diff --git a/audio/5.0/IStreamIn.hal b/audio/5.0/IStreamIn.hal
index b042960..e15b034 100644
--- a/audio/5.0/IStreamIn.hal
+++ b/audio/5.0/IStreamIn.hal
@@ -169,6 +169,10 @@
     /**
      * Specifies the logical microphone (for processing).
      *
+     * If the feature is not supported an error should be returned
+     * If multiple microphones are present, this should be treated as a preference
+     * for their combined direction.
+     *
      * Optional method
      *
      * @param Direction constant
@@ -180,6 +184,10 @@
     /**
      * Specifies the zoom factor for the selected microphone (for processing).
      *
+     * If the feature is not supported an error should be returned
+     * If multiple microphones are present, this should be treated as a preference
+     * for their combined field dimension.
+     *
      * Optional method
      *
      * @param the desired field dimension of microphone capture. Range is from -1 (wide angle),
diff --git a/audio/common/5.0/types.hal b/audio/common/5.0/types.hal
index 0cbf35e..e1279ee 100644
--- a/audio/common/5.0/types.hal
+++ b/audio/common/5.0/types.hal
@@ -146,6 +146,7 @@
      */
     ECHO_REFERENCE      = 1997,
     FM_TUNER            = 1998,
+    HOTWORD             = 1999,
 };
 
 typedef int32_t AudioSession;
diff --git a/automotive/vehicle/2.0/types.hal b/automotive/vehicle/2.0/types.hal
index 435e19c..b04d096 100644
--- a/automotive/vehicle/2.0/types.hal
+++ b/automotive/vehicle/2.0/types.hal
@@ -1139,7 +1139,6 @@
      * Indicates which units the car is using to display fuel volume to the user. Eg. Liter or
      * Gallon.
      *
-     * Distance units are defined in VehicleUnit.
      * VehiclePropConfig.configArray is used to indicate the supported fuel volume display units.
      * Volume units are defined in VehicleUnit.
      * For example: configArray[0] = 0x41 // LITER
@@ -1160,7 +1159,6 @@
      * Indicates which units the car is using to display tire pressure to the user. Eg. PSI, Bar or
      * Kilopascal.
      *
-     * Distance units are defined in VehicleUnit.
      * VehiclePropConfig.configArray is used to indicate the supported pressure display units.
      * Pressure units are defined in VehicleUnit.
      * For example: configArray[0] = 0x70 // KILOPASCAL
@@ -1182,7 +1180,6 @@
      * Indicates which units the car is using to display EV battery information to the user. Eg.
      * watt-hours(Wh), kilowatt-hours(kWh) or ampere-hours(Ah).
      *
-     * Distance units are defined in VehicleUnit.
      * VehiclePropConfig.configArray is used to indicate the supported electrical energy units.
      * Electrical energy units are defined in VehicleUnit.
      * For example: configArray[0] = 0x60 // watt-hours
@@ -1199,6 +1196,22 @@
         | VehicleArea:GLOBAL),
 
     /**
+     * Fuel consumption units for display
+     *
+     * Indicates type of units the car is using to display fuel consumption information to user
+     * True indicates units are distance over volume such as MPG.
+     * False indicates units are volume over distance such as L/100KM.
+     *
+     * @change_mode VehiclePropertyChangeMode:ON_CHANGE
+     * @access VehiclePropertyAccess:READ_WRITE
+     */
+    FUEL_CONSUMPTION_UNITS_DISTANCE_OVER_VOLUME = (
+        0x0604
+        | VehiclePropertyGroup:SYSTEM
+        | VehiclePropertyType:BOOLEAN
+        | VehicleArea:GLOBAL),
+
+    /**
      * Outside temperature
      *
      * @change_mode VehiclePropertyChangeMode:CONTINUOUS
@@ -2588,7 +2601,11 @@
     KELVIN         = 0x32,
     MILLILITER     = 0x40,
     LITER          = 0x41,
+
+    /** deprecated. Use US_GALLON instead. */
     GALLON         = 0x42,
+    US_GALLON      = 0x42,
+    IMPERIAL_GALLON= 0x43,
     NANO_SECS      = 0x50,
     SECS           = 0x53,
     YEAR           = 0x59,
diff --git a/broadcastradio/2.0/default/BroadcastRadio.cpp b/broadcastradio/2.0/default/BroadcastRadio.cpp
index 28a0dd5..88a726f 100644
--- a/broadcastradio/2.0/default/BroadcastRadio.cpp
+++ b/broadcastradio/2.0/default/BroadcastRadio.cpp
@@ -49,6 +49,7 @@
         static_cast<uint32_t>(IdentifierType::AMFM_FREQUENCY),
         static_cast<uint32_t>(IdentifierType::RDS_PI),
         static_cast<uint32_t>(IdentifierType::HD_STATION_ID_EXT),
+        static_cast<uint32_t>(IdentifierType::DAB_SID_EXT),
     });
     prop.vendorInfo = hidl_vec<VendorKeyValue>({
         {"com.google.dummy", "dummy"},
diff --git a/broadcastradio/2.0/default/VirtualRadio.cpp b/broadcastradio/2.0/default/VirtualRadio.cpp
index 0b65979..c59fd8f 100644
--- a/broadcastradio/2.0/default/VirtualRadio.cpp
+++ b/broadcastradio/2.0/default/VirtualRadio.cpp
@@ -28,6 +28,7 @@
 using std::mutex;
 using std::vector;
 using utils::make_selector_amfm;
+using utils::make_selector_dab;
 
