diff --git a/audio/2.0/config/audio_policy_configuration.xsd b/audio/2.0/config/audio_policy_configuration.xsd
index c94da80..eb59152 100644
--- a/audio/2.0/config/audio_policy_configuration.xsd
+++ b/audio/2.0/config/audio_policy_configuration.xsd
@@ -365,10 +365,10 @@
         </xs:restriction>
     </xs:simpleType>
     <xs:complexType name="profile">
-        <xs:attribute name="name" type="xs:token" use="required"/>
-        <xs:attribute name="format" type="audioFormat" use="required"/>
-        <xs:attribute name="samplingRates" type="samplingRates" use="required"/>
-        <xs:attribute name="channelMasks" type="channelMask" use="required"/>
+        <xs:attribute name="name" type="xs:token" use="optional"/>
+        <xs:attribute name="format" type="audioFormat" use="optional"/>
+        <xs:attribute name="samplingRates" type="samplingRates" use="optional"/>
+        <xs:attribute name="channelMasks" type="channelMask" use="optional"/>
     </xs:complexType>
     <xs:simpleType name="gainMode">
         <xs:restriction base="xs:string">
diff --git a/audio/2.0/vts/functional/ValidateAudioConfiguration.cpp b/audio/2.0/vts/functional/ValidateAudioConfiguration.cpp
index 5fc1b3d..ec3259a 100644
--- a/audio/2.0/vts/functional/ValidateAudioConfiguration.cpp
+++ b/audio/2.0/vts/functional/ValidateAudioConfiguration.cpp
@@ -24,14 +24,11 @@
     const char* possibleConfigLocations[] = {"/odm/etc", "/vendor/etc", "/system/etc"};
     const char* configSchemaPath = "/data/local/tmp/audio_policy_configuration.xsd";
 
-    bool found = false;
     for (std::string folder : possibleConfigLocations) {
         const auto configPath = folder + '/' + configName;
         if (access(configPath.c_str(), R_OK) == 0) {
-            ASSERT_FALSE(found) << "Multiple " << configName << " found in "
-                                << ::testing::PrintToString(possibleConfigLocations);
-            found = true;
             ASSERT_VALID_XML(configPath.c_str(), configSchemaPath);
+            return; // The framework does not read past the first config file found
         }
     }
 }
diff --git a/automotive/vehicle/2.0/default/impl/vhal_v2_0/EmulatedVehicleHal.cpp b/automotive/vehicle/2.0/default/impl/vhal_v2_0/EmulatedVehicleHal.cpp
index 385f03d..6bc0522 100644
--- a/automotive/vehicle/2.0/default/impl/vhal_v2_0/EmulatedVehicleHal.cpp
+++ b/automotive/vehicle/2.0/default/impl/vhal_v2_0/EmulatedVehicleHal.cpp
@@ -162,12 +162,29 @@
     return StatusCode::OK;
 }
 
+static bool isDiagnosticProperty(VehiclePropConfig propConfig) {
+    switch (propConfig.prop) {
+        case OBD2_LIVE_FRAME:
+        case OBD2_FREEZE_FRAME:
+        case OBD2_FREEZE_FRAME_CLEAR:
+        case OBD2_FREEZE_FRAME_INFO:
+            return true;
+    }
+    return false;
+}
+
 // Parse supported properties list and generate vector of property values to hold current values.
 void EmulatedVehicleHal::onCreate() {
     for (auto& it : kVehicleProperties) {
         VehiclePropConfig cfg = it.config;
         int32_t supportedAreas = cfg.supportedAreas;
 
+        if (isDiagnosticProperty(cfg)) {
+            // do not write an initial empty value for the diagnostic properties
+            // as we will initialize those separately.
+            continue;
+        }
+
         //  A global property will have supportedAreas = 0
         if (isGlobalProp(cfg.prop)) {
             supportedAreas = 0;
diff --git a/automotive/vehicle/2.0/types.hal b/automotive/vehicle/2.0/types.hal
index 12b92dd..7c08b4a 100644
--- a/automotive/vehicle/2.0/types.hal
+++ b/automotive/vehicle/2.0/types.hal
@@ -785,7 +785,8 @@
      * requested by Android side. The focus has both per stream characteristics
      * and global characteristics.
      *
-     * Focus request (get of this property) must take the following form:
+     * Focus request (get of this property) must take the following form with indices defined
+     * by VehicleAudioFocusIndex:
      *   int32Values[0]: VehicleAudioFocusRequest type
      *   int32Values[1]: bit flags of streams requested by this focus request.
      *                   There can be up to 32 streams.
@@ -803,7 +804,7 @@
      *                   REQUEST_RELEASE). In that case, audio module must
      *                   maintain mute state until user's explicit action to
      *                   play some media.
-     *   int32Values[3]: Currently active audio contexts. Use combination of
+     *   int32Values[3]: Audio contexts wishing to be active. Use combination of
      *                   flags from VehicleAudioContextFlag.
      *                   This can be used as a hint to adjust audio policy or
      *                   other policy decision.
@@ -818,7 +819,7 @@
      * request.
      *
      * Focus response (set and subscription callback for this property) must
-     * take the following form:
+     * take the following form with indices defined by VehicleAudioFocusIndex:
      *   int32Values[0]: VehicleAudioFocusState type
      *   int32Values[1]: bit flags of streams allowed.
      *   int32Values[2]: External focus state: bit flags of currently active
@@ -847,9 +848,10 @@
      *                       side is playing something permanent.
      *                   LOSS_TRANSIENT: must always be
      *                       VehicleAudioExtFocusFlag#PERMANENT_FLAG
-     *   int32Values[3]: context requested by android side when responding to
-     *                   focus request. When car side is taking focus away,
-     *                   this must be zero.
+     *   int32Values[3]: Audio context(s) allowed to be active. When responding positively to a
+     *                   focus request from Android, the request's original context must be
+     *                   repeated here. When taking focus away, or denying a request, the
+     *                   rejected or stopped context would have its corresponding bit cleared.
      *
      * A focus response must be sent per each focus request even if there is
      * no change in focus state. This can happen in case like focus request
@@ -890,8 +892,8 @@
      *
      * VehiclePropConfig
      *   configArray[0] : bit flags of all supported audio contexts from
-     *                    VehicleAudioContextFlag. If this is 0, audio volume is
-     *                    controlled per physical stream.
+     *                    VehicleAudioContextFlag. If this is 0, audio volume
+     *                    is controlled per physical stream.
      *   configArray[1] : flags defined in VehicleAudioVolumeCapabilityFlag to
      *                    represent audio module's capability.
      *   configArray[2..3] : reserved
@@ -901,7 +903,7 @@
      *                         indicates mute state.
      *
      * Data type looks like:
-     *   int32Values[0] : stream context as defined in VehicleAudioContextFlag.
+     *   int32Values[0] : audio context as defined in VehicleAudioContextFlag.
      *                    If only physical stream is supported
      *                    (configArray[0] == 0), this must represent physical
      *                    stream number.
@@ -909,8 +911,8 @@
      *                    defined in the config.
      *   int32Values[2] : One of VehicleAudioVolumeState.
      *
-     * This property requires per stream based get. HAL implementation must
-     * check stream number in get call to return the right volume.
+     * HAL implementations must check the incoming value of audio context
+     * field in get call to return the right volume.
      *
      * @change_mode VehiclePropertyChangeMode:ON_CHANGE
      * @access VehiclePropertyAccess:READ_WRITE
@@ -949,18 +951,18 @@
      *                    to represent audio module's capability.
      *
      * Data type looks like:
-     *   int32Values[0] : stream context as defined in VehicleAudioContextFlag.
+     *   int32Values[0] : audio context as defined in VehicleAudioContextFlag.
      *                    If only physical stream is supported
      *                    (configArray[0] == 0), this must represent physical
      *                    stream number.
      *   int32Values[1] : maximum volume set to the stream. If there is no
-     *                    restriction, this value must be bigger than
+     *                    restriction, this value must be equal to
      *                    AUDIO_VOLUME's max value.
      *
      * If car does not support this feature, this property must not be
      * populated by HAL.
-     * This property requires per stream based get. HAL implementation must
-     * check stream number in get call to return the right volume.
+     * HAL implementations must check the incoming value of audio context
+     * field in get call to return the right volume.
      *
      * @change_mode VehiclePropertyChangeMode:ON_CHANGE
      * @access VehiclePropertyAccess:READ_WRITE
@@ -974,10 +976,8 @@
 
     /**
      * Property to share audio routing policy of android side. This property is
-     * set at the beginning to pass audio policy in android side down to
+     * set at startup to pass audio policy in android side down to
      * vehicle HAL and car audio module.
-     * This can be used as a hint to adjust audio policy or other policy
-     * decision.
      *
      *   int32Values[0] : audio stream where the audio for the application
      *                    context must be routed by default. Note that this is
@@ -1001,11 +1001,11 @@
         | VehicleArea:GLOBAL),
 
     /**
-     * Property to return audio H/W variant type used in this car. This allows
-     * android side to support different audio policy based on H/W variant used.
-     * Note that other components like CarService may need overlay update to
-     * support additional variants. If this property does not
-     * exist, default audio policy must be used.
+     * Property to return audio H/W variant type used in this car. This is a
+     * zero based index into the set of audio routing policies defined in
+     * R.array.audioRoutingPolicy on CarService, which  may be overlaid to
+     * support multiple variants. If this property does not exist, the default
+     * audio policy must be used.
      *
      * @change_mode VehiclePropertyChangeMode:STATIC
      * @access VehiclePropertyAccess:READ
@@ -2224,6 +2224,9 @@
 
     /** Ext source is played. This is for tagging generic ext sources. */
     EXT_SOURCE_FLAG = 0x1000,
+
+    /** The phone ring tone is played */
+    RINGTONE_FLAG = 0x2000
 };
 
 /**
@@ -2232,23 +2235,23 @@
  */
 enum VehicleAudioVolumeCapabilityFlag : int32_t {
     /**
-     * External audio module or vehicle hal has persistent storage
-     * to keep the volume level. This must be set only when per context
-     * volume level is supported. When this is set, audio volume level per
-     * each context will be retrieved from the property when system starts up.
+     * External audio module or vehicle hal has persistent storage to keep the
+     * volume level. When this is set, the audio volume level for each context
+     * will be retrieved from the property when the system starts up.
      * And external audio module is also expected to adjust volume automatically
      * whenever there is an audio context change.
      * When this flag is not set, android side will assume that there is no
-     * persistent storage and stored value in android side will be used to
-     * initialize the volume level. And android side will set volume level
-     * of each physical streams whenever there is an audio context change.
+     * persistent storage and the value stored in the android side will be used to
+     * initialize the volume level, and android side will set volume level
+     * of each physical stream whenever there is an audio context change.
      */
     PERSISTENT_STORAGE = 0x1,
 
     /**
+     * [DEPRECATED]
      * When this flag is set, the H/W can support only single master volume for
-     * all streams.
-     * There is no way to set volume level differently per each stream or context.
+     * all streams. There is no way to set volume level differently for each stream
+     * or context.
      */
     MASTER_VOLUME_ONLY = 0x2,
 };
@@ -2264,16 +2267,16 @@
      * VehicleProperty#AUDIO_VOLUME_LIMIT and user's request to increase volume
      * further is not allowed.
      */
-    LIMIT_REACHED = 1,
+    STATE_LIMIT_REACHED = 1,
 };
 
 /**
  * Index in int32Values for VehicleProperty#AUDIO_VOLUME property.
  */
 enum VehicleAudioVolumeIndex : int32_t {
-    INDEX_STREAM = 0,
-    INDEX_VOLUME = 1,
-    INDEX_STATE = 2,
+    STREAM = 0,
+    VOLUME = 1,
+    STATE = 2,
 };
 
 /**
diff --git a/bluetooth/1.0/vts/functional/VtsHalBluetoothV1_0TargetTest.cpp b/bluetooth/1.0/vts/functional/VtsHalBluetoothV1_0TargetTest.cpp
index fb53366..6c9aa18 100644
--- a/bluetooth/1.0/vts/functional/VtsHalBluetoothV1_0TargetTest.cpp
+++ b/bluetooth/1.0/vts/functional/VtsHalBluetoothV1_0TargetTest.cpp
@@ -464,8 +464,9 @@
 
 // Return the number of completed packets reported by the controller.
 int BluetoothHidlTest::wait_for_completed_packets_event(uint16_t handle) {
-    EXPECT_TRUE(bluetooth_cb->WaitForCallback(kCallbackNameHciEventReceived)
-                    .no_timeout);
+    if (!bluetooth_cb->WaitForCallback(kCallbackNameHciEventReceived).no_timeout) {
+        ALOGW("%s: WaitForCallback timed out.", __func__);
+    }
     int packets_processed = 0;
     while (event_queue.size() > 0) {
         hidl_vec<uint8_t> event = event_queue.front();
@@ -604,20 +605,24 @@
 
   // This should work, but breaks on some current platforms.  Figure out how to
   // grandfather older devices but test new ones.
-  int sco_packets_sent = 0;
   if (0 && sco_connection_handles.size() > 0) {
     sendAndCheckSCO(1, max_sco_data_packet_length, sco_connection_handles[0]);
-    sco_packets_sent = 1;
-    EXPECT_EQ(sco_packets_sent,
-              wait_for_completed_packets_event(sco_connection_handles[0]));
+    int sco_packets_sent = 1;
+    int completed_packets = wait_for_completed_packets_event(sco_connection_handles[0]);
+    if (sco_packets_sent != completed_packets) {
+        ALOGW("%s: packets_sent (%d) != completed_packets (%d)", __func__, sco_packets_sent,
+              completed_packets);
+    }
   }
 
-  int acl_packets_sent = 0;
   if (acl_connection_handles.size() > 0) {
     sendAndCheckACL(1, max_acl_data_packet_length, acl_connection_handles[0]);
-    acl_packets_sent = 1;
-    EXPECT_EQ(acl_packets_sent,
-              wait_for_completed_packets_event(acl_connection_handles[0]));
+    int acl_packets_sent = 1;
+    int completed_packets = wait_for_completed_packets_event(acl_connection_handles[0]);
+    if (acl_packets_sent != completed_packets) {
+        ALOGW("%s: packets_sent (%d) != completed_packets (%d)", __func__, acl_packets_sent,
+              completed_packets);
+    }
   }
 }
 
@@ -633,22 +638,26 @@
 
   // This should work, but breaks on some current platforms.  Figure out how to
   // grandfather older devices but test new ones.
-  int sco_packets_sent = 0;
   if (0 && sco_connection_handles.size() > 0) {
     sendAndCheckSCO(NUM_SCO_PACKETS_BANDWIDTH, max_sco_data_packet_length,
                     sco_connection_handles[0]);
-    sco_packets_sent = NUM_SCO_PACKETS_BANDWIDTH;
-    EXPECT_EQ(sco_packets_sent,
-              wait_for_completed_packets_event(sco_connection_handles[0]));
+    int sco_packets_sent = NUM_SCO_PACKETS_BANDWIDTH;
+    int completed_packets = wait_for_completed_packets_event(sco_connection_handles[0]);
+    if (sco_packets_sent != completed_packets) {
+        ALOGW("%s: packets_sent (%d) != completed_packets (%d)", __func__, sco_packets_sent,
+              completed_packets);
+    }
   }
 
-  int acl_packets_sent = 0;
   if (acl_connection_handles.size() > 0) {
     sendAndCheckACL(NUM_ACL_PACKETS_BANDWIDTH, max_acl_data_packet_length,
                     acl_connection_handles[0]);
-    acl_packets_sent = NUM_ACL_PACKETS_BANDWIDTH;
-    EXPECT_EQ(acl_packets_sent,
-              wait_for_completed_packets_event(acl_connection_handles[0]));
+    int acl_packets_sent = NUM_ACL_PACKETS_BANDWIDTH;
+    int completed_packets = wait_for_completed_packets_event(acl_connection_handles[0]);
+    if (acl_packets_sent != completed_packets) {
+        ALOGW("%s: packets_sent (%d) != completed_packets (%d)", __func__, acl_packets_sent,
+              completed_packets);
+    }
   }
 }
 
diff --git a/broadcastradio/1.1/default/BroadcastRadio.cpp b/broadcastradio/1.1/default/BroadcastRadio.cpp
index 38b4b99..1bcfd82 100644
--- a/broadcastradio/1.1/default/BroadcastRadio.cpp
+++ b/broadcastradio/1.1/default/BroadcastRadio.cpp
@@ -64,7 +64,7 @@
             }),
             AmFmBandConfig({
                 Band::FM_HD,
-                87900,   // lowerLimit
+                87700,   // lowerLimit
                 107900,  // upperLimit
                 {200},   // spacings
             }),
diff --git a/camera/device/1.0/default/CameraDevice.cpp b/camera/device/1.0/default/CameraDevice.cpp
index c53c0d8..a03bbc8 100644
--- a/camera/device/1.0/default/CameraDevice.cpp
+++ b/camera/device/1.0/default/CameraDevice.cpp
@@ -377,10 +377,14 @@
     hidl_handle hidlHandle = mem->mHidlHandle;
     MemoryId id = object->mDeviceCallback->registerMemory(hidlHandle, buf_size, num_bufs);
     mem->handle.mId = id;
-    if (object->mMemoryMap.count(id) != 0) {
-        ALOGE("%s: duplicate MemoryId %d returned by client!", __FUNCTION__, id);
+
+    {
+        Mutex::Autolock _l(object->mMemoryMapLock);
+        if (object->mMemoryMap.count(id) != 0) {
+            ALOGE("%s: duplicate MemoryId %d returned by client!", __FUNCTION__, id);
+        }
+        object->mMemoryMap[id] = mem;
     }
-    object->mMemoryMap[id] = mem;
     mem->handle.mDevice = object;
     return &mem->handle;
 }
@@ -398,7 +402,10 @@
         ALOGE("%s: camera HAL return memory while camera is not opened!", __FUNCTION__);
     }
     device->mDeviceCallback->unregisterMemory(mem->handle.mId);
-    device->mMemoryMap.erase(mem->handle.mId);
+    {
+        Mutex::Autolock _l(device->mMemoryMapLock);
+        device->mMemoryMap.erase(mem->handle.mId);
+    }
     mem->decStrong(mem);
 }
 
@@ -826,7 +833,16 @@
         return;
     }
     if (mDevice->ops->release_recording_frame) {
-        CameraHeapMemory* camMemory = mMemoryMap.at(memId);
+        CameraHeapMemory* camMemory;
+        {
+            Mutex::Autolock _l(mMemoryMapLock);
+            auto it = mMemoryMap.find(memId);
+            if (it == mMemoryMap.end() || it->second == nullptr) {
+                ALOGE("%s unknown memoryId %d", __FUNCTION__, memId);
+                return;
+            }
+            camMemory = it->second;
+        }
         if (bufferIndex >= camMemory->mNumBufs) {
             ALOGE("%s: bufferIndex %d exceeds number of buffers %d",
                     __FUNCTION__, bufferIndex, camMemory->mNumBufs);
diff --git a/camera/device/1.0/default/CameraDevice_1_0.h b/camera/device/1.0/default/CameraDevice_1_0.h
index c078596..2c980f0 100644
--- a/camera/device/1.0/default/CameraDevice_1_0.h
+++ b/camera/device/1.0/default/CameraDevice_1_0.h
@@ -165,6 +165,8 @@
 
     sp<ICameraDeviceCallback> mDeviceCallback = nullptr;
 
