs.
+ * * The first m (m >= 1) inputs are input-output operands. For the first
+ * iteration, these are initialized from the corresponding inputs of the
+ * WHILE operation. In subsequent iterations, their values come from the
+ * corresponding outputs of the body subgraph produced during the previous
+ * iteration.
+ * * The next k (k >= 0) inputs are state-only operands. They are similar to
+ * the input-output operands, except that their values are no longer
+ * available after the loop terminates.
+ * * The last n (n >= 0) inputs are input-only operands. Their values come
+ * from the corresponding inputs of the WHILE operation.
+ *
+ * The body subgraph produces (m + k) outputs.
+ * * The first m outputs are input-output operands. They become the outputs
+ * of the WHILE operation when a termination condition is reached.
+ * * The last k outputs are state-only operands. Their values are no longer
+ * available after the loop terminates.
+ *
+ * The numbers m, k, and n are inferred by the driver as follows:
+ * m = (WHILE operation output count)
+ * k = (body subgraph output count) - m
+ * n = (body subgraph input count) - m - k
+ *
+ * The pseudo-code below illustrates the flow of a WHILE operation with
+ * inputs condition, body, initial_input_output, initial_state, input_only
+ * (m = 1, k = 1, n = 1):
+ *
+ * input_output = initial_input_output
+ * state = initial_state
+ * while condition(input_output, state, input_only):
+ * input_output, state = body(input_output, state, input_only)
+ * return input_output
+ *
+ * Inputs:
+ * * 0: A {@link OperandType::SUBGRAPH} reference to the condition
+ * subgraph. The subgraph must have (m + k + n) inputs with
+ * the same types, ranks, dimensions,
+ * scales, zeroPoints, and extraParams as the
+ * corresponding inputs of the WHILE operation and exactly one output
+ * of {@link OperandType::TENSOR_BOOL8} and shape [1].
+ * All of the operands mentioned must have fully specified dimensions.
+ * * 1: A {@link OperandType::SUBGRAPH} reference to the body subgraph.
+ * The subgraph must have (m + k + n) inputs and (m + k) outputs with
+ * the same types, ranks, dimensions,
+ * scales, zeroPoints, and extraParams as the
+ * corresponding inputs and outputs of the WHILE operation.
+ * All of the operands mentioned must have fully specified dimensions.
+ * * (m inputs): Initial values for input-output operands.
+ * * (k inputs): Initial values for state-only operands.
+ * * (n inputs): Values for input-only operands.
+ *
+ * Outputs:
+ * * 0 ~ (m - 1): Outputs produced by the loop.
+ */
+ WHILE = 97,
+ /**
+ * Computes exponential linear activation on the input tensor element-wise.
+ *
+ * The output is calculated using the following formula:
+ *
+ * ELU(x) = max(0, x) + min(0, alpha * (exp(x) - 1))
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: from 1.
+ *
+ * Inputs:
+ * * 0: A tensor, specifying the input. May be zero-sized.
+ * * 1: A scalar, specifying the alpha parameter.
+ * For input tensor of {@link OperandType::TENSOR_FLOAT16},
+ * the alpha value must be of {@link OperandType::FLOAT16}.
+ * For input tensor of {@link OperandType::TENSOR_FLOAT32},
+ * the alpha value must be of {@link OperandType::FLOAT32}.
+ *
+ * Outputs:
+ * * 0: The output tensor of same shape and type as input0.
+ */
+ ELU = 98,
+ /**
+ * Computes hard-swish activation on the input tensor element-wise.
+ *
+ * Hard swish activation is introduced in
+ * https://arxiv.org/pdf/1905.02244.pdf
+ *
+ * The output is calculated using the following formula:
+ *
+ * h-swish(x) = x * max(0, min(6, (x + 3))) / 6
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED}
+ *
+ * Supported tensor rank: from 1.
+ *
+ * Inputs:
+ * * 0: A tensor, specifying the input. May be zero-sized.
+ *
+ * Outputs:
+ * * 0: The output tensor of same shape and type as input0.
+ * Scale and zero point of this tensor may be different from the input
+ * tensor's parameters.
+ */
+ HARD_SWISH = 99,
+ /**
+ * Creates a tensor filled with a scalar value.
+ *
+ * Supported output tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ *
+ * Supported tensor rank: from 1.
+ *
+ * Inputs:
+ * * 0: A 1-D tensor, specifying the desired output tensor shape.
+ * * 1: A scalar, specifying the value to fill the output tensors with.
+ * For output tensor of {@link OperandType::TENSOR_FLOAT16},
+ * the scalar must be of {@link OperandType::FLOAT16}.