 VirtualRadio gAmFmRadio(
     "AM/FM radio mock",
@@ -41,6 +42,16 @@
         {make_selector_amfm(106100), "106 KMEL", "Drake", "Marvins Room"},
     });
 
+// clang-format off
+VirtualRadio gDabRadio(
+    "DAB radio mock",
+    {
+        {make_selector_dab(12345, 225648), "BBC Radio 1", "Khalid", "Talk"},  // 12B
+        {make_selector_dab(22345, 222064), "Classic FM", "Jean Sibelius", "Andante Festivo"},  // 11D
+        {make_selector_dab(32345, 222064), "Absolute Radio", "Coldplay", "Clocks"},  // 11D
+    });
+// clang-format on
+
 VirtualRadio::VirtualRadio(const std::string& name, const vector<VirtualProgram>& initialList)
     : mName(name), mPrograms(initialList) {}
 
diff --git a/broadcastradio/2.0/default/VirtualRadio.h b/broadcastradio/2.0/default/VirtualRadio.h
index 9c07816..6fa70c5 100644
--- a/broadcastradio/2.0/default/VirtualRadio.h
+++ b/broadcastradio/2.0/default/VirtualRadio.h
@@ -52,6 +52,9 @@
 /** AM/FM virtual radio space. */
 extern VirtualRadio gAmFmRadio;
 
+/** DAB virtual radio space. */
+extern VirtualRadio gDabRadio;
+
 }  // namespace implementation
 }  // namespace V2_0
 }  // namespace broadcastradio
diff --git a/broadcastradio/2.0/default/service.cpp b/broadcastradio/2.0/default/service.cpp
index af96dad..349aba2 100644
--- a/broadcastradio/2.0/default/service.cpp
+++ b/broadcastradio/2.0/default/service.cpp
@@ -23,6 +23,7 @@
 using android::hardware::joinRpcThreadpool;
 using android::hardware::broadcastradio::V2_0::implementation::BroadcastRadio;
 using android::hardware::broadcastradio::V2_0::implementation::gAmFmRadio;
+using android::hardware::broadcastradio::V2_0::implementation::gDabRadio;
 
 int main() {
     android::base::SetDefaultTag("BcRadioDef");
@@ -30,8 +31,13 @@
     configureRpcThreadpool(4, true);
 
     BroadcastRadio broadcastRadio(gAmFmRadio);
-    auto status = broadcastRadio.registerAsService();
-    CHECK_EQ(status, android::OK) << "Failed to register Broadcast Radio HAL implementation";
+    auto amFmStatus = broadcastRadio.registerAsService("amfm");
+    CHECK_EQ(amFmStatus, android::OK)
+        << "Failed to register Broadcast Radio AM/FM HAL implementation";
+
+    BroadcastRadio dabRadio(gDabRadio);
+    auto dabStatus = dabRadio.registerAsService("dab");
+    CHECK_EQ(dabStatus, android::OK) << "Failed to register Broadcast Radio DAB HAL implementation";
 
     joinRpcThreadpool();
     return 1;  // joinRpcThreadpool shouldn't exit
diff --git a/broadcastradio/common/utils2x/Utils.cpp b/broadcastradio/common/utils2x/Utils.cpp
index 7892653..43f272e 100644
--- a/broadcastradio/common/utils2x/Utils.cpp
+++ b/broadcastradio/common/utils2x/Utils.cpp
@@ -299,6 +299,20 @@
     return sel;
 }
 