+    mutable Mutex mMemoryMapLock; // gating access to mMemoryMap
+                                  // must not hold mLock after this lock is acquired
     std::unordered_map<MemoryId, CameraHeapMemory*> mMemoryMap;
 
     bool mMetadataMode = false;
diff --git a/compatibility_matrix.26.xml b/compatibility_matrix.26.xml
index c9b68a8..5c81f26 100644
--- a/compatibility_matrix.26.xml
+++ b/compatibility_matrix.26.xml
@@ -57,7 +57,7 @@
     </hal>
     <hal format="hidl" optional="true">
         <name>android.hardware.broadcastradio</name>
-        <version>1.0</version>
+        <version>1.0-1</version>
         <interface>
             <name>IBroadcastRadioFactory</name>
             <instance>default</instance>
@@ -71,6 +71,14 @@
             <instance>legacy/0</instance>
         </interface>
     </hal>
+    <hal format="hidl" optional="true">
+        <name>android.hardware.cas</name>
+        <version>1.0</version>
+        <interface>
+            <name>IMediaCasService</name>
+            <instance>default</instance>
+        </interface>
+    </hal>
     <hal format="hidl" optional="false">
         <name>android.hardware.configstore</name>
         <version>1.0</version>
@@ -204,16 +212,24 @@
         </interface>
     </hal>
     <hal format="hidl" optional="true">
-        <name>android.hardware.power</name>
+        <name>android.hardware.oemlock</name>
         <version>1.0</version>
         <interface>
+            <name>IOemLock</name>
+            <instance>default</instance>
+        </interface>
+    </hal>
+    <hal format="hidl" optional="true">
+        <name>android.hardware.power</name>
+        <version>1.0-1</version>
+        <interface>
             <name>IPower</name>
             <instance>default</instance>
         </interface>
     </hal>
     <hal format="hidl" optional="true">
         <name>android.hardware.radio</name>
-        <version>1.0</version>
+        <version>1.0-1</version>
         <interface>
             <name>IRadio</name>
             <instance>slot1</instance>
@@ -256,9 +272,25 @@
         </interface>
     </hal>
     <hal format="hidl" optional="true">
-        <name>android.hardware.thermal</name>
+        <name>android.hardware.tetheroffload.config</name>
         <version>1.0</version>
         <interface>
+            <name>IOffloadConfig</name>
+            <instance>default</instance>
+        </interface>
+    </hal>
+    <hal format="hidl" optional="true">
+        <name>android.hardware.tetheroffload.control</name>
+        <version>1.0</version>
+        <interface>
+            <name>IOffloadControl</name>
+            <instance>default</instance>
+        </interface>
+    </hal>
+    <hal format="hidl" optional="true">
+        <name>android.hardware.thermal</name>
+        <version>1.0-1</version>
+        <interface>
             <name>IThermal</name>
             <instance>default</instance>
         </interface>
@@ -281,7 +313,7 @@
     </hal>
     <hal format="hidl" optional="true">
         <name>android.hardware.usb</name>
-        <version>1.0</version>
+        <version>1.0-1</version>
         <interface>
             <name>IUsb</name>
             <instance>default</instance>
@@ -289,7 +321,7 @@
     </hal>
     <hal format="hidl" optional="true">
         <name>android.hardware.vibrator</name>
-        <version>1.0</version>
+        <version>1.0-1</version>
         <interface>
             <name>IVibrator</name>
             <instance>default</instance>
@@ -304,14 +336,30 @@
         </interface>
     </hal>
     <hal format="hidl" optional="true">
-        <name>android.hardware.wifi</name>
+        <name>android.hardware.weaver</name>
         <version>1.0</version>
         <interface>
+            <name>IWeaver</name>
+            <instance>default</instance>
+        </interface>
+    </hal>
+    <hal format="hidl" optional="true">
+        <name>android.hardware.wifi</name>
+        <version>1.0-1</version>
+        <interface>
             <name>IWifi</name>
             <instance>default</instance>
         </interface>
     </hal>
     <hal format="hidl" optional="true">
+        <name>android.hardware.wifi.offload</name>
+        <version>1.0</version>
+        <interface>
+            <name>IOffload</name>
+            <instance>default</instance>
+        </interface>
+    </hal>
+    <hal format="hidl" optional="true">
         <name>android.hardware.wifi.supplicant</name>
         <version>1.0</version>
         <interface>
diff --git a/compatibility_matrix.27.xml b/compatibility_matrix.27.xml
new file mode 100644
index 0000000..5c81f26
--- /dev/null
+++ b/compatibility_matrix.27.xml
@@ -0,0 +1,370 @@
+<compatibility-matrix version="1.0" type="framework">
+    <hal format="hidl" optional="false">
+        <name>android.hardware.audio</name>
+        <version>2.0</version>
+        <interface>
+            <name>IDevicesFactory</name>
+            <instance>default</instance>
+        </interface>
+    </hal>
+    <hal format="hidl" optional="false">
+        <name>android.hardware.audio.effect</name>
+        <version>2.0</version>
+        <interface>
+            <name>IEffectsFactory</name>
+            <instance>default</instance>
+        </interface>
+    </hal>
+    <hal format="hidl" optional="true">
+        <name>android.hardware.automotive.evs</name>
+        <version>1.0</version>
+        <interface>
+            <name>IEvsEnumerator</name>
+            <instance>default</instance>
+        </interface>
+    </hal>
+    <hal format="hidl" optional="true">
+        <name>android.hardware.automotive.vehicle</name>
+        <version>2.0</version>
+        <interface>
+            <name>IVehicle</name>
+            <instance>default</instance>
+        </interface>
+    </hal>
+    <hal format="hidl" optional="true">
+        <name>android.hardware.biometrics.fingerprint</name>
+        <version>2.1</version>
+        <interface>
+            <name>IBiometricsFingerprint</name>
+            <instance>default</instance>
+        </interface>
+    </hal>
+    <hal format="hidl" optional="true">
+        <name>android.hardware.bluetooth</name>
+        <version>1.0</version>
+        <interface>
+            <name>IBluetoothHci</name>
+            <instance>default</instance>
+        </interface>
+    </hal>
+    <hal format="hidl" optional="true">
+        <name>android.hardware.boot</name>
+        <version>1.0</version>
+        <interface>
+            <name>IBootControl</name>
+            <instance>default</instance>
+        </interface>
+    </hal>
+    <hal format="hidl" optional="true">
+        <name>android.hardware.broadcastradio</name>
+        <version>1.0-1</version>
+        <interface>
+            <name>IBroadcastRadioFactory</name>
+            <instance>default</instance>
+        </interface>
+    </hal>
+    <hal format="hidl" optional="true">
+        <name>android.hardware.camera.provider</name>
+        <version>2.4</version>
+        <interface>
+            <name>ICameraProvider</name>
+            <instance>legacy/0</instance>
+        </interface>
+    </hal>
+    <hal format="hidl" optional="true">
+        <name>android.hardware.cas</name>
+        <version>1.0</version>
+        <interface>
+            <name>IMediaCasService</name>
+            <instance>default</instance>
+        </interface>
+    </hal>
+    <hal format="hidl" optional="false">
+        <name>android.hardware.configstore</name>
+        <version>1.0</version>
+        <interface>
+            <name>ISurfaceFlingerConfigs</name>
+            <instance>default</instance>
+        </interface>
+    </hal>
+    <hal format="hidl" optional="true">
+        <name>android.hardware.contexthub</name>
+        <version>1.0</version>
+        <interface>
+            <name>IContexthub</name>
+            <instance>default</instance>
+        </interface>
+    </hal>
+    <hal format="hidl" optional="false">
+        <name>android.hardware.drm</name>
+        <version>1.0</version>
+        <interface>
+            <name>ICryptoFactory</name>
+            <instance>default</instance>
+        </interface>
+        <interface>
+            <name>IDrmFactory</name>
+            <instance>default</instance>
+        </interface>
+    </hal>
+    <hal format="hidl" optional="true">
+        <name>android.hardware.dumpstate</name>
+        <version>1.0</version>
+        <interface>
+            <name>IDumpstateDevice</name>
+            <instance>default</instance>
+        </interface>
+    </hal>
+    <hal format="hidl" optional="false">
+        <name>android.hardware.gatekeeper</name>
+        <version>1.0</version>
+        <interface>
+            <name>IGatekeeper</name>
+            <instance>default</instance>
+        </interface>
+    </hal>
+    <hal format="hidl" optional="true">
+        <name>android.hardware.gnss</name>
+        <version>1.0</version>
+        <interface>
+            <name>IGnss</name>
+            <instance>default</instance>
+        </interface>
+    </hal>
+    <hal format="hidl" optional="false">
+        <name>android.hardware.graphics.allocator</name>
+        <version>2.0</version>
+        <interface>
+            <name>IAllocator</name>
+            <instance>default</instance>
+        </interface>
+    </hal>
+    <hal format="hidl" optional="false">
+        <name>android.hardware.graphics.composer</name>
+        <version>2.1</version>
+        <interface>
+            <name>IComposer</name>
+            <instance>default</instance>
+        </interface>
+    </hal>
+    <hal format="hidl" optional="false">
+        <name>android.hardware.graphics.mapper</name>
+        <version>2.0</version>
+        <interface>
+            <name>IMapper</name>
+            <instance>default</instance>
+        </interface>
+    </hal>
+    <hal format="hidl" optional="true">
+        <name>android.hardware.health</name>
+        <version>1.0</version>
+        <interface>
+            <name>IHealth</name>
+            <instance>default</instance>
+        </interface>
+    </hal>
+    <hal format="hidl" optional="true">
+        <name>android.hardware.ir</name>
+        <version>1.0</version>
+    </hal>
+    <hal format="hidl" optional="false">
+        <name>android.hardware.keymaster</name>
+        <version>3.0</version>
+        <interface>
+            <name>IKeymasterDevice</name>
+            <instance>default</instance>
+        </interface>
+    </hal>
+    <hal format="hidl" optional="true">
+        <name>android.hardware.light</name>
+        <version>2.0</version>
+        <interface>
+            <name>ILight</name>
+            <instance>default</instance>
+        </interface>
+    </hal>
+    <hal format="hidl" optional="false">
+        <name>android.hardware.media.omx</name>
+        <version>1.0</version>
+        <interface>
+            <name>IOmx</name>
+            <instance>default</instance>
+        </interface>
+        <interface>
+            <name>IOmxStore</name>
+            <instance>default</instance>
+        </interface>
+    </hal>
+    <hal format="hidl" optional="true">
+        <name>android.hardware.memtrack</name>
+        <version>1.0</version>
+        <interface>
+            <name>IMemtrack</name>
+            <instance>default</instance>
+        </interface>
+    </hal>
+    <hal format="hidl" optional="true">
+        <name>android.hardware.nfc</name>
+        <version>1.0</version>
+        <interface>
+            <name>INfc</name>
+            <instance>default</instance>
+        </interface>
+    </hal>
+    <hal format="hidl" optional="true">
+        <name>android.hardware.oemlock</name>
+        <version>1.0</version>
+        <interface>
+            <name>IOemLock</name>
+            <instance>default</instance>
+        </interface>
+    </hal>
+    <hal format="hidl" optional="true">
+        <name>android.hardware.power</name>
+        <version>1.0-1</version>
+        <interface>
+            <name>IPower</name>
+            <instance>default</instance>
+        </interface>
+    </hal>
+    <hal format="hidl" optional="true">
+        <name>android.hardware.radio</name>
+        <version>1.0-1</version>
+        <interface>
+            <name>IRadio</name>
+            <instance>slot1</instance>
+        </interface>
+        <interface>
+            <name>ISap</name>
+            <instance>slot1</instance>
+        </interface>
+    </hal>
+    <hal format="hidl" optional="true">
+        <name>android.hardware.radio.deprecated</name>
+        <version>1.0</version>
+        <interface>
+            <name>IOemHook</name>
+            <instance>slot1</instance>
+        </interface>
+    </hal>
+    <hal format="hidl" optional="true">
+        <name>android.hardware.renderscript</name>
+        <version>1.0</version>
+        <interface>
+            <name>IDevice</name>
+            <instance>default</instance>
+        </interface>
+    </hal>
+    <hal format="hidl" optional="true">
+        <name>android.hardware.sensors</name>
+        <version>1.0</version>
+        <interface>
+            <name>ISensors</name>
+            <instance>default</instance>
+        </interface>
+    </hal>
+    <hal format="hidl" optional="true">
+        <name>android.hardware.soundtrigger</name>
+        <version>2.0</version>
+        <interface>
+            <name>ISoundTriggerHw</name>
+            <instance>default</instance>
+        </interface>
+    </hal>
+    <hal format="hidl" optional="true">
+        <name>android.hardware.tetheroffload.config</name>
+        <version>1.0</version>
+        <interface>
+            <name>IOffloadConfig</name>
+            <instance>default</instance>
+        </interface>
+    </hal>
+    <hal format="hidl" optional="true">
+        <name>android.hardware.tetheroffload.control</name>
+        <version>1.0</version>
+        <interface>
+            <name>IOffloadControl</name>
+            <instance>default</instance>
+        </interface>
+    </hal>
+    <hal format="hidl" optional="true">
+        <name>android.hardware.thermal</name>
+        <version>1.0-1</version>
+        <interface>
+            <name>IThermal</name>
+            <instance>default</instance>
+        </interface>
+    </hal>
+    <hal format="hidl" optional="true">
+        <name>android.hardware.tv.cec</name>
+        <version>1.0</version>
+        <interface>
+            <name>IHdmiCec</name>
+            <instance>default</instance>
+        </interface>
+    </hal>
+    <hal format="hidl" optional="true">
+        <name>android.hardware.tv.input</name>
+        <version>1.0</version>
+        <interface>
+            <name>ITvInput</name>
+            <instance>default</instance>
+        </interface>
+    </hal>
+    <hal format="hidl" optional="true">
+        <name>android.hardware.usb</name>
+        <version>1.0-1</version>
+        <interface>
+            <name>IUsb</name>
+            <instance>default</instance>
+        </interface>
+    </hal>
+    <hal format="hidl" optional="true">
+        <name>android.hardware.vibrator</name>
+        <version>1.0-1</version>
+        <interface>
+            <name>IVibrator</name>
+            <instance>default</instance>
+        </interface>
+    </hal>
+    <hal format="hidl" optional="true">
+        <name>android.hardware.vr</name>
+        <version>1.0</version>
+        <interface>
+            <name>IVr</name>
+            <instance>default</instance>
+        </interface>
+    </hal>
+    <hal format="hidl" optional="true">
+        <name>android.hardware.weaver</name>
+        <version>1.0</version>
+        <interface>
+            <name>IWeaver</name>
+            <instance>default</instance>
+        </interface>
+    </hal>
+    <hal format="hidl" optional="true">
+        <name>android.hardware.wifi</name>
+        <version>1.0-1</version>
+        <interface>
+            <name>IWifi</name>
+            <instance>default</instance>
+        </interface>
+    </hal>
+    <hal format="hidl" optional="true">
+        <name>android.hardware.wifi.offload</name>
+        <version>1.0</version>
+        <interface>
+            <name>IOffload</name>
+            <instance>default</instance>
+        </interface>
+    </hal>
+    <hal format="hidl" optional="true">
+        <name>android.hardware.wifi.supplicant</name>
+        <version>1.0</version>
+        <interface>
+            <name>ISupplicant</name>
+            <instance>default</instance>
+        </interface>
+    </hal>
+</compatibility-matrix>
diff --git a/compatibility_matrix.current.xml b/compatibility_matrix.current.xml
index 8d50325..5c81f26 100644
--- a/compatibility_matrix.current.xml
+++ b/compatibility_matrix.current.xml
@@ -57,7 +57,7 @@
     </hal>
     <hal format="hidl" optional="true">
         <name>android.hardware.broadcastradio</name>
-        <version>1.0</version>
+        <version>1.0-1</version>
         <interface>
             <name>IBroadcastRadioFactory</name>
             <instance>default</instance>
@@ -71,6 +71,14 @@
             <instance>legacy/0</instance>
         </interface>
     </hal>
+    <hal format="hidl" optional="true">
+        <name>android.hardware.cas</name>
+        <version>1.0</version>
+        <interface>
+            <name>IMediaCasService</name>
+            <instance>default</instance>
+        </interface>
+    </hal>
     <hal format="hidl" optional="false">
         <name>android.hardware.configstore</name>
         <version>1.0</version>
@@ -206,10 +214,14 @@
     <hal format="hidl" optional="true">
         <name>android.hardware.oemlock</name>
         <version>1.0</version>
+        <interface>
+            <name>IOemLock</name>
+            <instance>default</instance>
+        </interface>
     </hal>
     <hal format="hidl" optional="true">
         <name>android.hardware.power</name>
-        <version>1.0</version>
+        <version>1.0-1</version>
         <interface>
             <name>IPower</name>
             <instance>default</instance>
@@ -217,7 +229,7 @@
     </hal>
     <hal format="hidl" optional="true">
         <name>android.hardware.radio</name>
-        <version>1.0</version>
+        <version>1.0-1</version>
         <interface>
             <name>IRadio</name>
             <instance>slot1</instance>
@@ -260,9 +272,25 @@
         </interface>
     </hal>
     <hal format="hidl" optional="true">
-        <name>android.hardware.thermal</name>
+        <name>android.hardware.tetheroffload.config</name>
         <version>1.0</version>
         <interface>
+            <name>IOffloadConfig</name>
+            <instance>default</instance>
+        </interface>
+    </hal>
+    <hal format="hidl" optional="true">
+        <name>android.hardware.tetheroffload.control</name>
+        <version>1.0</version>
+        <interface>
+            <name>IOffloadControl</name>
+            <instance>default</instance>
+        </interface>
+    </hal>
+    <hal format="hidl" optional="true">
+        <name>android.hardware.thermal</name>
+        <version>1.0-1</version>
+        <interface>
             <name>IThermal</name>
             <instance>default</instance>
         </interface>
@@ -285,7 +313,7 @@
     </hal>
     <hal format="hidl" optional="true">
         <name>android.hardware.usb</name>
-        <version>1.0</version>
+        <version>1.0-1</version>
         <interface>
             <name>IUsb</name>
             <instance>default</instance>
@@ -293,7 +321,7 @@
     </hal>
     <hal format="hidl" optional="true">
         <name>android.hardware.vibrator</name>
-        <version>1.0</version>
+        <version>1.0-1</version>
         <interface>
             <name>IVibrator</name>
             <instance>default</instance>
@@ -310,16 +338,28 @@
     <hal format="hidl" optional="true">
         <name>android.hardware.weaver</name>
         <version>1.0</version>
+        <interface>
+            <name>IWeaver</name>
+            <instance>default</instance>
+        </interface>
     </hal>
     <hal format="hidl" optional="true">
         <name>android.hardware.wifi</name>
-        <version>1.0</version>
+        <version>1.0-1</version>
         <interface>
             <name>IWifi</name>
             <instance>default</instance>
         </interface>
     </hal>
     <hal format="hidl" optional="true">
+        <name>android.hardware.wifi.offload</name>
+        <version>1.0</version>
+        <interface>
+            <name>IOffload</name>
+            <instance>default</instance>
+        </interface>
+    </hal>
+    <hal format="hidl" optional="true">
         <name>android.hardware.wifi.supplicant</name>
         <version>1.0</version>
         <interface>
diff --git a/compatibility_matrix.legacy.xml b/compatibility_matrix.legacy.xml
index ad6d4b9..5655fb9 100644
--- a/compatibility_matrix.legacy.xml
+++ b/compatibility_matrix.legacy.xml
@@ -57,7 +57,7 @@
     </hal>
     <hal format="hidl" optional="true">
         <name>android.hardware.broadcastradio</name>
-        <version>1.0</version>
+        <version>1.0-1</version>
         <interface>
             <name>IBroadcastRadioFactory</name>
             <instance>default</instance>
@@ -71,6 +71,14 @@
             <instance>legacy/0</instance>
         </interface>
     </hal>
+    <hal format="hidl" optional="true">
+        <name>android.hardware.cas</name>
+        <version>1.0</version>
+        <interface>
+            <name>IMediaCasService</name>
+            <instance>default</instance>
+        </interface>
+    </hal>
     <hal format="hidl" optional="false">
         <name>android.hardware.configstore</name>
         <version>1.0</version>
@@ -204,16 +212,24 @@
         </interface>
     </hal>
     <hal format="hidl" optional="true">
-        <name>android.hardware.power</name>
+        <name>android.hardware.oemlock</name>
         <version>1.0</version>
         <interface>
+            <name>IOemLock</name>
+            <instance>default</instance>
+        </interface>
+    </hal>
+    <hal format="hidl" optional="true">
+        <name>android.hardware.power</name>
+        <version>1.0-1</version>
+        <interface>
             <name>IPower</name>
             <instance>default</instance>
         </interface>
     </hal>
     <hal format="hidl" optional="true">
         <name>android.hardware.radio</name>
-        <version>1.0</version>
+        <version>1.0-1</version>
         <interface>
             <name>IRadio</name>
             <instance>slot1</instance>
@@ -256,9 +272,25 @@
         </interface>
     </hal>
     <hal format="hidl" optional="true">
-        <name>android.hardware.thermal</name>
+        <name>android.hardware.tetheroffload.config</name>
         <version>1.0</version>
         <interface>
+            <name>IOffloadConfig</name>
+            <instance>default</instance>
+        </interface>
+    </hal>
+    <hal format="hidl" optional="true">
+        <name>android.hardware.tetheroffload.control</name>
+        <version>1.0</version>
+        <interface>
+            <name>IOffloadControl</name>
+            <instance>default</instance>
+        </interface>
+    </hal>
+    <hal format="hidl" optional="true">
+        <name>android.hardware.thermal</name>
+        <version>1.0-1</version>
+        <interface>
             <name>IThermal</name>
             <instance>default</instance>
         </interface>
@@ -281,7 +313,7 @@
     </hal>
     <hal format="hidl" optional="true">
         <name>android.hardware.usb</name>
-        <version>1.0</version>
+        <version>1.0-1</version>
         <interface>
             <name>IUsb</name>
             <instance>default</instance>
@@ -289,7 +321,7 @@
     </hal>
     <hal format="hidl" optional="true">
         <name>android.hardware.vibrator</name>
-        <version>1.0</version>
+        <version>1.0-1</version>
         <interface>
             <name>IVibrator</name>
             <instance>default</instance>
@@ -304,14 +336,30 @@
         </interface>
     </hal>
     <hal format="hidl" optional="true">
-        <name>android.hardware.wifi</name>
+        <name>android.hardware.weaver</name>
         <version>1.0</version>
         <interface>
+            <name>IWeaver</name>
+            <instance>default</instance>
+        </interface>
+    </hal>
+    <hal format="hidl" optional="true">
+        <name>android.hardware.wifi</name>
+        <version>1.0-1</version>
+        <interface>
             <name>IWifi</name>
             <instance>default</instance>
         </interface>
     </hal>
     <hal format="hidl" optional="true">
+        <name>android.hardware.wifi.offload</name>
+        <version>1.0</version>
+        <interface>
+            <name>IOffload</name>
+            <instance>default</instance>
+        </interface>
+    </hal>
+    <hal format="hidl" optional="true">
         <name>android.hardware.wifi.supplicant</name>
         <version>1.0</version>
         <interface>
diff --git a/configstore/1.0/default/seccomp_policy/configstore@1.0-arm64.policy b/configstore/1.0/default/seccomp_policy/configstore@1.0-arm64.policy
index 7e3dfe0c..43bf1fa 100644
--- a/configstore/1.0/default/seccomp_policy/configstore@1.0-arm64.policy
+++ b/configstore/1.0/default/seccomp_policy/configstore@1.0-arm64.policy
@@ -39,6 +39,7 @@
 rt_sigreturn: 1
 getrlimit: 1
 madvise: 1
+clock_gettime: 1
 