+ * For output tensor of {@link OperandType::TENSOR_FLOAT32},
+ * the scalar must be of {@link OperandType::FLOAT32}.
+ * For output tensor of {@link OperandType::TENSOR_INT32},
+ * the scalar must be of {@link OperandType::INT32}.
+ *
+ * Outputs:
+ * * 0: The output tensor.
+ */
+ FILL = 100,
+ /**
+ * Returns the rank of a tensor.
+ *
+ * The rank of a tensor is the number of dimensions in it. Also known as
+ * "order", "degree", "ndims".
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT16_SYMM}
+ * * {@link OperandType::TENSOR_BOOL8}
+ * * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}
+ * * {@link OperandType::TENSOR_QUANT16_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_SYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED}
+ *
+ * Supported tensor rank: from 1.
+ *
+ * Inputs:
+ * * 0: The input tensor.
+ *
+ * Outputs:
+ * * 0: A scalar of {@link OperandType::INT32}, specifying the rank
+ * of the input tensor.
+ */
+ RANK = 101,
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/OutputShape.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/OutputShape.aidl
new file mode 100644
index 0000000..d206a25
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/OutputShape.aidl
@@ -0,0 +1,33 @@
+/*
+ * Copyright (C) 2020 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+
+package android.hardware.neuralnetworks;
+
+/**
+ * Describes the shape information of an output operand after execution.
+ */
+@VintfStability
+parcelable OutputShape {
+ /**
+ * Dimensions of the operand.
+ */
+ int[] dimensions;
+ /**
+ * Whether the provided buffer size is sufficient for the output.
+ */
+ boolean isSufficient;
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/PerformanceInfo.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/PerformanceInfo.aidl
new file mode 100644
index 0000000..6ee29c2
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/PerformanceInfo.aidl
@@ -0,0 +1,37 @@
+/*
+ * Copyright (C) 2020 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+
+package android.hardware.neuralnetworks;
+
+/**
+ * Performance information for the reference workload.
+ *
+ * Used by a driver to report its performance characteristics.
+ */
+@VintfStability
+parcelable PerformanceInfo {
+ /**
+ * Ratio of the time taken by the driver to execute the workload compared to the time the CPU
+ * would take for the same workload. A lower number is better.
+ */
+ float execTime;
+ /**
+ * Ratio of the energy used by the driver compared to what the CPU would use for doing the same
+ * workload. A lower number is better.
+ */
+ float powerUsage;
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/Priority.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/Priority.aidl
new file mode 100644
index 0000000..fe87598
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/Priority.aidl
@@ -0,0 +1,29 @@
+/*
+ * Copyright (C) 2020 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+
+package android.hardware.neuralnetworks;
+
+/**
+ * Priority given to a prepared model for execution.
+ */
+@VintfStability
+@Backing(type="int")
+enum Priority {
+ LOW,
+ MEDIUM,
+ HIGH,
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/Request.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/Request.aidl
new file mode 100644
index 0000000..396ff30
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/Request.aidl
@@ -0,0 +1,55 @@
+/*
+ * Copyright (C) 2020 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+
+package android.hardware.neuralnetworks;
+
+import android.hardware.neuralnetworks.RequestArgument;
+import android.hardware.neuralnetworks.RequestMemoryPool;
+
+/**
+ * Inputs to be sent to and outputs to be retrieved from a prepared model.
+ *
+ * A Request serves two primary tasks:
+ * 1) Provides the input and output data to be used when executing the model.
+ * 2) Specifies any updates to the input operand metadata that were left unspecified at model
+ * preparation time.
+ *
+ * An output must not overlap with any other output, with an input, or with an operand of lifetime
+ * CONSTANT_POOL.
+ */
+@VintfStability
+parcelable Request {
+ /**
+ * Input data and information to be used in the execution of a prepared model.
+ *
+ * The index of the input corresponds to the index in Model.main.inputIndexes.
+ * E.g., input[i] corresponds to Model.main.inputIndexes[i].
+ */
+ RequestArgument[] inputs;
+ /**
+ * Output data and information to be used in the execution of a prepared model.
+ *
+ * The index of the output corresponds to the index in Model.main.outputIndexes.
+ * E.g., output[i] corresponds to Model.main.outputIndexes[i].
+ */
+ RequestArgument[] outputs;
+ /**
+ * A collection of memory pools containing operand data for both the inputs and the outputs to a
+ * model.
+ */
+ RequestMemoryPool[] pools;
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/RequestArgument.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/RequestArgument.aidl
new file mode 100644
index 0000000..e615fa6
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/RequestArgument.aidl
@@ -0,0 +1,53 @@
+/*
+ * Copyright (C) 2020 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+
+package android.hardware.neuralnetworks;
+
+import android.hardware.neuralnetworks.DataLocation;
+
+/**
+ * Metadata information specifying the location of the input or output data and any updates to the
+ * input or output operand.