+ProgramSelector make_selector_dab(uint32_t sidExt, uint32_t ensemble) {
+    ProgramSelector sel = {};
+    // TODO(maryabad): Have a helper function to create the sidExt instead of
+    // passing the whole identifier here. Something like make_dab_sid_ext.
+    sel.primaryId = make_identifier(IdentifierType::DAB_SID_EXT, sidExt);
+    hidl_vec<ProgramIdentifier> secondaryIds = {
+        make_identifier(IdentifierType::DAB_ENSEMBLE, ensemble),
+        // TODO(maryabad): Include frequency here when the helper method to
+        // translate between ensemble and frequency is implemented.
+    };
+    sel.secondaryIds = secondaryIds;
+    return sel;
+}
+
 Metadata make_metadata(MetadataKey key, int64_t value) {
     Metadata meta = {};
     meta.key = static_cast<uint32_t>(key);
diff --git a/broadcastradio/common/utils2x/include/broadcastradio-utils-2x/Utils.h b/broadcastradio/common/utils2x/include/broadcastradio-utils-2x/Utils.h
index c4aecb2..f4e0732 100644
--- a/broadcastradio/common/utils2x/include/broadcastradio-utils-2x/Utils.h
+++ b/broadcastradio/common/utils2x/include/broadcastradio-utils-2x/Utils.h
@@ -126,6 +126,7 @@
 
 V2_0::ProgramIdentifier make_identifier(V2_0::IdentifierType type, uint64_t value);
 V2_0::ProgramSelector make_selector_amfm(uint32_t frequency);
+V2_0::ProgramSelector make_selector_dab(uint32_t sidExt, uint32_t ensemble);
 V2_0::Metadata make_metadata(V2_0::MetadataKey key, int64_t value);
 V2_0::Metadata make_metadata(V2_0::MetadataKey key, std::string value);
 