 # used during process crash by crash_dump to dump process info
 rt_sigprocmask: 1
diff --git a/current.txt b/current.txt
index c0dd0fa..5207c6a 100644
--- a/current.txt
+++ b/current.txt
@@ -189,7 +189,7 @@
 
 # ABI preserving changes to HALs during Android O MR1 (Initial Set)
 
-05e0edf31db5b3ae19047d87059545cc2f836023edf8f9d969319e15e3485158 android.hardware.automotive.vehicle@2.0::types
+26a4dd19a71f3a28249100af29be470f80e08355165fe6a7173aaa1ef264640d android.hardware.automotive.vehicle@2.0::types
 150a338ce11fcec70757c9675d83cf6a5d7b40d0c812741b91671fecce59eac9 android.hardware.broadcastradio@1.0::types
 dc7e6d4f537b9943e27edc4f86c5a03bb643b18f18f866f8c3c71c0ac4ea8cbc android.hardware.broadcastradio@1.0::types
 760485232f6cce07f8bb05e3475509956996b702f77415ee5bff05e2ec5a5bcc android.hardware.dumpstate@1.0::IDumpstateDevice
@@ -244,5 +244,6 @@
 86ba9c03978b79a742e990420bc5ced0673d25a939f82572996bef92621e2014 android.hardware.cas@1.0::IMediaCasService
 503da837d1a67cbdb7c08a033e927e5430ae1b159d98bf72c6336b4dcc5e76f5 android.hardware.cas.native@1.0::types
 619600109232ed64b827c8a11beed8070b1827ae464547d7aa146cf0473b4bca android.hardware.cas.native@1.0::IDescrambler
+0a159f81359cd4f71bbe00972ee8403ea79351fb7c0cd48be72ebb3e424dbaef android.hardware.radio@1.0::types
 f4945e397b5dea41bb64518dfde59be71245d8a125fd1e0acffeb57ac7b08fed android.hardware.thermal@1.1::IThermal
 c8bc853546dd55584611def2a9fa1d99f657e3366c976d2f60fe6b8aa6d2cb87 android.hardware.thermal@1.1::IThermalCallback
diff --git a/graphics/composer/2.1/default/IComposerCommandBuffer.h b/graphics/composer/2.1/default/IComposerCommandBuffer.h
index 9ee5f4f..058709c 100644
--- a/graphics/composer/2.1/default/IComposerCommandBuffer.h
+++ b/graphics/composer/2.1/default/IComposerCommandBuffer.h
@@ -92,6 +92,23 @@
     bool writeQueue(bool* outQueueChanged, uint32_t* outCommandLength,
             hidl_vec<hidl_handle>* outCommandHandles)
     {
+        // After data are written to the queue, it may not be read by the
+        // remote reader when
+        //
+        //  - the writer does not send them (because of other errors)
+        //  - the hwbinder transaction fails
+        //  - the reader does not read them (because of other errors)
+        //
+        // Discard the stale data here.
+        size_t staleDataSize = mQueue ? mQueue->availableToRead() : 0;
+        if (staleDataSize > 0) {
+            ALOGW("discarding stale data from message queue");
+            CommandQueueType::MemTransaction tx;
+            if (mQueue->beginRead(staleDataSize, &tx)) {
+                mQueue->commitRead(staleDataSize);
+            }
+        }
+
         // write data to queue, optionally resizing it
         if (mQueue && (mDataMaxSize <= mQueue->getQuantumCount())) {
             if (!mQueue->write(mData.get(), mDataWritten)) {
diff --git a/graphics/composer/2.1/vts/functional/VtsHalGraphicsComposerV2_1TargetTest.cpp b/graphics/composer/2.1/vts/functional/VtsHalGraphicsComposerV2_1TargetTest.cpp
index 18955da..9a749d7 100644
--- a/graphics/composer/2.1/vts/functional/VtsHalGraphicsComposerV2_1TargetTest.cpp
+++ b/graphics/composer/2.1/vts/functional/VtsHalGraphicsComposerV2_1TargetTest.cpp
@@ -23,6 +23,7 @@
 #include "VtsHalGraphicsMapperTestUtils.h"
 
 #include <VtsHalHidlTargetTestBase.h>
+#include <VtsHalHidlTargetTestEnvBase.h>
 #include <unistd.h>
 
 #include <algorithm>
@@ -703,10 +704,11 @@
 }  // namespace android
 
 int main(int argc, char** argv) {
-  ::testing::InitGoogleTest(&argc, argv);
-
-  int status = RUN_ALL_TESTS();
-  LOG(INFO) << "Test result = " << status;
-
-  return status;
+    using android::hardware::graphics::composer::V2_1::tests::GraphicsComposerHidlEnvironment;
+    ::testing::AddGlobalTestEnvironment(GraphicsComposerHidlEnvironment::Instance());
+    ::testing::InitGoogleTest(&argc, argv);
+    GraphicsComposerHidlEnvironment::Instance()->init(&argc, argv);
+    int status = RUN_ALL_TESTS();
+    ALOGI("Test result = %d", status);
+    return status;
 }
diff --git a/keymaster/3.0/vts/functional/keymaster_hidl_hal_test.cpp b/keymaster/3.0/vts/functional/keymaster_hidl_hal_test.cpp
index 78353ea..3d78f45 100644
--- a/keymaster/3.0/vts/functional/keymaster_hidl_hal_test.cpp
+++ b/keymaster/3.0/vts/functional/keymaster_hidl_hal_test.cpp
@@ -1774,7 +1774,7 @@
                                              .Authorization(TAG_NO_AUTH_REQUIRED)
                                              .EcdsaSigningKey(224)
                                              .Digest(Digest::NONE)));
-    string message(64 * 1024, 'a');
+    string message(2 * 1024, 'a');
     SignMessage(message, AuthorizationSetBuilder().Digest(Digest::NONE));
 }
 
@@ -2607,7 +2607,7 @@
 }
 
 /*
- * EncryptionOperationsTest.RsaNoPaddingTooLong
+ * EncryptionOperationsTest.RsaNoPaddingTooLarge
  *
  * Verifies that raw RSA encryption of too-large (numerically) messages fails in the expected way.
  */
@@ -3907,7 +3907,7 @@
  * Verifies that the addRngEntropy method doesn't blow up when given a largish amount of data.
  */
 TEST_F(AddEntropyTest, AddLargeEntropy) {
-    EXPECT_EQ(ErrorCode::OK, keymaster().addRngEntropy(HidlBuf(string(16 * 1024, 'a'))));
+    EXPECT_EQ(ErrorCode::OK, keymaster().addRngEntropy(HidlBuf(string(2 * 1024, 'a'))));
 }
 
 typedef KeymasterHidlTest AttestationTest;
diff --git a/media/omx/1.0/vts/functional/audio/VtsHalMediaOmxV1_0TargetAudioDecTest.cpp b/media/omx/1.0/vts/functional/audio/VtsHalMediaOmxV1_0TargetAudioDecTest.cpp
index 6790ebf..8520757 100644
--- a/media/omx/1.0/vts/functional/audio/VtsHalMediaOmxV1_0TargetAudioDecTest.cpp
+++ b/media/omx/1.0/vts/functional/audio/VtsHalMediaOmxV1_0TargetAudioDecTest.cpp
@@ -765,13 +765,13 @@
     int bytesCount = 0;
     uint32_t flags = 0;
     uint32_t timestamp = 0;
-    timestampDevTest = true;
+    timestampDevTest = false;
     while (1) {
         if (!(eleInfo >> bytesCount)) break;
         eleInfo >> flags;
         eleInfo >> timestamp;
         Info.push_back({bytesCount, flags, timestamp});
-        if (flags != OMX_BUFFERFLAG_CODECCONFIG)
+        if (timestampDevTest && (flags != OMX_BUFFERFLAG_CODECCONFIG))
             timestampUslist.push_back(timestamp);
     }
     eleInfo.close();
@@ -813,7 +813,7 @@
     packedArgs audioArgs = {eEncoding, compName};
     testEOS(omxNode, observer, &iBuffer, &oBuffer, false, eosFlag, nullptr,
             portReconfiguration, kPortIndexInput, kPortIndexOutput, &audioArgs);
-    EXPECT_EQ(timestampUslist.empty(), true);
+    if (timestampDevTest) EXPECT_EQ(timestampUslist.empty(), true);
     // set state to idle
     changeStateExecutetoIdle(omxNode, observer, &iBuffer, &oBuffer);
     // set state to executing
diff --git a/media/omx/1.0/vts/functional/component/VtsHalMediaOmxV1_0TargetComponentTest.cpp b/media/omx/1.0/vts/functional/component/VtsHalMediaOmxV1_0TargetComponentTest.cpp
index 0f29d91..2ec86b2 100644
--- a/media/omx/1.0/vts/functional/component/VtsHalMediaOmxV1_0TargetComponentTest.cpp
+++ b/media/omx/1.0/vts/functional/component/VtsHalMediaOmxV1_0TargetComponentTest.cpp
@@ -371,7 +371,7 @@
         kPortIndexOutput = kPortIndexInput + 1;
     }
 
-    for (size_t i = kPortIndexInput; i < kPortIndexOutput; i++) {
+    for (size_t i = kPortIndexInput; i <= kPortIndexOutput; i++) {
         OMX_PARAM_PORTDEFINITIONTYPE portDef;
         status =
             getPortParam(omxNode, OMX_IndexParamPortDefinition, i, &portDef);
@@ -406,10 +406,32 @@
             setPortParam(omxNode, OMX_IndexParamPortDefinition, i, &mirror);
 
             portDef = mirror;
-            portDef.nBufferSize >>= 1;
+            OMX_U32 nBufferSize = portDef.nBufferSize >> 1;
+            if (nBufferSize != 0) {
+                if (!strncmp(gEnv->getComponent().c_str(), "OMX.google.", 11)) {
+                    portDef.nBufferSize = nBufferSize;
+                } else {
+                    // Probable alignment requirements of vendor component
+                    portDef.nBufferSize = ALIGN_POWER_OF_TWO(nBufferSize, 12);
+                    nBufferSize = portDef.nBufferSize;
+                }
+            } else {
+                ASSERT_TRUE(false) << "Unexpected buffer size";
+            }
             setPortParam(omxNode, OMX_IndexParamPortDefinition, i, &portDef);
             getPortParam(omxNode, OMX_IndexParamPortDefinition, i, &portDef);
-            EXPECT_EQ(portDef.nBufferSize, mirror.nBufferSize);
+            // SPECIAL CASE: For video decoder, allow configuration of input
+            // buffer size even if it is less than minimum requirement and
+            // similarly for encoder allow configuration of output port buffer
+            // size.
+            if ((compClass == video_encoder && i == kPortIndexOutput) ||
+                (compClass == video_decoder && i == kPortIndexInput)) {
+                double dev = (portDef.nBufferSize / (double)nBufferSize);
+                dev -= 1;
+                if (dev < 0 || dev > 0.1) EXPECT_TRUE(false);
+            } else {
+                EXPECT_EQ(portDef.nBufferSize, mirror.nBufferSize);
+            }
             setPortParam(omxNode, OMX_IndexParamPortDefinition, i, &mirror);
 
             portDef = mirror;
@@ -467,6 +489,11 @@
         portBase = params.nStartPortNumber;
     }
 
+    // set state to idle
+    status = omxNode->sendCommand(toRawCommandType(OMX_CommandStateSet),
+                                  OMX_StateIdle);
+    ASSERT_EQ(status, android::hardware::media::omx::V1_0::Status::OK);
+
     OMX_PARAM_PORTDEFINITIONTYPE portDef;
     status =
         getPortParam(omxNode, OMX_IndexParamPortDefinition, portBase, &portDef);
@@ -556,6 +583,67 @@
                             kPortIndexInput, kPortIndexOutput);
 }
 
+// test port mode configuration when the component is in various states
+TEST_F(ComponentHidlTest, PortModeConfig) {
+    description("Test Port Mode Configuration");
+    if (disableTest) return;
+    android::hardware::media::omx::V1_0::Status status;
+    uint32_t kPortIndexInput = 0, kPortIndexOutput = 1;
+    Message msg;
+
+    status = setRole(omxNode, gEnv->getRole().c_str());
+    ASSERT_EQ(status, ::android::hardware::media::omx::V1_0::Status::OK);
+    OMX_PORT_PARAM_TYPE params;
+    if (compClass == audio_decoder || compClass == audio_encoder) {
+        status = getParam(omxNode, OMX_IndexParamAudioInit, &params);
+    } else {
+        status = getParam(omxNode, OMX_IndexParamVideoInit, &params);
+    }
+    if (status == ::android::hardware::media::omx::V1_0::Status::OK) {
+        ASSERT_EQ(params.nPorts, 2U);
+        kPortIndexInput = params.nStartPortNumber;
+        kPortIndexOutput = kPortIndexInput + 1;
+    }
+
+    android::Vector<BufferInfo> iBuffer, oBuffer;
+
+    // set port mode
+    PortMode portMode[2];
+    initPortMode(portMode, isSecure, compClass);
+    status = omxNode->setPortMode(kPortIndexInput, portMode[0]);
+    EXPECT_EQ(status, ::android::hardware::media::omx::V1_0::Status::OK);
+    status = omxNode->setPortMode(kPortIndexOutput, portMode[1]);
+    EXPECT_EQ(status, ::android::hardware::media::omx::V1_0::Status::OK);
+
+    // set state to idle
+    changeStateLoadedtoIdle(omxNode, observer, &iBuffer, &oBuffer,
+                            kPortIndexInput, kPortIndexOutput, portMode);
+    // Only Allow Port Mode configuration in loaded state
+    status = omxNode->setPortMode(kPortIndexInput, portMode[0]);
+    EXPECT_NE(status, ::android::hardware::media::omx::V1_0::Status::OK);
+    status = omxNode->setPortMode(kPortIndexOutput, portMode[1]);
+    EXPECT_NE(status, ::android::hardware::media::omx::V1_0::Status::OK);
+
+    // set state to executing
+    changeStateIdletoExecute(omxNode, observer);
+    // Only Allow Port Mode configuration in loaded state
+    status = omxNode->setPortMode(kPortIndexInput, portMode[0]);
+    EXPECT_NE(status, ::android::hardware::media::omx::V1_0::Status::OK);
+    status = omxNode->setPortMode(kPortIndexOutput, portMode[1]);
+    EXPECT_NE(status, ::android::hardware::media::omx::V1_0::Status::OK);
+
+    // set state to idle
+    changeStateExecutetoIdle(omxNode, observer, &iBuffer, &oBuffer);
+    // set state to loaded
+    changeStateIdletoLoaded(omxNode, observer, &iBuffer, &oBuffer,
+                            kPortIndexInput, kPortIndexOutput);
+
+    status = omxNode->setPortMode(kPortIndexInput, portMode[0]);
+    EXPECT_EQ(status, ::android::hardware::media::omx::V1_0::Status::OK);
+    status = omxNode->setPortMode(kPortIndexOutput, portMode[1]);
+    EXPECT_EQ(status, ::android::hardware::media::omx::V1_0::Status::OK);
+}
+
 // state transitions test
 TEST_F(ComponentHidlTest, StateTransitions) {
     description("Test State Transitions Loaded<->Idle<->Execute");
diff --git a/media/omx/1.0/vts/functional/video/VtsHalMediaOmxV1_0TargetVideoDecTest.cpp b/media/omx/1.0/vts/functional/video/VtsHalMediaOmxV1_0TargetVideoDecTest.cpp
index c75bd7c..a5b5524 100644
--- a/media/omx/1.0/vts/functional/video/VtsHalMediaOmxV1_0TargetVideoDecTest.cpp
+++ b/media/omx/1.0/vts/functional/video/VtsHalMediaOmxV1_0TargetVideoDecTest.cpp
@@ -498,6 +498,21 @@
         ASSERT_EQ(msg.data.eventData.data1, kPortIndexOutput);
         if (msg.data.eventData.data2 == OMX_IndexParamPortDefinition ||
             msg.data.eventData.data2 == 0) {
+            // Components can send various kinds of port settings changed events
+            // all at once. Before committing to a full port reconfiguration,
+            // defer any events waiting in the queue to be addressed to a later
+            // point.
+            android::List<Message> msgQueueDefer;
+            while (1) {
+                status = observer->dequeueMessage(&msg, DEFAULT_TIMEOUT,
+                                                  iBuffer, oBuffer);
+                if (status !=
+                    android::hardware::media::omx::V1_0::Status::TIMED_OUT) {
+                    msgQueueDefer.push_back(msg);
+                    continue;
+                } else
+                    break;
+            }
             status = omxNode->sendCommand(
                 toRawCommandType(OMX_CommandPortDisable), kPortIndexOutput);
             ASSERT_EQ(status, android::hardware::media::omx::V1_0::Status::OK);
@@ -577,6 +592,16 @@
                 ASSERT_EQ(msg.data.eventData.data1, OMX_CommandPortEnable);
                 ASSERT_EQ(msg.data.eventData.data2, kPortIndexOutput);
 
+                // Push back deferred messages to the list
+                android::List<Message>::iterator it = msgQueueDefer.begin();
+                while (it != msgQueueDefer.end()) {
+                    status = omxNode->dispatchMessage(*it);
+                    ASSERT_EQ(
+                        status,
+                        ::android::hardware::media::omx::V1_0::Status::OK);
+                    it++;
+                }
+
                 // dispatch output buffers
                 for (size_t i = 0; i < oBuffer->size(); i++) {
                     dispatchOutputBuffer(omxNode, oBuffer, i, oPortMode);
@@ -814,7 +839,7 @@
     OMX_VIDEO_PARAM_PORTFORMATTYPE portFormat;
     *eColorFormat = OMX_COLOR_FormatUnused;
     portFormat.nIndex = 0;
-    while (1) {
+    while (portFormat.nIndex < 512) {
         status = getPortParam(omxNode, OMX_IndexParamVideoPortFormat,
                               kPortIndexOutput, &portFormat);
         if (status != ::android::hardware::media::omx::V1_0::Status::OK) break;
@@ -828,7 +853,9 @@
             break;
         }
         if (OMX_COLOR_FormatYUV420SemiPlanar == portFormat.eColorFormat ||
-            OMX_COLOR_FormatYUV420Planar == portFormat.eColorFormat) {
+            OMX_COLOR_FormatYUV420Planar == portFormat.eColorFormat ||
+            OMX_COLOR_FormatYUV420PackedPlanar == portFormat.eColorFormat ||
+            OMX_COLOR_FormatYUV420PackedSemiPlanar == portFormat.eColorFormat) {
             *eColorFormat = portFormat.eColorFormat;
             break;
         }
@@ -906,7 +933,7 @@
         eleInfo >> flags;
         eleInfo >> timestamp;
         Info.push_back({bytesCount, flags, timestamp});
-        if (flags != OMX_BUFFERFLAG_CODECCONFIG)
+        if (timestampDevTest && (flags != OMX_BUFFERFLAG_CODECCONFIG))
             timestampUslist.push_back(timestamp);
         if (maxBytesCount < bytesCount) maxBytesCount = bytesCount;
     }
@@ -980,7 +1007,7 @@
                            kPortIndexInput, kPortIndexOutput, portMode[1]);
     testEOS(omxNode, observer, &iBuffer, &oBuffer, false, eosFlag, portMode,
             portReconfiguration, kPortIndexInput, kPortIndexOutput, nullptr);
-    EXPECT_EQ(timestampUslist.empty(), true);
+    if (timestampDevTest) EXPECT_EQ(timestampUslist.empty(), true);
     // set state to idle
     changeStateExecutetoIdle(omxNode, observer, &iBuffer, &oBuffer);
     // set state to executing
diff --git a/media/omx/1.0/vts/functional/video/VtsHalMediaOmxV1_0TargetVideoEncTest.cpp b/media/omx/1.0/vts/functional/video/VtsHalMediaOmxV1_0TargetVideoEncTest.cpp
index f4a4e9b..23f051d 100644
--- a/media/omx/1.0/vts/functional/video/VtsHalMediaOmxV1_0TargetVideoEncTest.cpp
+++ b/media/omx/1.0/vts/functional/video/VtsHalMediaOmxV1_0TargetVideoEncTest.cpp
@@ -1300,7 +1300,7 @@
     eleStream.close();
     waitOnInputConsumption(omxNode, observer, &iBuffer, &oBuffer);
     testEOS(omxNode, observer, &iBuffer, &oBuffer, false, eosFlag);
-    EXPECT_EQ(timestampUslist.empty(), true);
+    if (timestampDevTest) EXPECT_EQ(timestampUslist.empty(), true);
 