+ */
+@VintfStability
+parcelable RequestArgument {
+ /**
+ * If true, the argument does not have a value. This can be used for operations that take
+ * optional arguments. If true, the fields of location are set to 0 and the dimensions vector is
+ * left empty.
+ */
+ boolean hasNoValue;
+ /**
+ * The location within one of the memory pools passed in the Request.
+ */
+ DataLocation location;
+ /**
+ * Updated dimension information.
+ *
+ * If dimensions.size() > 0, dimension information was provided along with the argument. This
+ * can be the case for models that accept inputs of varying size. This can't change the rank,
+ * just the value of the dimensions that were unspecified in the model. If dimensions.size() >
+ * 0, then all dimensions must be specified here; and any dimension that was specified in the
+ * model must have the same value here.
+ *
+ * If the dimensions in the model are not fully specified, then they must be fully specified
+ * here, unless hasNoValue is set to true. If the dimensions in the model are fully specified,
+ * then either dimensions.size() may be 0, or the dimensions in the model must be identical to
+ * the dimensions here.
+ */
+ int[] dimensions;
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/RequestMemoryPool.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/RequestMemoryPool.aidl
new file mode 100644
index 0000000..166746d
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/RequestMemoryPool.aidl
@@ -0,0 +1,36 @@
+/*
+ * Copyright (C) 2020 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+
+package android.hardware.neuralnetworks;
+
+import android.hardware.neuralnetworks.Memory;
+
+/**
+ * A memory pool.
+ */
+@VintfStability
+union RequestMemoryPool {
+ /**
+ * Specifies a client-managed shared memory pool.
+ */
+ Memory pool;
+ /**
+ * Specifies a driver-managed buffer. It is the token returned from IDevice::allocate, and is
+ * specific to the IDevice object.
+ */
+ int token;
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/Subgraph.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/Subgraph.aidl
new file mode 100644
index 0000000..0a76285
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/Subgraph.aidl
@@ -0,0 +1,51 @@
+/*
+ * Copyright (C) 2020 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+
+package android.hardware.neuralnetworks;
+
+import android.hardware.neuralnetworks.Operand;
+import android.hardware.neuralnetworks.Operation;
+
+/**
+ * An excerpt of the execution graph.
+ */
+@VintfStability
+parcelable Subgraph {
+ /**
+ * All operands included in the subgraph.
+ */
+ Operand[] operands;
+ /**
+ * All operations included in the subgraph.
+ *
+ * The operations are sorted into execution order. Every operand with lifetime SUBGRAPH_OUTPUT
+ * or TEMPORARY_VARIABLE must be written before it is read.
+ */
+ Operation[] operations;
+ /**
+ * Input indexes of the subgraph. There must be at least one.
+ *
+ * Each value corresponds to the index of the operand in "operands".
+ */
+ int[] inputIndexes;
+ /**
+ * Output indexes of the subgraph. There must be at least one.
+ *
+ * Each value corresponds to the index of the operand in "operands".
+ */
+ int[] outputIndexes;
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/SymmPerChannelQuantParams.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/SymmPerChannelQuantParams.aidl
new file mode 100644
index 0000000..8ae41a4
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/SymmPerChannelQuantParams.aidl
@@ -0,0 +1,33 @@
+/*
+ * Copyright (C) 2020 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+
+package android.hardware.neuralnetworks;
+
+/**
+ * Parameters for TENSOR_QUANT8_SYMM_PER_CHANNEL operand.
+ */
+@VintfStability
+parcelable SymmPerChannelQuantParams {
+ /**
+ * Array of scaling values for each channel. Each value must be greater than zero.
+ */
+ float[] scales;
+ /**
+ * Index of the channel dimension
+ */
+ int channelDim;
+}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/Timing.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/Timing.aidl
new file mode 100644
index 0000000..b04f74e
--- /dev/null
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/Timing.aidl
@@ -0,0 +1,37 @@
+/*
+ * Copyright (C) 2020 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+
+package android.hardware.neuralnetworks;
+
+/**
+ * Timing information measured during execution. Each time is a duration from the beginning of some
+ * task to the end of that task, including time when that task is not active (for example, preempted
+ * by some other task, or waiting for some resource to become available).
+ *
+ * Times are measured in nanoseconds. When a time is not available, it must be reported as -1.
+ */
+@VintfStability
+parcelable Timing {
+ /**
+ * Execution time on device (not driver, which runs on host processor).
+ */
+ long timeOnDevice;
+ /**
+ * Execution time in driver (including time on device).
+ */
+ long timeInDriver;
+}