diff --git a/current.txt b/current.txt
index b818922..96c1bbf 100644
--- a/current.txt
+++ b/current.txt
@@ -416,11 +416,11 @@
 0a911297821854985cfcdb17b63d7948af0f0f51ce8c68cc86367c185bbc772e android.hardware.audio@5.0::IDevicesFactory
 ce2e8c6c8559fd42bd69e0dee27b4d9c93cd9b2eff487b4e6b6395b6a1a993d6 android.hardware.audio@5.0::IPrimaryDevice
 4a4e5e5d9357004a1256bde8d36010ee00c51cea811a1c1e0dd969a9fc0bf862 android.hardware.audio@5.0::IStream
-e05e48c583de14c1e5a6fa9d48ea50244e3e0924b76b342374e7471dc8007ba9 android.hardware.audio@5.0::IStreamIn
+b9d41ff4031266de1ecef394a8a64de7d857634dd08dc6be855fca2fe3075975 android.hardware.audio@5.0::IStreamIn
 9471b12b1c255bb530695720bc4174bd74987b75b1f820854af8944bc8c215c9 android.hardware.audio@5.0::IStreamOut
 1b0500367ed2b32a841667ac3200edf3d3a164e8004aca445ff1b085ac831e93 android.hardware.audio@5.0::IStreamOutCallback
 83e365479cc77d8717c155e1787ee668cd2ae4c557b467cf75b8e7cd53697ad8 android.hardware.audio@5.0::types
-a0df6961e65444e1ca40a206d7f31304d313e8b7e5b122855e3272ab02720cd4 android.hardware.audio.common@5.0::types
+07d17800b298331e90d4ea5d8ba19a1ae3fe9c1dbff08d9f75fd3ade09496d67 android.hardware.audio.common@5.0::types
 f269297866765b95ddd1825676cc8a772f0c7c9863286df596fc302781a42ff5 android.hardware.audio.effect@5.0::IAcousticEchoCancelerEffect
 fa187b602d8939644ef708ed7627f2e3deac97899a4bda1de07f2ff126abe243 android.hardware.audio.effect@5.0::IAutomaticGainControlEffect
 e1bf864ccb8458c0da1dcc74a2e748b1dca8ac360df590591cf82d98292d7981 android.hardware.audio.effect@5.0::IBassBoostEffect
@@ -464,12 +464,15 @@
 7f460e795f5d1ed5e378935f98c6db4d39497de988aef1b4c2a4a07a6c400392 android.hardware.gnss@2.0::IAGnss
 2e5ad983734069e84a760004b32da0d09e4170c05380abe27e6eb80e4aa70d5a android.hardware.gnss@2.0::IAGnssCallback
 1f4ac068a88a72360280d94a7f6fd7c63813c1eea4891a0eb01394d3e7e775f2 android.hardware.gnss@2.0::IAGnssRil
-6e2f9a44375a0ae0b49ca7d711cb88945189d398535078408269e1e85889061d android.hardware.gnss@2.0::IGnss
-782dfc724272f279985de348c824197357941382f73c0083f0344d8ec594d2a8 android.hardware.gnss@2.0::IGnssCallback
+4deafcdcffa2d002119e7f58810b767a84666e76475aae68e757ec2845d9756d android.hardware.gnss@2.0::IGnss
+db6bdf6dfc5edf6c85d2944976db899227abb51079c893874353c322342c50b6 android.hardware.gnss@2.0::IGnssBatching
+1f89392f1ebb693d8fa6f50324b1635fc79fab246d31900e63998e1b0e17511c android.hardware.gnss@2.0::IGnssBatchingCallback
+b11a5e4a1602d3f408716b6fe2c578a79f060d571aad8e828f9a4426d161fbcf android.hardware.gnss@2.0::IGnssCallback
 ecc966c68bddbd95c8dae782b84204cf01c75734675e8769963f3b5106ec128b android.hardware.gnss@2.0::IGnssConfiguration
+b670bae2ab8517336290532e364502b4db9120340d75474ccc8442b1b15d6ab7 android.hardware.gnss@2.0::IGnssDebug
 c67759f5d6387d273b66729180d03690e827f0b6b8d4e13ce2ff42d31b224065 android.hardware.gnss@2.0::IGnssMeasurement
-3dd30a3ca77ef5ab109a55ba603ff816ae5019436886093dccf8fd6a068f85f1 android.hardware.gnss@2.0::IGnssMeasurementCallback
-4bcd767dd05304b4722c6521c7ed8d4a05faf6022f228f2c088379c647871f7c android.hardware.gnss@2.0::types
+15e09903748857f4beb5f485784606931fa5a6277cd070baa6d584df485b7948 android.hardware.gnss@2.0::IGnssMeasurementCallback
+a49c973f21ddf41bc402de55d7c8dffacf4dce06b0bbca4f5ffd3b09a471317e android.hardware.gnss@2.0::types
 d4cc8d91930d5a1a62deb0d97d398510a115ce3ede2d2978738651b9d01b11c3 android.hardware.gnss.measurement_corrections@1.0::IMeasurementCorrections
 3eec9763db9b101644f14175b77c9954047445a468e9c743fd402d472d4aa97e android.hardware.gnss.measurement_corrections@1.0::IMeasurementCorrectionsCallback
 6ef12cd95df73f8f80c25eb035d98ca4594f9cee571fdabea838a0b6016dd908 android.hardware.gnss.measurement_corrections@1.0::types