     // set state to idle
     changeStateExecutetoIdle(omxNode, observer, &iBuffer, &oBuffer);
diff --git a/neuralnetworks/1.0/Android.bp b/neuralnetworks/1.0/Android.bp
index b5603a2..d7c3bbb 100644
--- a/neuralnetworks/1.0/Android.bp
+++ b/neuralnetworks/1.0/Android.bp
@@ -5,6 +5,7 @@
     srcs: [
         "types.hal",
         "IDevice.hal",
+        "IEvent.hal",
         "IPreparedModel.hal",
     ],
 }
@@ -19,6 +20,7 @@
     out: [
         "android/hardware/neuralnetworks/1.0/types.cpp",
         "android/hardware/neuralnetworks/1.0/DeviceAll.cpp",
+        "android/hardware/neuralnetworks/1.0/EventAll.cpp",
         "android/hardware/neuralnetworks/1.0/PreparedModelAll.cpp",
     ],
 }
@@ -38,6 +40,11 @@
         "android/hardware/neuralnetworks/1.0/BnHwDevice.h",
         "android/hardware/neuralnetworks/1.0/BpHwDevice.h",
         "android/hardware/neuralnetworks/1.0/BsDevice.h",
+        "android/hardware/neuralnetworks/1.0/IEvent.h",
+        "android/hardware/neuralnetworks/1.0/IHwEvent.h",
+        "android/hardware/neuralnetworks/1.0/BnHwEvent.h",
+        "android/hardware/neuralnetworks/1.0/BpHwEvent.h",
+        "android/hardware/neuralnetworks/1.0/BsEvent.h",
         "android/hardware/neuralnetworks/1.0/IPreparedModel.h",
         "android/hardware/neuralnetworks/1.0/IHwPreparedModel.h",
         "android/hardware/neuralnetworks/1.0/BnHwPreparedModel.h",
diff --git a/neuralnetworks/1.0/IDevice.hal b/neuralnetworks/1.0/IDevice.hal
index b826b23..b6f9433 100644
--- a/neuralnetworks/1.0/IDevice.hal
+++ b/neuralnetworks/1.0/IDevice.hal
@@ -18,14 +18,66 @@
 
 package android.hardware.neuralnetworks@1.0;
 
+import IEvent;
 import IPreparedModel;
 
+/**
+ * This interface represents a device driver.
+ */
 interface IDevice {
-    initialize() generates(Capabilities capabilities);
+    /**
+     * Gets the capabilities of a driver.
+     *
+     * @return status ErrorStatus::NONE if successful.
+     * @return capabilities Capabilities of the driver.
+     */
+    getCapabilities() generates (ErrorStatus status, Capabilities capabilities);
 
-    getSupportedSubgraph(Model model) generates(vec<bool> supported);
+    /**
+     * Gets the supported operations in a model.
+     *
+     * getSupportedSubgraph provides a more nuanced indication on whether a
+     * model is able to be compiled by the driver. Having the entire model
+     * allows for additional information such as tensor shapes to inputs or
+     * tensor strides, information which is not known in "initialize".
+     *
+     * @param model A model whose operations--and their corresponding
+     *              operands--are to be verified by the driver.
+     * @return status ErrorStatus::NONE if successful.
+     * @return supportedOperations 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.
+     */
+    getSupportedOperations(Model model)
+            generates (ErrorStatus status, vec<bool> supportedOperations);
 
-    prepareModel(Model model) generates(IPreparedModel preparedModel);
+    /**
+     * Prepares a model for execution.
+     *
+     * prepareModel is used to make any necessary transformations or alternative
+     * representations to a model for execution, possible including
+     * transformations on the constant data, optimization on the model's graph,
+     * or compilation into the device's native binary.
+     *
+     * 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.
+     *
+     * @param model The model to be prepared for execution.
+     * @param event A synchronization callback that must be signaled once the
+     *              execution has finished.
+     * @return status ErrorStatus::NONE if successful.
+     * @return preparedModel A handle to the resultant prepared model.
+     */
+    prepareModel(Model model, IEvent event)
+            generates (ErrorStatus status, IPreparedModel preparedModel);
 
-    getStatus() generates(DeviceStatus status);
+    /**
+     * Returns the current status of a driver.
+     *
+     * @return status Status of the driver.
+     */
+    getStatus() generates (DeviceStatus status);
 };
diff --git a/neuralnetworks/1.0/IEvent.hal b/neuralnetworks/1.0/IEvent.hal
new file mode 100644
index 0000000..2ebda58
--- /dev/null
+++ b/neuralnetworks/1.0/IEvent.hal
@@ -0,0 +1,43 @@
+/*
+ * Copyright (C) 2017 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.
+ */
+
+/* This HAL is a work in progress */
+
+package android.hardware.neuralnetworks@1.0;
+
+/**
+ * The IEvent interface is a callback object passed by the
+ * Neuralnetworks runtime to the vendor service. It is used as a
+ * synchronization primitive between one or more runtime threads and a
+ * single asynchronous vendor thread.  An event object is passed as an
+ * argument to a HIDL call that is expected to take a non-trivial
+ * amount of time. When the asynchronous execution thread has
+ * completed its computation, it must call "notify" on the event to
+ * indicate to the Neuralnetworks runtime whether the computation was
+ * successful or not, and that the corresponding output is ready to be
+ * consumed if the execution was successful.
+ */
+interface IEvent {
+
+    /**
+     * IEvent::notify is called by the server thread (i.e., the thread doing
+     * the work) to mark the event as completed so that any threads requiring
+     * the corresponding output can continue executing.
+     *
+     * @param status ErrorStatus::NONE if successful.
+     */
+    oneway notify(ErrorStatus status);
+};
diff --git a/neuralnetworks/1.0/IPreparedModel.hal b/neuralnetworks/1.0/IPreparedModel.hal
index 566d6ac..a7c3342 100644
--- a/neuralnetworks/1.0/IPreparedModel.hal
+++ b/neuralnetworks/1.0/IPreparedModel.hal
@@ -14,12 +14,33 @@
  * limitations under the License.
  */
 
-/* This HAL is a work in progress */
-
 package android.hardware.neuralnetworks@1.0;
 
+import IEvent;
+
+/**
+ * IPreparedModel describes a model that has been prepared for execution and
+ * is used to launch executions.
+ */
 interface IPreparedModel {
-    // TODO: The execution is synchronous.  Change that to have a callback on completion.
-    // Multiple threads can call this execute function concurrently.
-    execute(Request request) generates(bool success);
+    /**
+     * Spawns an asynchronous execution on a prepared model.
+     *
+     * Executions are asynchronous with respect to the Neuralnetworks runtime.
+     * To support this, IPreparedModel::execute must spawn a new task and return
+     * whether the task was successfully launched. The asynchronous task which
+     * performs the execution must call event's IEvent::notify with the status
+     * of the execution immediately after the execution has finished.
+     *
+     * Multiple threads can call this execute function concurrently.
+     *
+     * @param request The input and output information on which the prepared
+     *                model is to be executed.
+     *                prepared model.
+     * @param event A callback used for synchronization that must be signaled
+     *              once the execution has finished.
+     * @return status ErrorStatus::NONE if the asynchronous task was
+     *                successfully launched.
+     */
+    execute(Request request, IEvent event) generates (ErrorStatus status);
 };
diff --git a/neuralnetworks/1.0/types.hal b/neuralnetworks/1.0/types.hal
index 959506a..39e3d34 100644
--- a/neuralnetworks/1.0/types.hal
+++ b/neuralnetworks/1.0/types.hal
@@ -14,170 +14,1259 @@
  * limitations under the License.
  */
 
-/* This HAL is a work in progress */
-
 package android.hardware.neuralnetworks@1.0;
 
-// The types an operand can have.
-// These values are the same as found in the NeuralNetworks.h file.
-// When modifying, be sure to update HAL_NUM_OPERAND_TYPES in HalIntefaces.h.
-enum OperandType : uint32_t {
-    FLOAT16                   = 0,
-    FLOAT32                   = 1,
-    INT8                      = 2,
-    UINT8                     = 3,
-    INT16                     = 4,
-    UINT16                    = 5,
-    INT32                     = 6,
-    UINT32                    = 7,
-    TENSOR_FLOAT16            = 8,
-    TENSOR_FLOAT32            = 9,
-    TENSOR_QUANT8_ASYMM       = 10,
+/**
+ * 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.
+ */
+enum OperandType : int32_t {
+    /**
+     * The following entries are used to declare scalars.
+     */
+    FLOAT32             = 0,
+    INT32               = 1,
+    UINT32              = 2,
+
+    /**
+     * The following entries are used to declare tensors.
+     */
+    TENSOR_FLOAT32      = 3,
+    TENSOR_INT32        = 4,
+
+    /**
+     * A tensor of 8 bit 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
+     * - zero_value: a 32 bit integer
+     *
+     * The formula is:
+     * real_value = (integer_value - zero_value) * scale.
+     */
+    TENSOR_QUANT8_ASYMM = 5,
+
+    /**
+     * The following entries are OEM specific operand types.
+     */
+    OEM                 = 10000,
+    TENSOR_OEM_BYTE     = 10001,
 };
 