@@ -505,11 +508,11 @@
 7d3c292ca75ec3e22a8fd4ae72d2edb0659d280257e763786e766f3429954dd1 android.hardware.media.c2@1.0::types
 4880af120fc1640225abdc2c60bda6d79617d73484d5124913c7278af3b11e2d android.hardware.neuralnetworks@1.2::IBurstCallback
 19877e466ad8c6ed42b38050b77bd010cf7800ff365fdc8574f45bbfda03a758 android.hardware.neuralnetworks@1.2::IBurstContext
-96249c852dabeefa3a9496ecdfc44681a071c665bfbf88527bf775c88bf1ab1b android.hardware.neuralnetworks@1.2::IDevice
+b83317b66721241887d2770b5ae95fd5af1e77c5daa7530ecb08fae8892f2b43 android.hardware.neuralnetworks@1.2::IDevice
 92714960d1a53fc2ec557302b41c7cc93d2636d8364a44bd0f85be0c92927ff8 android.hardware.neuralnetworks@1.2::IExecutionCallback
-83885d366f22ada42c00d8854f0b7e7ba4cf73ddf80bb0d8e168ce132cec57ea android.hardware.neuralnetworks@1.2::IPreparedModel
+36e1064c869965dee533c537cefbe87e54db8bd8cd45be7e0e93e00e8a43863a android.hardware.neuralnetworks@1.2::IPreparedModel
 e1c734d1545e1a4ae749ff1dd9704a8e594c59aea7c8363159dc258e93e0df3b android.hardware.neuralnetworks@1.2::IPreparedModelCallback
-114056b3b9303e0e858f28e718ba45722de5678d1d54eec0dcd10788604bf2bb android.hardware.neuralnetworks@1.2::types
+209a5ee694b94328afb2af2768f1fe6a69148e2cbb85ec3c340a36eed818c697 android.hardware.neuralnetworks@1.2::types
 cf7a4ba516a638f9b82a249c91fb603042c2d9ca43fd5aad9cf6c0401ed2a5d7 android.hardware.nfc@1.2::INfc
 abf98c2ae08bf765db54edc8068e36d52eb558cff6706b6fd7c18c65a1f3fc18 android.hardware.nfc@1.2::types
 4cb252dc6372a874aef666b92a6e9529915aa187521a700f0789065c3c702ead android.hardware.power.stats@1.0::IPowerStats
@@ -541,9 +544,10 @@
 61bc302e7c974c59b25898c585c6e9685e8a81021b1bed3eedf5224198f2785a android.hardware.usb@1.2::IUsb
 46996cd2a1c66261a75a1f6ecada77eeb5861eb264fa39b996548fe0a7f22dd3 android.hardware.usb@1.2::IUsbCallback
 3bbaa8cbc5d6b1da21f5509b2b641e05fc7eeca1354751eb1bb3cf37f89aa32f android.hardware.usb@1.2::types
-92c1a726c80970d623b891f7c2f9a989a40a15ee1244092b49f4eb6adcdce4e9 android.hardware.vibrator@1.3::IVibrator
+0f7ff73793548d5154014059b7e0fe9ef6355d32218ace157954d02055f5248b android.hardware.vibrator@1.3::IVibrator
+2e313dc27a1327a29862ab3e085917f75c9e996f7c8df5a0ce37b9a0ed076b80 android.hardware.vibrator@1.3::types
 f19832856a3f53ced5ef91d3cc630a57fb7f4d4ce15f364dbed09099b89f6830 android.hardware.wifi@1.3::IWifi
-7c6799c19bfdb3dec016b751556fe246cf7d37191ee7bb82a0091ab9fbf6f2fb android.hardware.wifi@1.3::IWifiChip
+64be084b6e1ef330b75fa916593dc0b94b0ec7a16d5cfaa5a31e6c9143c8288d android.hardware.wifi@1.3::IWifiChip
 3bef30e8b61ab050c0f6fd26572712be5ebb7707d624c9aa6c74bbb9d6a5b4a9 android.hardware.wifi@1.3::IWifiStaIface
 f3dbd8dd0d6333c005610288a4785d0ef79a72a7bbe6d0a46d46fa89fc886f1e android.hardware.wifi@1.3::types
 2fae61e962f68091335f7ff4581fcfe2e28ce7f6132d7a712fa13d7965543e4d android.hardware.wifi.hostapd@1.1::IHostapd
diff --git a/gnss/2.0/Android.bp b/gnss/2.0/Android.bp
index c01ec55..6cfd346 100644
--- a/gnss/2.0/Android.bp
+++ b/gnss/2.0/Android.bp
@@ -12,8 +12,11 @@
         "IAGnssCallback.hal",
         "IAGnssRil.hal",
         "IGnss.hal",
+        "IGnssBatching.hal",
+        "IGnssBatchingCallback.hal",
         "IGnssCallback.hal",
         "IGnssConfiguration.hal",
+        "IGnssDebug.hal",
         "IGnssMeasurement.hal",
         "IGnssMeasurementCallback.hal",
     ],
diff --git a/gnss/2.0/IGnss.hal b/gnss/2.0/IGnss.hal
index 2c149b7..f19f8d0 100644
--- a/gnss/2.0/IGnss.hal
+++ b/gnss/2.0/IGnss.hal
@@ -23,9 +23,11 @@
 import GnssLocation;
 import IGnssCallback;
 import IGnssConfiguration;
+import IGnssDebug;
 import IGnssMeasurement;
 import IAGnss;
 import IAGnssRil;
+import IGnssBatching;
 