-// The type of operations.  Unlike the operation types found in
-// NeuralNetworks.h file, these specify the data type they operate on.
-// This is done to simplify the work of drivers.
-// TODO: Currently they are the same.  Add a conversion when finalizing the model.
-// When modifying, be sure to update HAL_NUM_OPERATION_TYPES in HalIntefaces.h.
-enum OperationType : uint32_t {
-    OEM_OPERATION                = 0,
-    AVERAGE_POOL                 = 1,
-    CONCATENATION                = 2,
-    CONV                         = 3,
-    DEPTHWISE_CONV               = 4,
-    MAX_POOL                     = 5,
-    L2_POOL                      = 6,
-    DEPTH_TO_SPACE               = 7,
-    SPACE_TO_DEPTH               = 8,
-    LOCAL_RESPONSE_NORMALIZATION = 9,
-    SOFTMAX                      = 10,
-    RESHAPE                      = 11,
-    SPLIT                        = 12,
-    FAKE_QUANT                   = 13,
-    ADD                          = 14,
-    FULLY_CONNECTED              = 15,
-    CAST                         = 16,
-    MUL                          = 17,
-    L2_NORMALIZATION             = 18,
-    LOGISTIC                     = 19,
-    RELU                         = 20,
-    RELU6                        = 21,
-    RELU1                        = 22,
-    TANH                         = 23,
-    DEQUANTIZE                   = 24,
-    FLOOR                        = 25,
-    GATHER                       = 26,
-    RESIZE_BILINEAR              = 27,
-    LSH_PROJECTION               = 28,
-    LSTM                         = 29,
-    SVDF                         = 30,
-    RNN                          = 31,
-    N_GRAM                       = 32,
-    EMBEDDING_LOOKUP             = 33,
-    HASHTABLE_LOOKUP             = 34,
+/**
+ * Operation types.
+ *
+ * The type of an operation in a model.
+ */
+enum OperationType : int32_t {
+    /**
+     * Adds two tensors, elment-wise.
+     *
+     * Takes two input tensors of identical type 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}
+     *
+     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     * Supported tensor rank: up to 4
+     *
+     * Inputs:
+     * 0: A tensor.
+     * 1: A tensor of the same type, and compatible dimensions as input0.
+     * 2: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+     *    Specifies the activation to invoke on the result of each addition.
+     *
+     * Ouputs:
+     * 0: The sum, a tensor of the same type as input0.
+     */
+    ADD = 0,
+
+    /**
+     * Performs a 2-D average pooling operation.
+     *
+     * The output dimensions are functions of the filter dimensions, stride, and padding.
+     *
+     * The values in output Tensor is computed as:
+     *     output[batch, row, col, channel] =
+     *         sum_{i, j}(input[batch, row + i, col + j, channel]) / sum(1)
+     *
+     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor rank: 4, with "NHWC" data layout.
+     *
+     * Inputs:
+     * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
+     * 1: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
+     * 2: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
+     * 3: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
+     * 4: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
+     * 5: An INT32 value, specifying the output stride in the ‘width’ dimension.
+     * 6: An INT32 value, specifying the output stride in the ‘height’ dimension.
+     * 7: An INT32 value, specifying the filter width.
+     * 8: An INT32 value, specifying the filter height.
+     * 9: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+     *    Specifies the activation to invoke on the result of each addition.
+     *
+     * Ouputs:
+     * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
+     */
+    AVERAGE_POOL_2D = 1,
+
+    /**
+     * Concatenates the input tensors along the given dimension.
+     *
+     * The input tensors must have identical type and the same dimensions except the
+     * dimension along the concatenation axis.
+     *
+     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor rank: up to 4
+     *
+     * Inputs:
+     * 0 ~ n: The list on n input tensors, of shape [D0, D1, ..., Daxis(i), ..., Dm]
+     * n+1: An INT32 value, specifying the concatenation axis.
+     * n+2: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+     *    Specifies the activation to invoke on the result of each addition.
+     *
+     * Ouputs:
+     * 0: The output, a tensor of the same type as the input tensors.
+          The output shape is [D0, D1, ..., sum(Daxis(i)), ..., Dm].
+     */
+    CONCATENATION = 2,
+
+    /**
+     * Performs an 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 output Tensor is computed as:
+     *     output[batch, row, col, channel] =
+     *         sum_{i, j} (
+     *             input[batch, row + i, col + j, k] *
+     *             filter[channel, row + i, col + j, k] +
+     *             bias[channel]
+     *         )
+     *
+     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor rank: 4, with "NHWC" data layout.
+     *
+     * Inputs:
+     * 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.
+     * 2: A 1-D tensor, of shape [depth_out], specifying the bias.
+     *    For input tensor of {@link OperandType::TENSOR_FLOAT32} type, the bias should
+     *    also be of {@link OperandType::TENSOR_FLOAT32}.
+     *    For input tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the bias
+     *    should be of {@link OperandType::TENSOR_INT32}.
+     * 3: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
+     * 4: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
+     * 5: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
+     * 6: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
+     * 7: An INT32 value, specifying the output stride in the ‘width’ dimension.
+     * 8: An INT32 value, specifying the output stride in the ‘height’ dimension.
+     * 9: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+     *    Specifies the activation to invoke on the result of each addition.
+     *
+     * Ouputs:
+     * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth_out].
+     */
+    CONV_2D = 3,
+
+    /**
+     * Performs an depthwise 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_in] containing
+     * in_channels 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 output Tensor is 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[di, dj, k, q]
+     *         )
+     *
+     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor rank: 4, with "NHWC" data layout.
+     *
+     * Inputs:
+     * 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.
+     * 2: A 1-D tensor, of shape [depth_out], specifying the bias.
+     *    For input tensor of {@link OperandType::TENSOR_FLOAT32} type, the bias should
+     *    also be of {@link OperandType::TENSOR_FLOAT32}.
+     *    For input tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the bias
+     *    should be of {@link OperandType::TENSOR_INT32}.
+     * 3: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
+     * 4: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
+     * 5: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
+     * 6: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
+     * 7: An INT32 value, specifying the output stride in the ‘width’ dimension.
+     * 8: An INT32 value, specifying the output stride in the ‘height’ dimension.
+     * 9: An INT32 value, specifying the depthwise multiplier.
+     * 10: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+     *    Specifies the activation to invoke on the result of each addition.
+     *
+     * Ouputs:
+     * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth_out].
+     */
+    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 types: {@link OperandType::TENSOR_FLOAT32}
+     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor rank: 4, with "NHWC" data layout.
+     *
+     * Inputs:
+     * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
+     * 1: An INT32 value, specifying the block_size. block_size must be >=1 and
+     *    block_size * block_size must be a divisor of the input depth.
+     *
+     * Ouputs:
+     * 0: The output 4-D tensor, of shape [batch, height*block_size, width*block_size,
+     *    depth/(block_size*block_size)].
+     */
+    DEPTH_TO_SPACE = 5,
+
+    /**
+     * Dequantizes the input tensor.
+     *
+     * The formula is:
+     *     output = (input - zero_value) * scale.
+     *
+     * Supported tensor types: {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor rank: up to 4
+     *
+     * Inputs:
+     * 0: A tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}.
+     *
+     * Ouputs:
+     * 0: The output tensor of same shape as input0, but with type
+          {@link OperandType::TENSOR_FLOAT32}.
+     */
+    DEQUANTIZE = 6,
+
+    /**
+     * Looks up items from a given tensor.
+     *
+     * Each item in the output is a raw copy of the corresponding item in
+     * the input “values”. If the the given “lookup” indices are out of bounds,
+     * the op will fail and an error will be reported.
+     *
+     * Inputs:
+     * * 0: Values. An n-D tensor of any type X (where n >= 2). E.g., if n is 2,
+     *      then the shape would be [lookup_dimension, values_dimension], where
+     *      “lookup_dimension” corresponds to the indexing dimension in the lookup
+     *      table, and “values_dimension” to the contents.
+     * * 1: Lookups. An 1-D tensor of type T, of shape [lookup_size], where
+     *      “lookup_size” is the number of elements to look for, and each entry
+     *      corresponds to the first dimension of the “values” tensor.
+     *
+     * Output:
+     * * 0: A n-D tensor of type X and the same rank and shape as the “values”
+     *      tensor, except for the first dimension which has size “lookup_size”.
+     */
+    EMBEDDING_LOOKUP = 7,
+
+    /**
+     * Computes element-wise floor() on the input tensor.
+     *
+     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     * Supported tensor rank: up to 4
+     *
+     * Inputs:
+     * 0: A tensor.
+     *
+     * Ouputs:
+     * 0: The output, a tensor of the same type and dimensions as input0.
+     */
+    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 types: {@link OperandType::TENSOR_FLOAT32}
+     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor rank: up to 4.
+     *
+     * Inputs:
+     * 0: A tensor, specifying the input. If rank is greater than 2, then it gets flattened to
+     *    a 2-D Tensor. The 2-D Tensor is handled as if dimensions corresponded to shape
+     *    [batch_size, input_size], where “batch_size” corresponds to the batching dimension,
+     *    and “input_size” is the size of the input.
+     * 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} type, the bias should
+     *    also be of {@link OperandType::TENSOR_FLOAT32}.
+     *    For input tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the bias
+     *    should be of {@link OperandType::TENSOR_INT32}.
+     * 3: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+     *    Specifies the activation to invoke on the result of each addition.
+     *
+     * Ouputs:
+     * 0: The output tensor, of shape [batch_size, num_units].
+     */
+    FULLY_CONNECTED = 9,
+
+    /**
+     * Looks up values of a hash table with given keys.
+     *
+     * Inputs:
+     * * 0: Lookups. A 1-D int32 tensor with shape [ k ].
+     * * 1: Keys. A 1-D int32 tensor with shape [ n ], *MUST* be sorted in
+     *      ascending order.
+     * * 2: Values. A tensor with shape [ n … ].
+     *
+     * Outputs:
+     * * 0: Output. A tensor with shape [ k …].
+     * * 1: Hits. A uint8 tensor with shape [ k ] indicates whether the lookup
+     *      hits or not.
+     */
+    HASHTABLE_LOOKUP = 10,
+
+    /**
+     * Applies L2 normalization along a the depth dimension.
+     *
+     * The values in output Tensor is computed as:
+     *     output[batch, row, col, channel] =
+     *         input[batch, row, col, channel] /
+     *         sqrt(sum_{c} pow(input[batch, row, col, c], 2))
+     *
+     * For x with more dimensions, independently normalizes each 1-D slice along dimension dim.
+     *
+     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     * Supported tensor rank: 4, with "NHWC" data layout.
+     *
+     * Inputs:
+     * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
+     *
+     * Ouputs:
+     * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
+     */
+    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 output Tensor is computed as:
+     *     output[batch, row, col, channel] =
+     *         sqrt(sum_{i, j} pow(input[batch, row + i, col + j, channel], 2) / sum(1))
+     *
+     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     * Supported tensor rank: 4, with "NHWC" data layout.
+     *
+     * Inputs:
+     * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
+     * 1: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
+     * 2: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
+     * 3: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
+     * 4: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
+     * 5: An INT32 value, specifying the output stride in the ‘width’ dimension.
+     * 6: An INT32 value, specifying the output stride in the ‘height’ dimension.
+     * 7: An INT32 value, specifying the filter width.
+     * 8: An INT32 value, specifying the filter height.
+     * 9: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+     *    Specifies the activation to invoke on the result of each addition.
+     *
+     * Ouputs:
+     * 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.
+     *
+     * In details:
+     *     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)
+     *
+     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     * Supported tensor rank: 4, with "NHWC" data layout.
+     *
+     * Inputs:
+     * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
+     * 1: An INT32 value, specifying the radius of the normalization window.
+     * 2: A FLOAT32 value, specifying the bias, must not be zero.
+     * 3: A FLOAT32 value, specifying the scale factor, alpha.
+     * 4: A FLOAT32 value, specifying the exponent, beta.
+     *
+     * Ouputs:
+     * 0: The output tensor of same shape as input0.
+     */
+    LOCAL_RESPONSE_NORMALIZATION = 13,
+
+    /**
+     * Computes sigmoid activation on the input tensor element-wise.
+     *
+     * In details:
+     *     output = 1 / (1 + exp(-input))
+     *
+     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor rank: up to 4.
+     *
+     * Inputs:
+     * 0: A tensor, specifying the input.
+     *
+     * Ouputs:
+     * 0: The output tensor of same shape as input0.
+     */
+    LOGISTIC = 14,
+
+    /**
+     * Projects an input to a bit vector via locality senstive hashing.
+     *
+     * Inputs:
+     * * 0: Hash functions. Dim.size == 2, DataType: Float.
+     *            Tensor[0].Dim[0]: Number of hash functions.
+     *            Tensor[0].Dim[1]: Number of seeds per hash functions.
+     *            Tensor[0].Dim[1] <= 32 in sparse case.
+     *
+     * * 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(=1).
+     *          Computed bit vector is considered to be sparse.
+     *          Each output element is an int32 made up of multiple bits computed from
+     *          hash functions.
+     *
+     *        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.
+     */
+    LSH_PROJECTION = 15,
+
+    /**
+     * Long short-term memory unit (LSTM) recurrent network layer.
+     *
+     * The default non-peephole implementation is based on:
+     * http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf
+     * S. Hochreiter and J. Schmidhuber. "Long Short-Term Memory". Neural
+     * Computation, 9(8):1735-1780, 1997.
+     *
+     * The peephole implementation 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.
+     *
+     * 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 class has the following independently optional inputs:
+     * * If input gate (if CIFG): “input_to_forget_weights”,
+     *   “recurrent_to_input_weights”, “cell_to_input_weights”, “input_gate_bias”.
+     * * If no peephole connections: “cell_to_input_weights”,
+     *   “cell_to_forget_weights”, “cell_to_output_weights”.
+     * * If no projection layer: “projection_weights” and “projection_bias”.
+     * * If no projection bias: “projection_bias”.
+     *
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     *
+     * Inputs:
+     * * 0: Input.
+     *      A 2-D tensor of type T, of shape [batch_size, input_size], where
+     *      “batch_size” corresponds to the batching dimension, and “input_size”
+     *      is the size of the input.
+     * * 1: input_to_input_weights.
+     *      A 2-D tensor of type T, of shape [num_units, input_size], where
+     *      “num_units” corresponds to the number of cell units.
+     * * 2: input_to_forget_weights.
+     *      A 2-D tensor of type T, of shape [num_units, input_size].
+     * * 3: input_to_cell_weights.
+     *      A 2-D tensor of type T, of shape [num_units, input_size].
+     * * 4: input_to_output_weights.
+     *      A 2-D tensor of type T, of shape [num_units, input_size].
+     * * 5: recurrent_to_input_weights.
+     *      A 2-D tensor of type T, 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: recurrent_to_forget_weights.
+     *      A 2-D tensor of type T, of shape [num_units, output_size].
+     * * 7: recurrent_to_cell_weights.
+     *      A 2-D tensor of type T, of shape [num_units, output_size].
+     * * 8: recurrent_to_output_weights.
+     *      A 2-D tensor of type T, of shape [num_units, output_size].
+     * * 9: cell_to_input_weights.
+     *      A 1-D tensor of type T, of shape [num_units].
+     * * 10:cell_to_forget_weights.
+     *      A 1-D tensor of type T, of shape [num_units].
+     * * 11:cell_to_output_weights.
+     *      A 1-D tensor of type T, of shape [num_units].
+     * * 12:input_gate_bias.
+     *      A 1-D tensor of type T, of shape [num_units].
+     * * 13:forget_gate_bias.
+     *      A 1-D tensor of type T, of shape [num_units].
+     * * 14:cell_bias.
+     *      A 1-D tensor of type T, of shape [num_units].
+     * * 15:output_gate_bias.
+     *      A 1-D tensor of type T, of shape [num_units].
+     * * 16:projection_weights.
+     *      A 2-D tensor of type T, of shape [output_size, num_units].
+     * * 17:projection_bias.
+     *      A 1-D tensor of type T, of shape [output_size].
+     *
+     * Parameters:
+     * * 18:fused_activation_function.
+     *      An (optional) ActivationFunctionType indicating the activation
+     *      function.
+     *      If “NONE” is specified then it results in a linear activation.
+     * * 19:cell_clip.
+     *      A 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.
+     * * 20:proj_clip.
+     *      A 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.
+     *
+     * Outputs:
+     * * 0: scratch_buffer.
+     *      A 3-D tensor of type T, of shape [batch_size, num_cell, 4].
+     * * 1: output_state.
+     *      A 2-D tensor of type T, of shape [batch_size, output_size].
+     * * 2: cell_state.
+     *      A 2-D tensor of type T, of shape [batch_size, num_units].
+     * * 3: output.
+     *      A 2-D tensor of type T, of shape [batch_size, output_size]. This is
+     *      effectively the same as the current “output_state” 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 output Tensor is computed as:
+     *     output[batch, row, col, channel] =
+     *         max_{i, j} (input[batch, row + i, col + j, channel])
+     *
+     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor rank: 4, with "NHWC" data layout.
+     *
+     * Inputs:
+     * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
+     * 1: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
+     * 2: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
+     * 3: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
+     * 4: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
+     * 5: An INT32 value, specifying the output stride in the ‘width’ dimension.
+     * 6: An INT32 value, specifying the output stride in the ‘height’ dimension.
+     * 7: An INT32 value, specifying the filter width.
+     * 8: An INT32 value, specifying the filter height.
+     * 9: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+     *    Specifies the activation to invoke on the result of each addition.
+     *
+     * Ouputs:
+     * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
+     */
+    MAX_POOL_2D = 17,
+
+    /**
+     * Multiplies two tensors, elment-wise.
+     *
+     * Takes two input tensors of identical type 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.
+     *
+     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     * Supported tensor rank: up to 4
+     *
+     * Inputs:
+     * 0: A tensor.
+     * 1: A tensor of the same type, and compatible dimensions as input0.
+     * 2: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+     *    Specifies the activation to invoke on the result of each addition.
+     *
+     * Ouputs:
+     * 0: The product, a tensor of the same type as input0.
+     */
+    MUL = 18,
+
+    /**
+     * Computes rectified linear activation on the input tensor element-wise.
+     *
+     * In details:
+     *     output = max(0, input)
+     *
+     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor rank: up to 4.
+     *
+     * Inputs:
+     * 0: A tensor, specifying the input.
+     *
+     * Ouputs:
+     * 0: The output tensor of same shape as input0.
+     */
+    RELU = 19,
+
+    /**
+     * Computes rectified linear 1 activation on the input tensor element-wise.
+     *
+     * In details:
+     *     output = min(1.f, max(-1.f, input))
+     *
+     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor rank: up to 4.
+     *
+     * Inputs:
+     * 0: A tensor, specifying the input.
+     *
+     * Ouputs:
+     * 0: The output tensor of same shape as input0.
+     */
+    RELU1 = 20,
+
+    /**
+     * Computes rectified linear 6 activation on the input tensor element-wise.
+     *
+     * In details:
+     *     output = min(6, max(0, input))
+     *
+     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor rank: up to 4.
+     *
+     * Inputs:
+     * 0: A tensor, specifying the input.
+     *
+     * Ouputs:
+     * 0: The output tensor of same shape 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 types: {@link OperandType::TENSOR_FLOAT32}
+     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor rank: up to 4.
+     *
+     * Inputs:
+     * 0: A tensor, specifying the tensor to be reshaped.
+     * 1: A 1-D tensor of type {@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.
+     *
+     * Ouputs:
+     * 0: The output tensor, of shape specified by the input shape.
+     */
+    RESHAPE = 22,
+
+    /**
+     * Resizes images to given size using the bilinear interpretation.
+     *
+     * Resized images will be distorted if their original aspect ratio is not the same as input.
+     *
+     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     * Supported tensor rank: 4, with "NHWC" data layout.
+     *
+     * Inputs:
+     * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
+     * 1: An INT32 value, specifying the output width of the output tensor.
+     * 2: An INT32 value, specifying the output height of the output tensor.
+     *
+     * Ouputs:
+     * 0: The output 4-D tensor, of shape [batches, new_height, new_width, depth].
+     */
+    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 types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     *
+     * Inputs:
+     * * 0: input.
+     *      A 2-D tensor of type T, 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 type T, of shape [num_units, input_size], where
+     *      “num_units” corresponds to the number of units.
+     * * 2: recurrent_weights.
+     *      A 2-D tensor of type T, of shape [num_units, num_units], with columns
+     *      corresponding to the weights from each unit.
+     * * 3: bias.
+     *      A 1-D tensor of type T, of shape [num_units].
+     *
+     *    For FLOAT32 input tensor, bias must also be FLOAT32.
+     *    For UINT8 input tensor, bias must be INT32.
+     *
+     * Parameters
+     * * 4: fused_activation_function.
+     *      An (optional) ActivationFunctionType indicating the activation
+     *      function. If “NONE” is specified then it results in a linear
+     *      activation.
+     *
+     * * 5: Hidden state.
+     *      A 2-D tensor of type T, of shape [batch_size, num_units].
+     *
+     * Outputs:
+     * * 0: output.
+     *      A 2-D tensor of type T, 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.
+     *
+     * In details:
+     *     output[batch, i] =
+     *         exp((input[batch, i] - max(input[batch, :])) * beta) /
+     *         sum_{k}{exp((input[batch, k] - max(input[batch, :])) * beta)}
+     *
+     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor rank: 2 or 4.
+     *
+     * Inputs:
+     * 0: A 2-D or 4-D tensor, specifying the tensor to be reshaped.
+     * 1: A FLOAT32 value, specifying the scaling factor for the exponent, beta.
+     *
+     * Ouputs:
+     * 0: The output tensor of same shape as input0.
+     */
+    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 types: {@link OperandType::TENSOR_FLOAT32}
+     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor rank: 4, with "NHWC" data layout.
+     *
+     * Inputs:
+     * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
+     * 1: An INT32 value, specifying the block_size. block_size must be >=1 and
+     *    block_size must be a divisor of both the input height and width.
+     *
+     * Ouputs:
+     * 0: The output 4-D tensor, of shape [batch, height/block_size, width/block_size,
+     *    depth*block_size*block_size].
+     */
+    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, "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 types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     *
+     * Inputs:
+     * * 0: input.
+     *      A 2-D tensor of type T, 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 type T, of shape [num_units, input_size], where
+     *      “num_units” corresponds to the number of units.
+     * * 2: weights_time.
+     *      A 2-D tensor of type T, of shape [num_units, memory_size], where
+     *      “memory_size” corresponds to the fixed-size of the memory.
+     * * 3: bias.
+     *      A optional 1-D tensor of type T, of shape [num_units].
+     *
+     *    For FLOAT32 input tensor, bias must also be FLOAT32.
+     *    For UINT8 input tensor, bias must be INT32.
+     *
+     * Parameters:
+     * * 4: rank.
+     *      The rank of the SVD approximation.
+     * * 5: fused_activation_function.
+     *      An (optional) ActivationFunctionType indicating the activation function.
+     *      If “NONE” is specified then it results in a linear activation.
+     *
+     * Outputs:
+     * * 0: state.
+     *      A 2-D tensor of type T, of shape [batch_size, (memory_size - 1) * num_units * rank].
+     * * 1: output.
+     *      A 2-D tensor of type T, of shape [batch_size, num_units].
+     */
+    SVDF = 27,
+
+    /**
+     * Computes hyperbolic tangent of input tensor element-wise.
+     *
+     * In details:
+     *     output = tanh(input)
+     *
+     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     * Supported tensor rank: up to 4.
+     *
+     * Inputs:
+     * 0: A tensor, specifying the input.
+     *
+     * Ouputs:
+     * 0: The output tensor of same shape as input0.
+     */
+    TANH = 28,
+
+    /**
+     * OEM specific operation.
+     *
+     * This operation is OEM specific. It should only be used for OEM applications.
+     */
+    OEM_OPERATION = 10000,
 };
 
-// Two special values that can be used instead of a regular poolIndex.
-enum LocationValues : uint32_t {
-    // The location will be specified at runtime. It's either a temporary
-    // variable, an input, or an output.
-    LOCATION_AT_RUN_TIME = 0xFFFFFFFF,
-    // The operand's value is stored in the
-    // TODO: Only for old
-    LOCATION_SAME_BLOCK = 0xFFFFFFFE
+/**
+ * Fused activation function types.
+ */
+enum FusedActivationFunc : int32_t {
+    NONE  = 0,
+    RELU  = 1,
+    RELU1 = 2,
+    RELU6 = 3,
 };
 
-// Status of a device.
-enum DeviceStatus : uint32_t {
+/**
+ * How an operand is used.
+ */
+enum OperandLifeTime : int32_t {
+    /**
+     * The operand is internal to the model.  It's created by an operation
+     * and consumed by other operations.
+     */
+    TEMPORARY_VARIABLE,
+
+    /**
+     * The operand is an input of the model. An operand can't be both
+     * input and output of a model.
+     */
+    MODEL_INPUT,
+
+    /**
+     * The operand is an output of the model.
+     */
+    MODEL_OUTPUT,
+
+    /**
+     * The operand is a constant found in Model.operandValues.
+     */
+    CONSTANT_COPY,
+
+    /**
+     * The operand is a constant that was specified via a Memory object.
+     */
+    CONSTANT_REFERENCE,
+};
+
+/**
+ * Status of a device.
+ */
+enum DeviceStatus : int32_t {
     AVAILABLE,
     BUSY,
     OFFLINE,
-    UNKNOWN  // Do we need this?
+    UNKNOWN,
 };
 
-// For the reference workload
-// Used by a driver to report its performance characteristics.
-// TODO revisit the data types and scales.
-struct PerformanceInfo {
-    float execTime;    // in nanoseconds
-    float powerUsage;  // in picoJoules
-};
-
+/**
+ * A typed operation.
+ */
 struct OperationTuple {
-    // The type of operation.
+    /**
+     * The type of operation.
+     */
     OperationType operationType;
-    // The input data type of operation.
+
+    /**
+     * The input data type of operation.
+     */
     OperandType operandType;
 };
 
-// The capabilities of a driver.
+/**
+ * Performance information for the reference workload.
+ *
+ * Used by a driver to report its performance characteristics.
+ */
+struct PerformanceInfo {
+    /**
+     * Execution time in nanoseconds.
+     */
+    float execTime;
+
+    /**
+     * Power usage in picoJoules.
+     */
+    float powerUsage;
+};
+
+/**
+ * The capabilities of a driver.
+ */
 struct Capabilities {
+    /**
+     * A collection of typed operations supported by the driver.
+     */
     vec<OperationTuple> supportedOperationTuples;
-    // TODO Do the same for baseline model IDs
+
+    /**
+     * Indicates whether a driver caches its prepared model for reuse the next
+     * time the application begins. This is useful because the model may have
+     * been prepared in a previous run.
+     *
+     * True if caching is supported, false otherwise.
+     */
     bool cachesCompilation;
-    // TODO revisit the data types and scales.
-    float bootupTime;  // in nanoseconds
-    PerformanceInfo float16Performance;
+
+    /**
+     * Driver performance when operating on float32 data.
+     */
     PerformanceInfo float32Performance;
+
+    /**
+     * Driver performance when operating on asymmetric 8-bit quantized data.
+     */
     PerformanceInfo quantized8Performance;
 };
 
-// Describes the location of a data object.
+/**
+ * Describes the location of a data object.
+ */
 struct DataLocation {
-    // The index of the memory pool where this location is found.
-    // Two special values can also be used.  See the LOCATION_* constants above.
+    /**
+     * The index of the memory pool where this location is found.
+     */
     uint32_t poolIndex;
-    // Offset in bytes from the start of the pool.
+
+    /**
+     * Offset in bytes from the start of the pool.
+     */
     uint32_t offset;
-    // The length of the data, in bytes.
+
+    /**
+     * The length of the data in bytes.
+     */
     uint32_t length;
 };
 
+/**
+ * Describes one operand of the model's graph.
+ */
 struct Operand {
+    /**
+     * Data type of the operand.
+     */
     OperandType type;
+
+    /**
+     * Dimensions of the operand.
+     */
     vec<uint32_t> dimensions;
 
-    // The number of operations that uses this operand as input.
-    // TODO It would be nice to track the actual consumers, e.g. vec<uint32_t> consumers;
+    /**
+     * The number of operations that use this operand as input.
+     */
     uint32_t numberOfConsumers;
 
+    /**
+     * Quantized scale of the operand.
+     *
+     * Only applicable if the operand is of type TENSOR_QUANT8_ASYMM.
+     */
     float scale;
+
+    /**
+     * Quantized zero-point offset of the operand.
+     *
+     * Only applicable if the operand is of type TENSOR_QUANT8_ASYMM.
+     */
     int32_t zeroPoint;
 
-    // Where to find the data for this operand.
+    /**
+     * How the operand is used.
+     */
+    OperandLifeTime lifetime;
+
+    /**
+     * Where to find the data for this operand.
+     * If the lifetime is TEMPORARY_VARIABLE, MODEL_INPUT, or MODEL_OUTPUT:
+     * - All the fields will 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_REFERENCE:
+     * - location.poolIndex is set.
+     * - location.offset is the offset in bytes into the specified pool.
+     * - location.length is set.
+     */
     DataLocation location;
 };
 
-// Describes one operation of the graph.
+/**
+ * Describes one operation of the model's graph.
+ */
 struct Operation {
-    // The tuple describing the operation type and input type.
+    /**
+     * The tuple describing the operation type and input type.
+     */
     OperationTuple opTuple;
-    // Describes the table that contains the indexes of the inputs of the
-    // operation. The offset is the index in the operandIndexes table.
+
+    /**
+     * Describes the table that contains the indexes of the inputs of the
+     * operation. The offset is the index in the operandIndexes table.
+     */
     vec<uint32_t> inputs;
-    // Describes the table that contains the indexes of the outputs of the
-    // operation. The offset is the index in the operandIndexes table.
+
+    /**
+     * Describes the table that contains the indexes of the outputs of the
+     * operation. The offset is the index in the operandIndexes table.
+     */
     vec<uint32_t> outputs;
 };
 
-struct InputOutputInfo {
+/**
+ * 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
+ * might not be known is the shape of the input tensors.
+ */
+struct Model {
+    /**
+     * All operands included in the model.
+     */
+    vec<Operand> operands;
+
+    /**
+     * All operations included in the model.
+     *
+     * The operations are sorted into execution order.
+     */
+    vec<Operation> operations;
+
+    /**
+     * Input indexes of the model.
+     *
+     * Each value corresponds to the index of the operand in "operands".
+     */
+    vec<uint32_t> inputIndexes;
+
+    /**
+     * Output indexes of the model.
+     *
+     * Each value corresponds to the index of the operand in "operands".
+     */
+    vec<uint32_t> outputIndexes;
+
+    /**
+     * A byte buffer containing operand data that were copied into the model.
+     */
+    vec<uint8_t> operandValues;
+
+    /**
+     * A collection of shared memory pools containing operand data that were
+     * registered by the model.
+     */
+    vec<memory> pools;
+};
+
+/**
+ * Metadata information specifying the location of the input or output data and
+ * any updates to the input or output operand.
+ */
+struct RequestArgument {
+    /**
+     * The location within one of the memory pools passed in the Request.
+     */
     DataLocation location;
-    // If dimensions.size() > 0, we have updated dimensions.
+
+    /**
+     * 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.
+     */
     vec<uint32_t> dimensions;
 };
 
-struct Model {
-    vec<Operand> operands;
-    vec<Operation> operations;
-    vec<uint32_t> inputIndexes;
-    vec<uint32_t> outputIndexes;
-    vec<uint8_t> operandValues;
+/**
+ * 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.
+ */
+struct 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.inputIndexes.
+     *   E.g., input[i] corresponds to Model.inputIndexes[i].
+     */
+    vec<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.outputIndexes.
+     *   E.g., output[i] corresponds to Model.outputIndexes[i].
+     */
+    vec<RequestArgument> outputs;
+
+    /**
+     * A collection of shared memory pools containing operand data for both the
+     * inputs and the outputs to a model.
+     */
     vec<memory> pools;
 };
 
-struct Request {
-    vec<InputOutputInfo> inputs;
-    vec<InputOutputInfo> outputs;
-    vec<memory> pools;
+/**
+ * Return status of a function.
+ */
+enum ErrorStatus : int32_t {
+    NONE,
+    DEVICE_UNAVAILABLE,
+    GENERAL_FAILURE,
+    OUTPUT_INSUFFICIENT_SIZE,
+    INVALID_ARGUMENT,
 };
diff --git a/neuralnetworks/1.0/vts/functional/Android.bp b/neuralnetworks/1.0/vts/functional/Android.bp
index d76b2c3..2318430 100644
--- a/neuralnetworks/1.0/vts/functional/Android.bp
+++ b/neuralnetworks/1.0/vts/functional/Android.bp
@@ -16,7 +16,11 @@
 
 cc_test {
     name: "VtsHalNeuralnetworksV1_0TargetTest",
-    srcs: ["VtsHalNeuralnetworksV1_0TargetTest.cpp"],
+    srcs: [
+        "Event.cpp",
+        "GeneratedTestHarness.cpp",
+        "VtsHalNeuralnetworksV1_0TargetTest.cpp",
+    ],
     defaults: ["VtsHalTargetTestDefaults"],
     static_libs: [
         "android.hardware.neuralnetworks@1.0",
@@ -24,4 +28,8 @@
         "android.hidl.memory@1.0",
         "libhidlmemory",
     ],
+    header_libs: [
+        "libneuralnetworks_generated_test_harness_headers",
+        "libneuralnetworks_generated_tests",
+    ],
 }
diff --git a/neuralnetworks/1.0/vts/functional/Event.cpp b/neuralnetworks/1.0/vts/functional/Event.cpp
new file mode 100644
index 0000000..efaacb3
--- /dev/null
+++ b/neuralnetworks/1.0/vts/functional/Event.cpp
@@ -0,0 +1,94 @@
+#include "Event.h"
+#include <android-base/logging.h>
+
+namespace android {
+namespace hardware {
+namespace neuralnetworks {
+namespace V1_0 {
+namespace implementation {
+
+Event::Event() : mStatus(Status::WAITING) {}
+
+Event::~Event() {
+    // Note that we cannot call Event::join_thread from here: Event is
+    // intended to be reference counted, and it is possible that the
+    // reference count drops to zero in the bound thread, causing the
+    // bound thread to call this destructor. If a thread tries to join
+    // itself, it throws an exception, producing a message like the
+    // following:
+    //
+    //     terminating with uncaught exception of type std::__1::system_error:
+    //     thread::join failed: Resource deadlock would occur
+}
+
+Return<void> Event::notify(ErrorStatus status) {
+    {
+        std::lock_guard<std::mutex> lock(mMutex);
+        mStatus = status == ErrorStatus::NONE ? Status::SUCCESS : Status::ERROR;
+        if (mStatus == Status::SUCCESS && mCallback != nullptr) {
+            bool success = mCallback();
+            if (!success) {
+                LOG(ERROR) << "Event::notify -- callback failed";
+            }
+        }
+    }
+    mCondition.notify_all();
+    return Void();
+}
+
+Event::Status Event::poll() {
+    std::lock_guard<std::mutex> lock(mMutex);
+    return mStatus;
+}
+
+Event::Status Event::wait() {
+    std::unique_lock<std::mutex> lock(mMutex);
+    mCondition.wait(lock, [this]{return mStatus != Status::WAITING;});
+    join_thread_locked();
+    return mStatus;
+}
+
+bool Event::on_finish(std::function<bool(void)> callback) {
+    std::lock_guard<std::mutex> lock(mMutex);
+    if (mCallback != nullptr) {
+        LOG(ERROR) << "Event::on_finish -- a callback has already been bound to this event";
+        return false;
+    }
+    if (callback == nullptr) {
+        LOG(ERROR) << "Event::on_finish -- the new callback is invalid";
+        return false;
+    }
+    mCallback = std::move(callback);
+    return true;
+}
+
+bool Event::bind_thread(std::thread&& asyncThread) {
+    std::lock_guard<std::mutex> lock(mMutex);
+    if (mThread.joinable()) {
+        LOG(ERROR) << "Event::bind_thread -- a thread has already been bound to this event";
+        return false;
+    }
+    if (!asyncThread.joinable()) {
+        LOG(ERROR) << "Event::bind_thread -- the new thread is not joinable";
+        return false;
+    }
+    mThread = std::move(asyncThread);
+    return true;
+}
+
+void Event::join_thread() {
+    std::lock_guard<std::mutex> lock(mMutex);
+    join_thread_locked();
+}
+
+void Event::join_thread_locked() {
+    if (mThread.joinable()) {
+        mThread.join();
+    }
+}
+
+}  // namespace implementation
+}  // namespace V1_0
+}  // namespace neuralnetworks
+}  // namespace hardware
+}  // namespace android
diff --git a/neuralnetworks/1.0/vts/functional/Event.h b/neuralnetworks/1.0/vts/functional/Event.h
new file mode 100644
index 0000000..7dd4070
--- /dev/null
+++ b/neuralnetworks/1.0/vts/functional/Event.h
@@ -0,0 +1,216 @@
+#ifndef ANDROID_HARDWARE_NEURALNETWORKS_V1_0_EVENT_H
+#define ANDROID_HARDWARE_NEURALNETWORKS_V1_0_EVENT_H
+
+#include <android/hardware/neuralnetworks/1.0/IEvent.h>
+#include <chrono>
+#include <condition_variable>
+#include <functional>
+#include <hidl/MQDescriptor.h>
+#include <hidl/Status.h>
+#include <mutex>
+#include <thread>
+
+namespace android {
+namespace hardware {
+namespace neuralnetworks {
+namespace V1_0 {
+namespace implementation {
+
+using ::android::hardware::hidl_array;
+using ::android::hardware::hidl_memory;
+using ::android::hardware::hidl_string;
+using ::android::hardware::hidl_vec;
+using ::android::hardware::Return;
+using ::android::hardware::Void;
+using ::android::sp;
+
+/**
+ * The Event class is used internally by the Neuralnetworks runtime to
+ * synchronize between different threads. An asynchronous task is launched
+ * paired with an event object. When a client thread requires the output being
+ * processed by the asynchronous task, the client thread can wait for the result
+ * and be blocked until it has completed or a timeout condition has been
+ * reached, or poll the result periodically. Both poll and wait* may safely be
+ * called concurrently, even on the same event. When the server thread has
+ * completed, it should immediately call "notify" to indicate the corresponding
+ * output has been produced and awaken any client threads waiting on the event.
+ *
+ * This class exists to enable synchronization across HIDL. When synchronization
+ * is only required in the same process, consider using std::future, std::mutex,
+ * std::condition_variable, or std::experimental::latch instead.
+ */
+struct Event : public IEvent {
+    Event();
+    ~Event() override;
+
+    /**
+     * Event::Status::WAITING -- The corresponding asynchronous execution has
+     *                           not yet finished.
+     * Event::Status::SUCCESS -- The corresponding asynchronous execution has
+     *                           succeeded and the output is ready to be
+     *                           consumed.
+     * Event::Status::TIMEOUT -- The calling thread has waited longer than the
+     *                           user has specified. This only applies to the
+     *                           methods Event::wait_for and Event::wait_until.
+     * Event::Status::ERROR   -- The corresponding asynchronous execution has
+     *                           failed to properly execute.
+     */
+    enum class Status : uint32_t {
+        WAITING,
+        SUCCESS,
+        TIMEOUT,
+        ERROR,
+    };
+
+    /**
+     * IEvent::notify marks the event with the return status of the
+     * asynchronous call the event is paired with and enables all
+     * prior and future wait calls on the Event object to proceed. The
+     * call to IEvent::notify happens before any wait* calls on
+     * this event return (except in the case of TIMEOUT) and before
+     * any poll calls that see the resulting status. The asynchronous
+     * call the event is paired with must ensure that any update to
+     * state that should be visible to the caller of wait* or poll
+     * happens before the call to IEvent::notify.
+     *
+     * IEvent::notify can be called at most once on a given event.
+     *
+     * @param neuralnetworks::V1_0::ErrorStatus ErrorStatus::NONE on success
+     */
+    Return<void> notify(ErrorStatus status) override;
+
+    /**
+     * Event::poll returns the current status of the event.
+     *
+     * @return Status SUCCESS, ERROR, or WAITING
+     */
+    Event::Status poll();
+
+    /**
+     * Event::wait blocks until the event has been signaled.
+     *
+     * @return Status SUCCESS or ERROR
+     */
+    Event::Status wait();
+
+    /**
+     * Event::wait_for blocks until the event has been signaled or the time
+     * duration from the time the wait_for function was called has expired,
+     * whichever comes first.
+     *
+     * @return Status SUCCESS, ERROR, or TIMEOUT
+     */
+    template<class Rep, class Period>
+    Event::Status wait_for(const std::chrono::duration<Rep,Period>& timeout_duration);
+
+    /**
+     * Event::wait_until blocks until the event has been signaled or a certain
+     * time has been reached, whichever comes first.
+     *
+     * @return Status SUCCESS, ERROR, or TIMEOUT
+     */
+    template<class Clock, class Duration>
+    Event::Status wait_until(const std::chrono::time_point<Clock,Duration>& timeout_duration);
+
+    /**
+     * Event::on_finish binds a callback function to the event. The
+     * callback will be executed when IEvent::notify is called, before
+     * any calls to wait* return. (Note that wait_for or wait_until
+     * can return TIMEOUT before IEvent::notify is called for the
+     * first time, and hence before the callback is executed.)
+     *
+     * The callback function must not synchronize with or otherwise
+     * access the event object it is bound to.
+     *
+     * Event::on_finish can be called at most once on a given event.
+     *
+     * @param callback Function to be invoked the first time IEvent::notify is
+     *                 called. Must have a target -- i.e., must not compare equal
+     *                 to nullptr. Callback returns true if it successfully
+     *                 completes, false if it fails.
+     * @return bool True if the callback was successfully bound, false if
+     *              unsuccessful.
+     *
+     * TODO: What if notify has already been called before on_finish?
+     * TODO: Why does the return value of the callback matter?
+     */
+    bool on_finish(std::function<bool(void)> callback);
+
+    /**
+     * Event::bind_thread binds a thread to the event for later use by
+     * Event::join_thread.
+     *
+     * The thread must be passed using std::move.
+     *
+     * Once a thread is bound with Event::bind_thread, the client code
+     * should ensure that one of the following occurs before the event is
+     * destroyed:
+     * - Event::join_thread has been called.
+     * - Event::wait has been called.
+     * - Event::wait_for has been called and returned other than TIMEOUT.
+     * - Event::wait_until has been called and returned other than TIMEOUT.
+     *
+     * The bound thread shall not call any Event method with the exception of
+     * IEvent::notify, which it will call when the thread has finished its
+     * computation.
+     *
+     * Event::bind_thread can be called at most once on a given event.
+     *
+     * @param asyncThread Thread to be bound to the event. The thread object
+     *                    must represent a thread of execution -- i.e.,
+     *                    asyncThread.joinable() must be true.
+     * @return bool True if successful, false if thread was not properly bound.
+     */
+    bool bind_thread(std::thread&& asyncThread);
+
+    /**
+     * Event::join_thread ensures that the thread (if any) bound to
+     * this event with Event::bind_thread has fully finished and
+     * cleaned its resources. It is legal to call this function
+     * multiple times, concurrently or sequentially.
+     */
+    void join_thread();
+
+ private:
+    // Same as Event::join_thread but assumes we already hold a lock on mMutex.
+    void join_thread_locked();
+
+    Status                    mStatus;
+    std::mutex                mMutex;
+    std::condition_variable   mCondition;
+    std::function<bool(void)> mCallback;
+    std::thread               mThread;
+};
+
+
+// template function implementations
+
+template<class Rep, class Period>
+Event::Status Event::wait_for(const std::chrono::duration<Rep,Period>& timeout_duration) {
+    std::unique_lock<std::mutex> lock(mMutex);
+    std::cv_status status = mCondition.wait_for(lock, timeout_duration,
+                                                [this]{return mStatus != Status::WAITING;});
+    if (status != std::cv_status::timeout) {
+        join_thread_locked();
+    }
+    return status != std::cv_status::timeout ? mStatus : Status::TIMEOUT;
+}
+
+template<class Clock, class Duration>
+Event::Status Event::wait_until(const std::chrono::time_point<Clock,Duration>& timeout_time) {
+    std::unique_lock<std::mutex> lock(mMutex);
+    std::cv_status status = mCondition.wait_until(lock, timeout_time,
+                                                  [this]{return mStatus != Status::WAITING;});
+    if (status != std::cv_status::timeout) {
+        join_thread_locked();
+    }
+    return status != std::cv_status::timeout ? mStatus : Status::TIMEOUT;
+}
+
+}  // namespace implementation
+}  // namespace V1_0
+}  // namespace neuralnetworks
+}  // namespace hardware
+}  // namespace android
+
+#endif  // ANDROID_HARDWARE_NEURALNETWORKS_V1_0_EVENT_H
diff --git a/neuralnetworks/1.0/vts/functional/GeneratedTestHarness.cpp b/neuralnetworks/1.0/vts/functional/GeneratedTestHarness.cpp
new file mode 100644
index 0000000..db90ac2
--- /dev/null
+++ b/neuralnetworks/1.0/vts/functional/GeneratedTestHarness.cpp
@@ -0,0 +1,203 @@
+/*
+ * Copyright (C) 2017 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.
+ */
+
+#include "Event.h"
+#include "TestHarness.h"
+#include "VtsHalNeuralnetworksV1_0TargetTest.h"
+
+#include <android-base/logging.h>
+#include <android/hidl/memory/1.0/IMemory.h>
+#include <hidlmemory/mapping.h>
+
+namespace android {
+namespace hardware {
+namespace neuralnetworks {
+namespace V1_0 {
+namespace vts {
+namespace functional {
+// allocator helper
+hidl_memory allocateSharedMemory(int64_t size, const std::string& type = "ashmem");
+
+namespace generated_tests {
+using ::android::hardware::neuralnetworks::V1_0::implementation::Event;
+using ::generated_tests::filter;
+using ::generated_tests::for_all;
+using ::generated_tests::for_each;
+using ::generated_tests::resize_accordingly;
+using ::generated_tests::MixedTyped;
+using ::generated_tests::MixedTypedExampleType;
+using ::generated_tests::Float32Operands;
+using ::generated_tests::Int32Operands;
+using ::generated_tests::Quant8Operands;
+// Top level driver for models and examples generated by test_generator.py
+// Test driver for those generated from ml/nn/runtime/test/spec
+void Execute(const sp<IDevice>& device, std::function<Model(void)> create_model,
+             std::function<bool(int)> is_ignored,
+             const std::vector<MixedTypedExampleType>& examples) {
+    Model model = create_model();
+    sp<IPreparedModel> preparedModel;
+    sp<Event> preparationEvent = new Event();
+    ASSERT_NE(nullptr, preparationEvent.get());
+    Return<void> prepareRet = device->prepareModel(
+        model, preparationEvent, [&](ErrorStatus status, const sp<IPreparedModel>& prepared) {
+            EXPECT_EQ(ErrorStatus::NONE, status);
+            preparedModel = prepared;
+        });
+    ASSERT_TRUE(prepareRet.isOk());
+    ASSERT_NE(nullptr, preparedModel.get());
+    Event::Status preparationStatus = preparationEvent->wait();
+    EXPECT_EQ(Event::Status::SUCCESS, preparationStatus);
+
+    const uint32_t INPUT = 0;
+    const uint32_t OUTPUT = 1;
+
+    int example_no = 1;
+    for (auto& example : examples) {
+        SCOPED_TRACE(example_no++);
+
+        const MixedTyped& inputs = example.first;
+        const MixedTyped& golden = example.second;
+
+        std::vector<RequestArgument> inputs_info, outputs_info;
+        uint32_t inputSize = 0, outputSize = 0;
+
+        // This function only partially specifies the metadata (vector of RequestArguments).
+        // The contents are copied over below.
+        for_all(inputs, [&inputs_info, &inputSize](int index, auto, auto s) {
+            if (inputs_info.size() <= static_cast<size_t>(index)) inputs_info.resize(index + 1);
+            RequestArgument arg = {
+                .location = {.poolIndex = INPUT, .offset = 0, .length = static_cast<uint32_t>(s)},
+                .dimensions = {},
+            };
+            inputs_info[index] = arg;
+            inputSize += s;
+        });
+        // Compute offset for inputs 1 and so on
+        {
+            size_t offset = 0;
+            for (auto& i : inputs_info) {
+                i.location.offset = offset;
+                offset += i.location.length;
+            }
+        }
+
+        MixedTyped test;  // holding test results
+
+        // Go through all outputs, initialize RequestArgument descriptors
+        resize_accordingly<float>(golden, test);
+        resize_accordingly<int32_t>(golden, test);
+        resize_accordingly<uint8_t>(golden, test);
+        for_all(golden, [&outputs_info, &outputSize](int index, auto, auto s) {
+            if (outputs_info.size() <= static_cast<size_t>(index)) outputs_info.resize(index + 1);
+            RequestArgument arg = {
+                .location = {.poolIndex = OUTPUT, .offset = 0, .length = static_cast<uint32_t>(s)},
+                .dimensions = {},
+            };
+            outputs_info[index] = arg;
+            outputSize += s;
+        });
+        // Compute offset for outputs 1 and so on
+        {
+            size_t offset = 0;
+            for (auto& i : outputs_info) {
+                i.location.offset = offset;
+                offset += i.location.length;
+            }
+        }
+        std::vector<hidl_memory> pools = {allocateSharedMemory(inputSize),
+                                          allocateSharedMemory(outputSize)};
+        ASSERT_NE(0ull, pools[INPUT].size());
+        ASSERT_NE(0ull, pools[OUTPUT].size());
+
+        // load data
+        sp<IMemory> inputMemory = mapMemory(pools[INPUT]);
+        sp<IMemory> outputMemory = mapMemory(pools[OUTPUT]);
+        ASSERT_NE(nullptr, inputMemory.get());
+        ASSERT_NE(nullptr, outputMemory.get());
+        char* inputPtr = reinterpret_cast<char*>(static_cast<void*>(inputMemory->getPointer()));
+        char* outputPtr = reinterpret_cast<char*>(static_cast<void*>(outputMemory->getPointer()));
+        ASSERT_NE(nullptr, inputPtr);
+        ASSERT_NE(nullptr, outputPtr);
+        inputMemory->update();
+        outputMemory->update();
+
+        // Go through all inputs, copy the values
+        for_all(inputs, [&inputs_info, inputPtr](int index, auto p, auto s) {
+            char* begin = (char*)p;
+            char* end = begin + s;
+            // TODO: handle more than one input
+            std::copy(begin, end, inputPtr + inputs_info[index].location.offset);
+        });
+
+        inputMemory->commit();
+        outputMemory->commit();
+        // execute request
+        sp<Event> executionEvent = new Event();
+        ASSERT_NE(nullptr, executionEvent.get());
+        Return<ErrorStatus> executeStatus = preparedModel->execute(
+            {.inputs = inputs_info, .outputs = outputs_info, .pools = pools}, executionEvent);
+        ASSERT_TRUE(executeStatus.isOk());
+        EXPECT_EQ(ErrorStatus::NONE, static_cast<ErrorStatus>(executeStatus));
+        Event::Status eventStatus = executionEvent->wait();
+        EXPECT_EQ(Event::Status::SUCCESS, eventStatus);
+
+        // validate results
+        outputMemory->read();
+#define COPY_BACK(ty)                                                              \
+    for_each<ty>(test, [&outputs_info, outputPtr](int index, std::vector<ty>& m) { \
+        RequestArgument& i = outputs_info[index];                                  \
+        ASSERT_EQ(m.size(), i.location.length / sizeof(ty));                       \
+        char* begin = outputPtr + i.location.offset;                               \
+        memcpy(m.data(), begin, i.location.length);                                \
+    });
+        COPY_BACK(float);
+        COPY_BACK(int32_t);
+        COPY_BACK(uint8_t);
+#undef COPY_BACK
+        outputMemory->commit();
+        // Filter out don't cares
+        MixedTyped filtered_golden;
+        MixedTyped filtered_test;
+        filter<float>(golden, &filtered_golden, is_ignored);
+        filter<float>(test, &filtered_test, is_ignored);
+        filter<int32_t>(golden, &filtered_golden, is_ignored);
+        filter<int32_t>(test, &filtered_test, is_ignored);
+        filter<uint8_t>(golden, &filtered_golden, is_ignored);
+        filter<uint8_t>(test, &filtered_test, is_ignored);
+
+        // We want "close-enough" results for float
+        for_each<float>(filtered_golden, [&filtered_test](int index, auto& golden_float) {
+            auto& test_float_operands = std::get<Float32Operands>(filtered_test);
+            auto& test_float = test_float_operands[index];
+            for (unsigned int i = 0; i < golden_float.size(); i++) {
+                SCOPED_TRACE(i);
+                EXPECT_NEAR(golden_float[i], test_float[i], 1.e-5);
+            }
+        });
+        EXPECT_EQ(std::get<Int32Operands>(filtered_golden), std::get<Int32Operands>(filtered_test));
+        EXPECT_EQ(std::get<Quant8Operands>(filtered_golden),
+                  std::get<Quant8Operands>(filtered_test));
+    }
+}
+
+}  // namespace generated_tests
+
+}  // namespace functional
+}  // namespace vts
+}  // namespace V1_0
+}  // namespace neuralnetworks
+}  // namespace hardware
+}  // namespace android
diff --git a/neuralnetworks/1.0/vts/functional/VtsHalNeuralnetworksV1_0TargetTest.cpp b/neuralnetworks/1.0/vts/functional/VtsHalNeuralnetworksV1_0TargetTest.cpp
index 84e67cb..453e3e5 100644
--- a/neuralnetworks/1.0/vts/functional/VtsHalNeuralnetworksV1_0TargetTest.cpp
+++ b/neuralnetworks/1.0/vts/functional/VtsHalNeuralnetworksV1_0TargetTest.cpp
@@ -17,10 +17,12 @@
 #define LOG_TAG "neuralnetworks_hidl_hal_test"
 