 /**
  * Represents the standard GNSS (Global Navigation Satellite System) interface.
@@ -55,6 +57,13 @@
     getExtensionGnssConfiguration_2_0() generates (IGnssConfiguration gnssConfigurationIface);
 
     /**
+     * This method returns the IGnssDebug interface.
+     *
+     * @return gnssDebugIface Handle to the IGnssDebug interface.
+     */
+    getExtensionGnssDebug_2_0() generates (IGnssDebug gnssDebugIface);
+
+    /**
      * This method returns the IAGnss Interface.
      *
      * The getExtensionAGnss() must return nullptr as the @1.0::IAGnss interface is
@@ -97,6 +106,13 @@
     getExtensionVisibilityControl() generates (IGnssVisibilityControl visibilityControlIface);
 
     /**
+     * This method returns the IGnssBatching interface.
+     *
+     * @return batchingIface Handle to the IGnssBatching interface.
+     */
+    getExtensionGnssBatching_2_0() generates (IGnssBatching batchingIface);
+
+    /**
      * Injects current location from the best available location provider.
      *
      * Unlike injectLocation, this method may inject a recent GNSS location from the HAL
diff --git a/gnss/2.0/IGnssBatching.hal b/gnss/2.0/IGnssBatching.hal
new file mode 100644
index 0000000..961fa69
--- /dev/null
+++ b/gnss/2.0/IGnssBatching.hal
@@ -0,0 +1,51 @@
+/*
+ * Copyright (C) 2019 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package android.hardware.gnss@2.0;
+
+import @1.0::IGnssBatching;
+import IGnssBatchingCallback;
+
+/**
+ * Extended interface for GNSS Batching support.
+ *
+ * If this interface is supported, this batching request must be able to run in
+ * parallel with, or without, non-batched location requested by the
+ * IGnss start() & stop() - i.e. both requests must be handled independently,
+ * and not interfere with each other.
+ *
+ * For example, if a 1Hz continuous output is underway on the IGnssCallback,
+ * due to an IGnss start() operation,
+ * and then a IGnssBatching start() is called for a location every 10
+ * seconds, the newly added batching request must not disrupt the 1Hz
+ * continuous location output on the IGnssCallback.
+ *
+ * As with GNSS Location outputs, source of location must be GNSS satellite
+ * measurements, optionally using interial and baro sensors to improve
+ * relative motion filtering. No additional absolute positioning information,
+ * such as WiFi derived location, may be mixed with the GNSS information.
+ */
+interface IGnssBatching extends @1.0::IGnssBatching {
+    /**
+     * Opens the interface and provides the callback routines
+     * to the implementation of this interface.
+     *
+     * @param callback Callback interface for IGnssBatching.
+     *
+     * @return success Returns true on success.
+     */
+    init_2_0(IGnssBatchingCallback callback) generates (bool success);
+};
\ No newline at end of file
diff --git a/gnss/2.0/IGnssBatchingCallback.hal b/gnss/2.0/IGnssBatchingCallback.hal
new file mode 100644
index 0000000..4f8b4ec
--- /dev/null
+++ b/gnss/2.0/IGnssBatchingCallback.hal
@@ -0,0 +1,36 @@
+/*
+ * Copyright (C) 2019 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package android.hardware.gnss@2.0;
+
+/** The callback interface to report measurements from the HAL. */
+interface IGnssBatchingCallback {
+    /**
+     * Called when a batch of locations is output, by various means, including
+     * a flush request, as well as the buffer becoming full (if appropriate option
+     * is set.)
+     *
+     * All locations returned by this callback must be cleared from the hardware
+     * buffer, such the sequential calls of this callback do not return any
+     * redundant locations.  (Same lat/lon, at a new time, is acceptable.)
+     *
+     * The GnssLocation struct in gnss@2.0 is extended to include elapsed realtime
+     * information.
+     *
+     * @param locations GNSS Location information from HAL.
+     */
+    gnssLocationBatchCb(vec<GnssLocation> locations);
+};
diff --git a/gnss/2.0/IGnssCallback.hal b/gnss/2.0/IGnssCallback.hal
index 4c31cf5..a96fd6c 100644
--- a/gnss/2.0/IGnssCallback.hal
+++ b/gnss/2.0/IGnssCallback.hal
@@ -19,6 +19,7 @@
 import @1.0::IGnssCallback;
 import @1.1::IGnssCallback;
 import GnssLocation;
+import GnssConstellationType;
 
 /**
  * This interface is required for the HAL to communicate certain information
@@ -94,4 +95,26 @@
      *        during-call to E911, or up to 5 minutes after end-of-call or text to E911).
      */
     gnssRequestLocationCb_2_0(bool independentFromGnss, bool isUserEmergency);
+
+    /** Extends a GnssSvInfo, replacing the GnssConstellationType. */
+    struct GnssSvInfo {
+        /**
+         * GNSS satellite information for a single satellite and frequency.
+         *
+         * In this version of the HAL, the field 'constellation' in the v1_0 struct is deprecated,
+         * and is no longer used by the framework. The constellation type is instead reported in
+         * @2.0::IGnssCallback.GnssSvInfo.constellation.
+         */
+        @1.0::IGnssCallback.GnssSvInfo v1_0;
+
+        /** Defines the constellation of the given SV. */
+        GnssConstellationType constellation;
+    };
+
+    /**
+     * Callback for the HAL to pass a vector of GnssSvInfo back to the client.
+     *
+     * @param svInfo SV status information from HAL.
+     */
+    gnssSvStatusCb_2_0(vec<GnssSvInfo> svInfoList);
 };
diff --git a/gnss/2.0/IGnssDebug.hal b/gnss/2.0/IGnssDebug.hal
new file mode 100644
index 0000000..a3138ba
--- /dev/null
+++ b/gnss/2.0/IGnssDebug.hal
@@ -0,0 +1,66 @@
+/*
+ * Copyright (C) 2019 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package android.hardware.gnss@2.0;
+
+import @1.0::IGnssDebug;
+
+/** Extended interface for DEBUG support. */
+interface IGnssDebug extends @1.0::IGnssDebug {
+
+    /** Extending SatelliteData, replacing the GnssConstellationType. */
+    struct SatelliteData {
+        /**
+         * GNSS Satellite info.
+         *
+         * In this version of the HAL, the field 'constellation' in the v1_0 struct is deprecated,
+         * and is no longer used by the framework. The constellation type is instead reported in
+         * @2.0::IGnssDebug.SatelliteData.constellation.
+         */
+        @1.0::IGnssDebug.SatelliteData v1_0;
+
+        /** Defines the constellation type of the given SV. */
+        GnssConstellationType constellation;
+    };
+
+    /**
+     * Provides a set of debug information that is filled by the GNSS chipset when the method
+     * getDebugData() is invoked.
+     */
+    struct DebugData {
+        /** Current best known position. */
+        @1.0::IGnssDebug.PositionDebug position;
+
+        /** Current best know time estimate. */
+        @1.0::IGnssDebug.TimeDebug time;
+
+        /**
+         * Provides a list of the available satellite data, for all
+         * satellites and constellations the device can track,
+         * including GnssConstellationType UNKNOWN.
+         */
+        vec<SatelliteData> satelliteDataArray;
+    };
+
+    /**
+     * This methods requests position, time and satellite ephemeris debug information from the HAL.
+     *
+     * @return ret debugData information from GNSS Hal that contains the current best known
+     * position, best known time estimate and a complete list of constellations that the device can
+     * track.
+     */
+    getDebugData_2_0() generates (DebugData debugData);
+};
diff --git a/gnss/2.0/IGnssMeasurementCallback.hal b/gnss/2.0/IGnssMeasurementCallback.hal
index d9751d3..e055f7a 100644
--- a/gnss/2.0/IGnssMeasurementCallback.hal
+++ b/gnss/2.0/IGnssMeasurementCallback.hal
@@ -19,6 +19,7 @@
 import @1.0::IGnssMeasurementCallback;
 import @1.1::IGnssMeasurementCallback;
 import ElapsedRealtime;
+import GnssConstellationType;
 
 /** The callback interface to report measurements from the HAL. */
 interface IGnssMeasurementCallback extends @1.1::IGnssMeasurementCallback {
@@ -365,7 +366,8 @@
     };
 
     /**
-     * Extends a GNSS Measurement, adding a GnssMeasurementCodeType.
+     * Extends a GNSS Measurement, adding a GnssMeasurementCodeType, a GnssMeasurementState, and
+     * replacing the GnssConstellationType.
      */
     struct GnssMeasurement {
         /**
@@ -380,6 +382,10 @@
          * In this version of the HAL, the field 'state' in the v1_1.v1_0 struct is deprecated, and
          * is no longer used by the framework. The satellite sync state is instead reported in
          * @2.0::IGnssMeasurementCallback.GnssMeasurement.state.
+         *
+         * In this version of the HAL, the field 'constellation' in the v1_1.v1_0 struct is
+         * deprecated, and is no longer used by the framework. The constellation type is instead
+         * reported in @2.0::IGnssMeasurementCallback.GnssMeasurement.constellation.
          */
         @1.1::IGnssMeasurementCallback.GnssMeasurement v1_1;
 
@@ -442,6 +448,11 @@
          * This value is mandatory.
          */
         bitfield<GnssMeasurementState> state;
+
+        /**
+         * The constellation type of the GNSS measurement.
+         */
+        GnssConstellationType constellation;
     };
 