 #include "VtsHalNeuralnetworksV1_0TargetTest.h"
+#include "Event.h"
+#include "TestHarness.h"
+
 #include <android-base/logging.h>
 #include <android/hidl/memory/1.0/IMemory.h>
 #include <hidlmemory/mapping.h>
-#include <string>
 
 namespace android {
 namespace hardware {
@@ -29,9 +31,18 @@
 namespace vts {
 namespace functional {
 
+using ::android::hardware::neuralnetworks::V1_0::implementation::Event;
+using ::generated_tests::MixedTypedExampleType;
+namespace generated_tests {
+extern void Execute(const sp<IDevice>&, std::function<Model(void)>, std::function<bool(int)>,
+                    const std::vector<MixedTypedExampleType>&);
+}
+
 // A class for test environment setup
 NeuralnetworksHidlEnvironment::NeuralnetworksHidlEnvironment() {}
 
+NeuralnetworksHidlEnvironment::~NeuralnetworksHidlEnvironment() {}
+
 NeuralnetworksHidlEnvironment* NeuralnetworksHidlEnvironment::getInstance() {
     // This has to return a "new" object because it is freed inside
     // ::testing::AddGlobalTestEnvironment when the gtest is being torn down
@@ -44,6 +55,8 @@
 }
 
 // The main test class for NEURALNETWORK HIDL HAL.
+NeuralnetworksHidlTest::~NeuralnetworksHidlTest() {}
+
 void NeuralnetworksHidlTest::SetUp() {
     device = ::testing::VtsHalHidlTargetTestBase::getService<IDevice>(
         NeuralnetworksHidlEnvironment::getInstance());
@@ -57,24 +70,24 @@
 
 // status test
 TEST_F(NeuralnetworksHidlTest, StatusTest) {
-    DeviceStatus status = device->getStatus();
-    EXPECT_EQ(DeviceStatus::AVAILABLE, status);
+    Return<DeviceStatus> status = device->getStatus();
+    ASSERT_TRUE(status.isOk());
+    EXPECT_EQ(DeviceStatus::AVAILABLE, static_cast<DeviceStatus>(status));
 }
 
 // initialization
-TEST_F(NeuralnetworksHidlTest, InitializeTest) {
-    Return<void> ret = device->initialize([](const Capabilities& capabilities) {
-        EXPECT_NE(nullptr, capabilities.supportedOperationTuples.data());
-        EXPECT_NE(0ull, capabilities.supportedOperationTuples.size());
-        EXPECT_EQ(0u, static_cast<uint32_t>(capabilities.cachesCompilation) & ~0x1);
-        EXPECT_LT(0.0f, capabilities.bootupTime);
-        EXPECT_LT(0.0f, capabilities.float16Performance.execTime);
-        EXPECT_LT(0.0f, capabilities.float16Performance.powerUsage);
-        EXPECT_LT(0.0f, capabilities.float32Performance.execTime);
-        EXPECT_LT(0.0f, capabilities.float32Performance.powerUsage);
-        EXPECT_LT(0.0f, capabilities.quantized8Performance.execTime);
-        EXPECT_LT(0.0f, capabilities.quantized8Performance.powerUsage);
-    });
+TEST_F(NeuralnetworksHidlTest, GetCapabilitiesTest) {
+    Return<void> ret =
+        device->getCapabilities([](ErrorStatus status, const Capabilities& capabilities) {
+            EXPECT_EQ(ErrorStatus::NONE, status);
+            EXPECT_NE(nullptr, capabilities.supportedOperationTuples.data());
+            EXPECT_NE(0ull, capabilities.supportedOperationTuples.size());
+            EXPECT_EQ(0u, static_cast<uint32_t>(capabilities.cachesCompilation) & ~0x1);
+            EXPECT_LT(0.0f, capabilities.float32Performance.execTime);
+            EXPECT_LT(0.0f, capabilities.float32Performance.powerUsage);
+            EXPECT_LT(0.0f, capabilities.quantized8Performance.execTime);
+            EXPECT_LT(0.0f, capabilities.quantized8Performance.powerUsage);
+        });
     EXPECT_TRUE(ret.isOk());
 }
 
@@ -96,9 +109,8 @@
             .numberOfConsumers = 1,
             .scale = 0.0f,
             .zeroPoint = 0,
-            .location = {.poolIndex = static_cast<uint32_t>(LocationValues::LOCATION_AT_RUN_TIME),
-                         .offset = 0,
-                         .length = 0},
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
         },
         {
             .type = OperandType::TENSOR_FLOAT32,
@@ -106,9 +118,8 @@
             .numberOfConsumers = 1,
             .scale = 0.0f,
             .zeroPoint = 0,
-            .location = {.poolIndex = static_cast<uint32_t>(LocationValues::LOCATION_SAME_BLOCK),
-                         .offset = 0,
-                         .length = size},
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 0, .length = size},
         },
         {
             .type = OperandType::INT32,
@@ -116,9 +127,8 @@
             .numberOfConsumers = 1,
             .scale = 0.0f,
             .zeroPoint = 0,
-            .location = {.poolIndex = static_cast<uint32_t>(LocationValues::LOCATION_SAME_BLOCK),
-                         .offset = size,
-                         .length = sizeof(int32_t)},
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = size, .length = sizeof(int32_t)},
         },
         {
             .type = OperandType::TENSOR_FLOAT32,
@@ -126,9 +136,8 @@
             .numberOfConsumers = 0,
             .scale = 0.0f,
             .zeroPoint = 0,
-            .location = {.poolIndex = static_cast<uint32_t>(LocationValues::LOCATION_AT_RUN_TIME),
-                         .offset = 0,
-                         .length = 0},
+            .lifetime = OperandLifeTime::MODEL_OUTPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
         },
     };
 
@@ -143,7 +152,7 @@
     std::vector<uint8_t> operandValues(
         reinterpret_cast<const uint8_t*>(operand2Data.data()),
         reinterpret_cast<const uint8_t*>(operand2Data.data()) + size);
-    int32_t activation[1] = {0};
+    int32_t activation[1] = {static_cast<int32_t>(FusedActivationFunc::NONE)};
     operandValues.insert(operandValues.end(), reinterpret_cast<const uint8_t*>(&activation[0]),
                          reinterpret_cast<const uint8_t*>(&activation[1]));
 
@@ -158,6 +167,7 @@
         .pools = pools,
     };
 }
+}  // anonymous namespace
 
 // allocator helper
 hidl_memory allocateSharedMemory(int64_t size, const std::string& type = "ashmem") {
@@ -178,16 +188,16 @@
 
     return memory;
 }
-}  // anonymous namespace
 
 // supported subgraph test
-TEST_F(NeuralnetworksHidlTest, SupportedSubgraphTest) {
+TEST_F(NeuralnetworksHidlTest, SupportedOperationsTest) {
     Model model = createTestModel();
-    std::vector<bool> supported;
-    Return<void> ret = device->getSupportedSubgraph(
-        model, [&](const hidl_vec<bool>& hidl_supported) { supported = hidl_supported; });
-    ASSERT_TRUE(ret.isOk());
-    EXPECT_EQ(/*model.operations.size()*/ 0ull, supported.size());
+    Return<void> ret = device->getSupportedOperations(
+        model, [&](ErrorStatus status, const hidl_vec<bool>& supported) {
+            EXPECT_EQ(ErrorStatus::NONE, status);
+            EXPECT_EQ(model.operations.size(), supported.size());
+        });
+    EXPECT_TRUE(ret.isOk());
 }
 
 // execute simple graph
@@ -198,18 +208,28 @@
     const uint32_t INPUT = 0;
     const uint32_t OUTPUT = 1;
 
-    // prpeare request
+    // prepare request
     Model model = createTestModel();
-    sp<IPreparedModel> preparedModel = device->prepareModel(model);
+    sp<IPreparedModel> preparedModel;
+    sp<Event> preparationEvent = new Event();
+    ASSERT_NE(nullptr, preparationEvent.get());
+    Return<void> prepareRet = device->prepareModel(
+        model, preparationEvent, [&](ErrorStatus status, const sp<IPreparedModel>& prepared) {
+            EXPECT_EQ(ErrorStatus::NONE, status);
+            preparedModel = prepared;
+        });
+    ASSERT_TRUE(prepareRet.isOk());
     ASSERT_NE(nullptr, preparedModel.get());
+    Event::Status preparationStatus = preparationEvent->wait();
+    EXPECT_EQ(Event::Status::SUCCESS, preparationStatus);
 
     // prepare inputs
     uint32_t inputSize = static_cast<uint32_t>(inputData.size() * sizeof(float));
     uint32_t outputSize = static_cast<uint32_t>(outputData.size() * sizeof(float));
-    std::vector<InputOutputInfo> inputs = {{
+    std::vector<RequestArgument> inputs = {{
         .location = {.poolIndex = INPUT, .offset = 0, .length = inputSize}, .dimensions = {},
     }};
-    std::vector<InputOutputInfo> outputs = {{
+    std::vector<RequestArgument> outputs = {{
         .location = {.poolIndex = OUTPUT, .offset = 0, .length = outputSize}, .dimensions = {},
     }};
     std::vector<hidl_memory> pools = {allocateSharedMemory(inputSize),
@@ -226,21 +246,40 @@
     float* outputPtr = reinterpret_cast<float*>(static_cast<void*>(outputMemory->getPointer()));
     ASSERT_NE(nullptr, inputPtr);
     ASSERT_NE(nullptr, outputPtr);
+    inputMemory->update();
+    outputMemory->update();
     std::copy(inputData.begin(), inputData.end(), inputPtr);
     std::copy(outputData.begin(), outputData.end(), outputPtr);
     inputMemory->commit();
     outputMemory->commit();
 
     // execute request
-    bool success = preparedModel->execute({.inputs = inputs, .outputs = outputs, .pools = pools});
-    EXPECT_TRUE(success);
+    sp<Event> executionEvent = new Event();
+    ASSERT_NE(nullptr, executionEvent.get());
+    Return<ErrorStatus> executeStatus = preparedModel->execute(
+        {.inputs = inputs, .outputs = outputs, .pools = pools}, executionEvent);
+    ASSERT_TRUE(executeStatus.isOk());
+    EXPECT_EQ(ErrorStatus::NONE, static_cast<ErrorStatus>(executeStatus));
+    Event::Status eventStatus = executionEvent->wait();
+    EXPECT_EQ(Event::Status::SUCCESS, eventStatus);
 
     // validate results { 1+5, 2+6, 3+7, 4+8 }
-    outputMemory->update();
+    outputMemory->read();
     std::copy(outputPtr, outputPtr + outputData.size(), outputData.begin());
+    outputMemory->commit();
     EXPECT_EQ(expectedData, outputData);
 }
 
+// Mixed-typed examples
+typedef MixedTypedExampleType MixedTypedExample;
+
+// in frameworks/ml/nn/runtime/tests/generated/
+#include "all_generated_vts_tests.cpp"
+
+// TODO: Add tests for execution failure, or wait_for/wait_until timeout.
+//       Discussion:
+//       https://googleplex-android-review.git.corp.google.com/#/c/platform/hardware/interfaces/+/2654636/5/neuralnetworks/1.0/vts/functional/VtsHalNeuralnetworksV1_0TargetTest.cpp@222
+
 }  // namespace functional
 }  // namespace vts
 }  // namespace V1_0
diff --git a/neuralnetworks/1.0/vts/functional/VtsHalNeuralnetworksV1_0TargetTest.h b/neuralnetworks/1.0/vts/functional/VtsHalNeuralnetworksV1_0TargetTest.h
index bb0cdaa..9c56e6a 100644
--- a/neuralnetworks/1.0/vts/functional/VtsHalNeuralnetworksV1_0TargetTest.h
+++ b/neuralnetworks/1.0/vts/functional/VtsHalNeuralnetworksV1_0TargetTest.h
@@ -31,6 +31,7 @@
 using ::android::hardware::neuralnetworks::V1_0::IPreparedModel;
 using ::android::hardware::neuralnetworks::V1_0::Capabilities;
 using ::android::hardware::neuralnetworks::V1_0::DeviceStatus;
+using ::android::hardware::neuralnetworks::V1_0::FusedActivationFunc;
 using ::android::hardware::neuralnetworks::V1_0::Model;
 using ::android::hardware::neuralnetworks::V1_0::OperationType;
 using ::android::hardware::neuralnetworks::V1_0::PerformanceInfo;
@@ -59,15 +60,17 @@
     NeuralnetworksHidlEnvironment& operator=(NeuralnetworksHidlEnvironment&&) = delete;
 
    public:
+    ~NeuralnetworksHidlEnvironment() override;
     static NeuralnetworksHidlEnvironment* getInstance();
-    virtual void registerTestServices() override;
+    void registerTestServices() override;
 };
 
 // The main test class for NEURALNETWORKS HIDL HAL.
 class NeuralnetworksHidlTest : public ::testing::VtsHalHidlTargetTestBase {
    public:
-    virtual void SetUp() override;
-    virtual void TearDown() override;
+    ~NeuralnetworksHidlTest() override;
+    void SetUp() override;
+    void TearDown() override;
 
     sp<IDevice> device;
 };
diff --git a/power/1.1/default/Android.bp b/power/1.1/default/Android.bp
deleted file mode 100644
index 0b3598b..0000000
--- a/power/1.1/default/Android.bp
+++ /dev/null
@@ -1,33 +0,0 @@
-// Copyright (C) 2016 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.
-
-cc_binary {
-    proprietary: true,
-    defaults: ["hidl_defaults"],
-    relative_install_path: "hw",
-    name: "android.hardware.power@1.1-service",
-    init_rc: ["android.hardware.power@1.1-service.rc"],
-    srcs: ["service.cpp" , "Power.cpp"],
-
-    shared_libs: [
-        "liblog",
-        "libdl",
-        "libutils",
-        "libhardware",
-        "libhidlbase",
-        "libhidltransport",
-        "android.hardware.power@1.0",
-        "android.hardware.power@1.1",
-    ],
-}
diff --git a/power/1.1/default/Power.cpp b/power/1.1/default/Power.cpp
deleted file mode 100644
index b5d0c84..0000000
--- a/power/1.1/default/Power.cpp
+++ /dev/null
@@ -1,177 +0,0 @@
-/*
- * Copyright (C) 2016 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.
- */
-
-#define LOG_TAG "android.hardware.power@1.1-impl"
-
-#include <log/log.h>
-
-#include <hardware/hardware.h>
-#include <hardware/power.h>
-
-#include "Power.h"
-
-namespace android {
-namespace hardware {
-namespace power {
-namespace V1_1 {
-namespace implementation {
-
-using ::android::hardware::power::V1_0::Feature;
-using ::android::hardware::power::V1_0::PowerHint;
-using ::android::hardware::power::V1_0::PowerStatePlatformSleepState;
-using ::android::hardware::power::V1_0::Status;
-using ::android::hardware::power::V1_1::PowerStateSubsystem;
-using ::android::hardware::hidl_vec;
-using ::android::hardware::Return;
-using ::android::hardware::Void;
-
-Power::Power(power_module_t *module) : mModule(module) {
-    if (mModule)
-        mModule->init(mModule);
-}
-
-Power::~Power() {
-    delete(mModule);
-}
-
-// Methods from ::android::hardware::power::V1_0::IPower follow.
-Return<void> Power::setInteractive(bool interactive)  {
-    if (mModule->setInteractive)
-        mModule->setInteractive(mModule, interactive ? 1 : 0);
-    return Void();
-}
-
-Return<void> Power::powerHint(PowerHint hint, int32_t data)  {
-    int32_t param = data;
-    if (mModule->powerHint) {
-        if (data)
-            mModule->powerHint(mModule, static_cast<power_hint_t>(hint), &param);
-        else
-            mModule->powerHint(mModule, static_cast<power_hint_t>(hint), NULL);
-    }
-    return Void();
-}
-
-Return<void> Power::setFeature(Feature feature, bool activate)  {
-    if (mModule->setFeature)
-        mModule->setFeature(mModule, static_cast<feature_t>(feature),
-                activate ? 1 : 0);
-    return Void();
-}
-
-Return<void> Power::getPlatformLowPowerStats(getPlatformLowPowerStats_cb _hidl_cb)  {
-    hidl_vec<PowerStatePlatformSleepState> states;
-    ssize_t number_platform_modes;
-    size_t *voters = nullptr;
-    power_state_platform_sleep_state_t *legacy_states = nullptr;
-    int ret;
-
-    if (mModule->get_number_of_platform_modes == nullptr ||
-            mModule->get_voter_list == nullptr ||
-            mModule->get_platform_low_power_stats == nullptr)
-    {
-        _hidl_cb(states, Status::SUCCESS);
-        return Void();
-    }
-
-    number_platform_modes = mModule->get_number_of_platform_modes(mModule);
-    if (number_platform_modes)
-    {
-       if ((ssize_t) (SIZE_MAX / sizeof(size_t)) <= number_platform_modes)  // overflow
-           goto done;
-       voters = new (std::nothrow) size_t [number_platform_modes];
-       if (voters == nullptr)
-           goto done;
-
-       ret = mModule->get_voter_list(mModule, voters);
-       if (ret != 0)
-           goto done;
-
-       if ((ssize_t) (SIZE_MAX / sizeof(power_state_platform_sleep_state_t))
-           <= number_platform_modes)  // overflow
-           goto done;
-       legacy_states = new (std::nothrow)
-           power_state_platform_sleep_state_t [number_platform_modes];
-       if (legacy_states == nullptr)
-           goto done;
-
-       for (int i = 0; i < number_platform_modes; i++)
-       {
-          legacy_states[i].voters = nullptr;
-          legacy_states[i].voters = new power_state_voter_t [voters[i]];
-          if (legacy_states[i].voters == nullptr)
-              goto done;
-       }
-
-       ret = mModule->get_platform_low_power_stats(mModule, legacy_states);
-       if (ret != 0)
-           goto done;
-
-       states.resize(number_platform_modes);
-       for (int i = 0; i < number_platform_modes; i++)
-       {
-          power_state_platform_sleep_state_t& legacy_state = legacy_states[i];
-          PowerStatePlatformSleepState& state = states[i];
-          state.name = legacy_state.name;
-          state.residencyInMsecSinceBoot = legacy_state.residency_in_msec_since_boot;
-          state.totalTransitions = legacy_state.total_transitions;
-          state.supportedOnlyInSuspend = legacy_state.supported_only_in_suspend;
-          state.voters.resize(voters[i]);
-          for(size_t j = 0; j < voters[i]; j++)
-          {
-              state.voters[j].name = legacy_state.voters[j].name;
-              state.voters[j].totalTimeInMsecVotedForSinceBoot = legacy_state.voters[j].total_time_in_msec_voted_for_since_boot;
-              state.voters[j].totalNumberOfTimesVotedSinceBoot = legacy_state.voters[j].total_number_of_times_voted_since_boot;
-          }
-       }
-    }
-done:
-    if (legacy_states)
-    {
-        for (int i = 0; i < number_platform_modes; i++)
-        {
-            if(legacy_states[i].voters)
-                delete(legacy_states[i].voters);
-        }
-    }
-    delete[] legacy_states;
-    delete[] voters;
-    _hidl_cb(states, Status::SUCCESS);
-    return Void();
-}
-
-// Methods from ::android::hardware::power::V1_1::IPower follow.
-Return<void> Power::getSubsystemLowPowerStats(getSubsystemLowPowerStats_cb _hidl_cb) {
-    hidl_vec<PowerStateSubsystem> subsystems;
-    ssize_t number_subsystems = 0;
-
-    //This API will report zero subsystems to support older devices
-    //For devices that support this API, they will have their own implementation
-    subsystems.resize(number_subsystems);
-    _hidl_cb(subsystems, Status::SUCCESS);
-    return Void();
-}
-
-Return<void> Power::powerHintAsync(PowerHint hint, int32_t data) {
-    // just call the normal power hint in this oneway function
-    return powerHint(hint, data);
-}
-
-} // namespace implementation
-}  // namespace V1_1
-}  // namespace power
-}  // namespace hardware
-}  // namespace android
diff --git a/power/1.1/default/Power.h b/power/1.1/default/Power.h
deleted file mode 100644
index e779d64..0000000
--- a/power/1.1/default/Power.h
+++ /dev/null
@@ -1,63 +0,0 @@
-/*
- * Copyright (C) 2016 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.
- */
-
-#ifndef ANDROID_HARDWARE_POWER_V1_1_POWER_H
-#define ANDROID_HARDWARE_POWER_V1_1_POWER_H
-
-#include <android/hardware/power/1.1/IPower.h>
-#include <hidl/MQDescriptor.h>
-#include <hidl/Status.h>
-#include <hardware/power.h>
-
-namespace android {
-namespace hardware {
-namespace power {
-namespace V1_1 {
-namespace implementation {
-
-using ::android::hardware::power::V1_0::Feature;
-using ::android::hardware::power::V1_0::PowerHint;
-using ::android::hardware::power::V1_1::IPower;
-using ::android::hardware::Return;
-using ::android::hardware::Void;
-
-struct Power : public IPower {
-    Power(power_module_t* module);
-    ~Power();
-
-    // Methods from ::android::hardware::power::V1_0::IPower follow
-    Return<void> setInteractive(bool interactive) override;
-    Return<void> powerHint(PowerHint hint, int32_t data) override;
-    Return<void> setFeature(Feature feature, bool activate) override;
-    Return<void> getPlatformLowPowerStats(getPlatformLowPowerStats_cb _hidl_cb) override;
-
-    // Methods from ::android::hardware::power::V1_1::IPower follow.
-    Return<void> getSubsystemLowPowerStats(getSubsystemLowPowerStats_cb _hidl_cb) override;
-    Return<void> powerHintAsync(PowerHint hint, int32_t data) override;
-
-    // Methods from ::android::hidl::base::V1_0::IBase follow.
-
-  private:
-    power_module_t* mModule;
-};
-
-}  // namespace implementation
-}  // namespace V1_1
-}  // namespace power
-}  // namespace hardware
-}  // namespace android
-
-#endif  // ANDROID_HARDWARE_POWER_V1_1_POWER_H
diff --git a/power/1.1/default/android.hardware.power@1.1-service.rc b/power/1.1/default/android.hardware.power@1.1-service.rc
deleted file mode 100644
index f2512f1..0000000
--- a/power/1.1/default/android.hardware.power@1.1-service.rc
+++ /dev/null
@@ -1,4 +0,0 @@
-service power-hal-1-1 /vendor/bin/hw/android.hardware.power@1.1-service
-    class hal
-    user system
-    group system
diff --git a/power/1.1/default/service.cpp b/power/1.1/default/service.cpp
deleted file mode 100644
index 571db2f..0000000
--- a/power/1.1/default/service.cpp
+++ /dev/null
@@ -1,89 +0,0 @@
-/*
- * Copyright (C) 2016 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.
- */
-
-#define LOG_TAG "android.hardware.power@1.1-service"
-
-#include <android/log.h>
-#include <hidl/HidlTransportSupport.h>
-#include <android/hardware/power/1.1/IPower.h>
-#include <hardware/power.h>
-#include "Power.h"
-
-using android::sp;
-using android::status_t;
-using android::OK;
-
-// libhwbinder:
-using android::hardware::configureRpcThreadpool;
-using android::hardware::joinRpcThreadpool;
-
-// Generated HIDL files
-using android::hardware::power::V1_1::IPower;
-using android::hardware::power::V1_1::implementation::Power;
-
-int main() {
-
-    status_t status;
-    android::sp<IPower> service = nullptr;
-    const hw_module_t* hw_module = nullptr;
-    power_module_t* power_module = nullptr;
-    int err;
-
-    ALOGI("Power HAL Service 1.1 (Default) is starting.");
-
-    err = hw_get_module(POWER_HARDWARE_MODULE_ID, &hw_module);
-    if (err) {
-        ALOGE("hw_get_module %s failed: %d", POWER_HARDWARE_MODULE_ID, err);
-        goto shutdown;
-    }
-
-    if (!hw_module->methods || !hw_module->methods->open) {
-        power_module = reinterpret_cast<power_module_t*>(
-            const_cast<hw_module_t*>(hw_module));
-    } else {
-        err = hw_module->methods->open(hw_module, POWER_HARDWARE_MODULE_ID,
-                                           reinterpret_cast<hw_device_t**>(&power_module));
-        if (err) {
-            ALOGE("Passthrough failed to load legacy HAL.");
-            goto shutdown;
-        }
-    }
-
-    service = new Power(power_module);
-    if (service == nullptr) {
-        ALOGE("Can not create an instance of Power HAL Iface, exiting.");
-
-        goto shutdown;
-    }
-
-    configureRpcThreadpool(1, true /*callerWillJoin*/);
-
-    status = service->registerAsService();
-    if (status != OK) {
-        ALOGE("Could not register service for Power HAL Iface (%d).", status);
-        goto shutdown;
-    }
-
-    ALOGI("Power Service is ready");
-    joinRpcThreadpool();
-    //Should not pass this line
-
-shutdown:
-    // In normal operation, we don't expect the thread pool to exit
-
-    ALOGE("Power Service is shutting down");
-    return 1;
-}
diff --git a/power/Android.bp b/power/Android.bp
index 7a315fa..a5415df 100644
--- a/power/Android.bp
+++ b/power/Android.bp
@@ -4,6 +4,5 @@
     "1.0/default",
     "1.0/vts/functional",
     "1.1",
-    "1.1/default",
     "1.1/vts/functional",
 ]
diff --git a/radio/1.0/types.hal b/radio/1.0/types.hal
index c5d7f8a..4d22bc0 100644
--- a/radio/1.0/types.hal
+++ b/radio/1.0/types.hal
@@ -1507,8 +1507,8 @@
     int32_t lac;                          // 16-bit Location Area Code, 0..65535, INT_MAX if unknown
     int32_t cid;                          // 16-bit GSM Cell Identity described in
                                           // TS 27.007, 0..65535, INT_MAX if unknown
-    int32_t arfcn;                        // 16-bit GSM Absolute RF channel number, INT_MAX if
-                                          // unknown
+    int32_t arfcn;                        // 16-bit GSM Absolute RF channel number; this value must
+                                          // be valid
     uint8_t bsic;                         // 6-bit Base Station Identity Code, 0xFF if unknown
 };
 
@@ -1520,9 +1520,9 @@
     int32_t cid;                          // 28-bit UMTS Cell Identity described in
                                           // TS 25.331, 0..268435455, INT_MAX if unknown
     int32_t psc;                          // 9-bit UMTS Primary Scrambling Code described in
-                                          // TS 25.331, 0..511, INT_MAX if unknown
-    int32_t uarfcn;                       // 16-bit UMTS Absolute RF Channel Number, INT_MAX if
-                                          // unknown
+                                          // TS 25.331, 0..511; this value must be valid
+    int32_t uarfcn;                       // 16-bit UMTS Absolute RF Channel Number; this value must
+                                          // be valid
 };
 
 struct CellIdentityCdma {
@@ -1547,10 +1547,10 @@
                                           // unknown
     int32_t ci;                           // 28-bit Cell Identity described in TS TS 27.007, INT_MAX
                                           // if unknown
-    int32_t pci;                          // physical cell id 0..503, INT_MAX if unknown
+    int32_t pci;                          // physical cell id 0..503; this value must be valid
     int32_t tac;                          // 16-bit tracking area code, INT_MAX if unknown
-    int32_t earfcn;                       // 18-bit LTE Absolute RC Channel Number, INT_MAX if
-                                          // unknown
+    int32_t earfcn;                       // 18-bit LTE Absolute RF Channel Number; this value must
+                                          // be valid
 };
 
 struct CellIdentityTdscdma {
diff --git a/radio/1.0/vts/functional/radio_hidl_hal_misc.cpp b/radio/1.0/vts/functional/radio_hidl_hal_misc.cpp
index d93b176..795af86 100644
--- a/radio/1.0/vts/functional/radio_hidl_hal_misc.cpp
+++ b/radio/1.0/vts/functional/radio_hidl_hal_misc.cpp
@@ -70,7 +70,7 @@
 TEST_F(RadioHidlTest, setRadioPower) {
     int serial = GetRandomSerialNumber();
 
-    radio->setRadioPower(serial, 0);
+    radio->setRadioPower(serial, 1);
     EXPECT_EQ(std::cv_status::no_timeout, wait());
     EXPECT_EQ(RadioResponseType::SOLICITED, radioRsp->rspInfo.type);
     EXPECT_EQ(serial, radioRsp->rspInfo.serial);
diff --git a/thermal/1.1/Android.bp b/thermal/1.1/Android.bp
index 833f219..0985d94 100644
--- a/thermal/1.1/Android.bp
+++ b/thermal/1.1/Android.bp
@@ -42,13 +42,16 @@
     ],
 }
 
-cc_library_shared {
+cc_library {
     name: "android.hardware.thermal@1.1",
     defaults: ["hidl-module-defaults"],
     generated_sources: ["android.hardware.thermal@1.1_genc++"],
     generated_headers: ["android.hardware.thermal@1.1_genc++_headers"],
     export_generated_headers: ["android.hardware.thermal@1.1_genc++_headers"],
     vendor_available: true,
+    vndk: {
+        enabled: true,
+    },
     shared_libs: [
         "libhidlbase",
         "libhidltransport",
diff --git a/thermal/1.1/vts/functional/Android.bp b/thermal/1.1/vts/functional/Android.bp
index cea5bf8..f5f01fa 100644
--- a/thermal/1.1/vts/functional/Android.bp
+++ b/thermal/1.1/vts/functional/Android.bp
@@ -16,20 +16,11 @@
 
 cc_test {
     name: "VtsHalThermalV1_1TargetTest",
-    defaults: ["hidl_defaults"],
+    defaults: ["VtsHalTargetTestDefaults"],
     srcs: ["VtsHalThermalV1_1TargetTest.cpp"],
-    shared_libs: [
-        "liblog",
-        "libhidlbase",
-        "libhidltransport",
-        "libutils",
+    static_libs: [
         "android.hardware.thermal@1.0",
         "android.hardware.thermal@1.1",
     ],
-    static_libs: ["VtsHalHidlTargetTestBase"],
-    cflags: [
-        "-O0",
-        "-g",
-    ],
 }
 
diff --git a/wifi/1.1/default/hidl_return_util.h b/wifi/1.1/default/hidl_return_util.h
index 2f95c23..f36c8bd 100644
--- a/wifi/1.1/default/hidl_return_util.h
+++ b/wifi/1.1/default/hidl_return_util.h
@@ -55,6 +55,25 @@
   return Void();
 }
 
+// Use for HIDL methods which return only an instance of WifiStatus.
+// This version passes the global lock acquired to the body of the method.
+// Note: Only used by IWifi::stop() currently.
+template <typename ObjT, typename WorkFuncT, typename... Args>
+Return<void> validateAndCallWithLock(
+    ObjT* obj,
+    WifiStatusCode status_code_if_invalid,
+    WorkFuncT&& work,
+    const std::function<void(const WifiStatus&)>& hidl_cb,
+    Args&&... args) {
+  auto lock = hidl_sync_util::acquireGlobalLock();
+  if (obj->isValid()) {
+    hidl_cb((obj->*work)(&lock, std::forward<Args>(args)...));
+  } else {
+    hidl_cb(createWifiStatus(status_code_if_invalid));
+  }
+  return Void();
+}
+
 // Use for HIDL methods which return instance of WifiStatus and a single return
 // value.
 template <typename ObjT, typename WorkFuncT, typename ReturnT, typename... Args>
diff --git a/wifi/1.1/default/wifi.cpp b/wifi/1.1/default/wifi.cpp
index 8456b90..c46ef95 100644
--- a/wifi/1.1/default/wifi.cpp
+++ b/wifi/1.1/default/wifi.cpp
@@ -31,6 +31,7 @@
 namespace V1_1 {
 namespace implementation {
 using hidl_return_util::validateAndCall;
+using hidl_return_util::validateAndCallWithLock;
 
 Wifi::Wifi()
     : legacy_hal_(new legacy_hal::WifiLegacyHal()),
@@ -64,8 +65,8 @@
 }
 
 Return<void> Wifi::stop(stop_cb hidl_status_cb) {
-  return validateAndCall(
-      this, WifiStatusCode::ERROR_UNKNOWN, &Wifi::stopInternal, hidl_status_cb);
+  return validateAndCallWithLock(this, WifiStatusCode::ERROR_UNKNOWN,
+                                 &Wifi::stopInternal, hidl_status_cb);
 }
 
 Return<void> Wifi::getChipIds(getChipIds_cb hidl_status_cb) {
@@ -120,7 +121,8 @@
   return wifi_status;
 }
 
-WifiStatus Wifi::stopInternal() {
+WifiStatus Wifi::stopInternal(
+    /* NONNULL */ std::unique_lock<std::recursive_mutex>* lock) {
   if (run_state_ == RunState::STOPPED) {
     return createWifiStatus(WifiStatusCode::SUCCESS);
   } else if (run_state_ == RunState::STOPPING) {
@@ -133,7 +135,7 @@
     chip_->invalidate();
     chip_.clear();
   }
-  WifiStatus wifi_status = stopLegacyHalAndDeinitializeModeController();
+  WifiStatus wifi_status = stopLegacyHalAndDeinitializeModeController(lock);
   if (wifi_status.code == WifiStatusCode::SUCCESS) {
     for (const auto& callback : event_cb_handler_.getCallbacks()) {
       if (!callback->onStop().isOk()) {
@@ -180,11 +182,11 @@
   return createWifiStatus(WifiStatusCode::SUCCESS);
 }
 
-WifiStatus Wifi::stopLegacyHalAndDeinitializeModeController() {
+WifiStatus Wifi::stopLegacyHalAndDeinitializeModeController(
+    /* NONNULL */ std::unique_lock<std::recursive_mutex>* lock) {
   run_state_ = RunState::STOPPING;
-  const auto on_complete_callback_ = [&]() { run_state_ = RunState::STOPPED; };
   legacy_hal::wifi_error legacy_status =
-      legacy_hal_->stop(on_complete_callback_);
+      legacy_hal_->stop(lock, [&]() { run_state_ = RunState::STOPPED; });
   if (legacy_status != legacy_hal::WIFI_SUCCESS) {
     LOG(ERROR) << "Failed to stop legacy HAL: "
                << legacyErrorToString(legacy_status);
diff --git a/wifi/1.1/default/wifi.h b/wifi/1.1/default/wifi.h
index 1ade2d8..3a64cbd 100644
--- a/wifi/1.1/default/wifi.h
+++ b/wifi/1.1/default/wifi.h
@@ -61,12 +61,13 @@
   WifiStatus registerEventCallbackInternal(
       const sp<IWifiEventCallback>& event_callback);
   WifiStatus startInternal();
-  WifiStatus stopInternal();
+  WifiStatus stopInternal(std::unique_lock<std::recursive_mutex>* lock);
   std::pair<WifiStatus, std::vector<ChipId>> getChipIdsInternal();
   std::pair<WifiStatus, sp<IWifiChip>> getChipInternal(ChipId chip_id);
 
   WifiStatus initializeLegacyHal();
-  WifiStatus stopLegacyHalAndDeinitializeModeController();
+  WifiStatus stopLegacyHalAndDeinitializeModeController(
+      std::unique_lock<std::recursive_mutex>* lock);
 
   // Instance is created in this root level |IWifi| HIDL interface object
   // and shared with all the child HIDL interface objects.
diff --git a/wifi/1.1/default/wifi_legacy_hal.cpp b/wifi/1.1/default/wifi_legacy_hal.cpp
index 151a600..a6f6971 100644
--- a/wifi/1.1/default/wifi_legacy_hal.cpp
+++ b/wifi/1.1/default/wifi_legacy_hal.cpp
@@ -15,6 +15,7 @@
  */
 
 #include <array>
+#include <chrono>
 
 #include <android-base/logging.h>
 #include <cutils/properties.h>
@@ -34,6 +35,7 @@
 static constexpr uint32_t kLinkLayerStatsDataMpduSizeThreshold = 128;
 static constexpr uint32_t kMaxWakeReasonStatsArraySize = 32;
 static constexpr uint32_t kMaxRingBuffers = 10;
+static constexpr uint32_t kMaxStopCompleteWaitMs = 50;
 
 // Helper function to create a non-const char* for legacy Hal API's.
 std::vector<char> makeCharVec(const std::string& str) {
@@ -53,7 +55,8 @@
 // Legacy HAL functions accept "C" style function pointers, so use global
 // functions to pass to the legacy HAL function and store the corresponding
 // std::function methods to be invoked.
-// Callback to be invoked once |stop| is complete.
+//
+// Callback to be invoked once |stop| is complete
 std::function<void(wifi_handle handle)> on_stop_complete_internal_callback;
 void onAsyncStopComplete(wifi_handle handle) {
   const auto lock = hidl_sync_util::acquireGlobalLock();
@@ -369,6 +372,7 @@
 }
 
 wifi_error WifiLegacyHal::stop(
+    /* NONNULL */ std::unique_lock<std::recursive_mutex>* lock,
     const std::function<void()>& on_stop_complete_user_callback) {
   if (!is_started_) {
     LOG(DEBUG) << "Legacy HAL already stopped";
@@ -376,19 +380,27 @@
     return WIFI_SUCCESS;
   }
   LOG(DEBUG) << "Stopping legacy HAL";
-  on_stop_complete_internal_callback = [on_stop_complete_user_callback,
-                                        this](wifi_handle handle) {
+  on_stop_complete_internal_callback =
+      [on_stop_complete_user_callback, this](wifi_handle handle) {
     CHECK_EQ(global_handle_, handle) << "Handle mismatch";
+    LOG(INFO) << "Legacy HAL stop complete callback received";
     // Invalidate all the internal pointers now that the HAL is
     // stopped.
     invalidate();
     iface_tool_.SetWifiUpState(false);
     on_stop_complete_user_callback();
+    is_started_ = false;
   };
   awaiting_event_loop_termination_ = true;
   global_func_table_.wifi_cleanup(global_handle_, onAsyncStopComplete);
+  const auto status = stop_wait_cv_.wait_for(
+      *lock, std::chrono::milliseconds(kMaxStopCompleteWaitMs),
+      [this] { return !awaiting_event_loop_termination_; });
+  if (!status) {
+    LOG(ERROR) << "Legacy HAL stop failed or timed out";
+    return WIFI_ERROR_UNKNOWN;
+  }
   LOG(DEBUG) << "Legacy HAL stop complete";
-  is_started_ = false;
   return WIFI_SUCCESS;
 }
 
@@ -1257,11 +1269,13 @@
 void WifiLegacyHal::runEventLoop() {
   LOG(DEBUG) << "Starting legacy HAL event loop";
   global_func_table_.wifi_event_loop(global_handle_);
+  const auto lock = hidl_sync_util::acquireGlobalLock();
   if (!awaiting_event_loop_termination_) {
     LOG(FATAL) << "Legacy HAL event loop terminated, but HAL was not stopping";
   }
   LOG(DEBUG) << "Legacy HAL event loop terminated";
   awaiting_event_loop_termination_ = false;
+  stop_wait_cv_.notify_one();
 }
 
 std::pair<wifi_error, std::vector<wifi_cached_scan_results>>
diff --git a/wifi/1.1/default/wifi_legacy_hal.h b/wifi/1.1/default/wifi_legacy_hal.h
index caa1bd5..5498803 100644
--- a/wifi/1.1/default/wifi_legacy_hal.h
+++ b/wifi/1.1/default/wifi_legacy_hal.h
@@ -20,6 +20,7 @@
 #include <functional>
 #include <thread>
 #include <vector>
+#include <condition_variable>
 
 #include <wifi_system/interface_tool.h>
 
@@ -149,8 +150,10 @@
   wifi_error initialize();
   // Start the legacy HAL and the event looper thread.
   wifi_error start();
-  // Deinitialize the legacy HAL and stop the event looper thread.
-  wifi_error stop(const std::function<void()>& on_complete_callback);
+  // Deinitialize the legacy HAL and wait for the event loop thread to exit
+  // using a predefined timeout.
+  wifi_error stop(std::unique_lock<std::recursive_mutex>* lock,
+                  const std::function<void()>& on_complete_callback);
   // Wrappers for all the functions in the legacy HAL function table.
   std::pair<wifi_error, std::string> getDriverVersion();
   std::pair<wifi_error, std::string> getFirmwareVersion();
@@ -293,6 +296,7 @@
   wifi_interface_handle wlan_interface_handle_;
   // Flag to indicate if we have initiated the cleanup of legacy HAL.
   std::atomic<bool> awaiting_event_loop_termination_;
+  std::condition_variable_any stop_wait_cv_;
   // Flag to indicate if the legacy HAL has been started.
   bool is_started_;
   wifi_system::InterfaceTool iface_tool_;