     /**
diff --git a/gnss/2.0/default/Android.bp b/gnss/2.0/default/Android.bp
index 64187e2..0fcd764 100644
--- a/gnss/2.0/default/Android.bp
+++ b/gnss/2.0/default/Android.bp
@@ -25,6 +25,7 @@
         "AGnss.cpp",
         "AGnssRil.cpp",
         "Gnss.cpp",
+	"GnssBatching.cpp",
         "GnssMeasurement.cpp",
         "GnssMeasurementCorrections.cpp",
         "GnssVisibilityControl.cpp",
diff --git a/gnss/2.0/default/Gnss.cpp b/gnss/2.0/default/Gnss.cpp
index 1dfdadb..75c2385 100644
--- a/gnss/2.0/default/Gnss.cpp
+++ b/gnss/2.0/default/Gnss.cpp
@@ -23,6 +23,7 @@
 
 #include "AGnss.h"
 #include "AGnssRil.h"
+#include "GnssBatching.h"
 #include "GnssConfiguration.h"
 #include "GnssMeasurement.h"
 #include "GnssMeasurementCorrections.h"
@@ -236,6 +237,11 @@
     return new GnssConfiguration{};
 }
 
+Return<sp<V2_0::IGnssDebug>> Gnss::getExtensionGnssDebug_2_0() {
+    // TODO(b/124012850): Implement function.
+    return sp<V2_0::IGnssDebug>{};
+}
+
 Return<sp<V2_0::IAGnss>> Gnss::getExtensionAGnss_2_0() {
     return new AGnss{};
 }
@@ -260,6 +266,10 @@
     return new GnssVisibilityControl();
 }
 
+Return<sp<V2_0::IGnssBatching>> Gnss::getExtensionGnssBatching_2_0() {
+    return new GnssBatching();
+}
+
 Return<bool> Gnss::setCallback_2_0(const sp<V2_0::IGnssCallback>& callback) {
     ALOGD("Gnss::setCallback_2_0");
     if (callback == nullptr) {
diff --git a/gnss/2.0/default/Gnss.h b/gnss/2.0/default/Gnss.h
index f02ab0a..72f7797 100644
--- a/gnss/2.0/default/Gnss.h
+++ b/gnss/2.0/default/Gnss.h
@@ -83,6 +83,7 @@
 
     // Methods from V2_0::IGnss follow.
     Return<sp<V2_0::IGnssConfiguration>> getExtensionGnssConfiguration_2_0() override;
+    Return<sp<V2_0::IGnssDebug>> getExtensionGnssDebug_2_0() override;
     Return<sp<V2_0::IAGnss>> getExtensionAGnss_2_0() override;
     Return<sp<V2_0::IAGnssRil>> getExtensionAGnssRil_2_0() override;
     Return<sp<V2_0::IGnssMeasurement>> getExtensionGnssMeasurement_2_0() override;
@@ -91,6 +92,7 @@
     getExtensionMeasurementCorrections() override;
     Return<sp<visibility_control::V1_0::IGnssVisibilityControl>> getExtensionVisibilityControl()
             override;
+    Return<sp<V2_0::IGnssBatching>> getExtensionGnssBatching_2_0() override;
     Return<bool> injectBestLocation_2_0(const V2_0::GnssLocation& location) override;
 
   private:
diff --git a/gnss/2.0/default/GnssBatching.cpp b/gnss/2.0/default/GnssBatching.cpp
new file mode 100644
index 0000000..d56cdfb
--- /dev/null
+++ b/gnss/2.0/default/GnssBatching.cpp
@@ -0,0 +1,70 @@
+/*
+ * Copyright (C) 2019 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 "GnssBatching"
+
+#include "GnssBatching.h"
+
+namespace android {
+namespace hardware {
+namespace gnss {
+namespace V2_0 {
+namespace implementation {
+
+sp<V2_0::IGnssBatchingCallback> GnssBatching::sCallback = nullptr;
+
+// Methods from ::android::hardware::gnss::V1_0::IGnssBatching follow.
+Return<bool> GnssBatching::init(const sp<V1_0::IGnssBatchingCallback>&) {
+    // TODO implement
+    return bool{};
+}
+
+Return<uint16_t> GnssBatching::getBatchSize() {
+    // TODO implement
+    return uint16_t{};
+}
+
+Return<bool> GnssBatching::start(const V1_0::IGnssBatching::Options&) {
+    // TODO implement
+    return bool{};
+}
+
+Return<void> GnssBatching::flush() {
+    // TODO implement
+    return Void();
+}
+
+Return<bool> GnssBatching::stop() {
+    // TODO implement
+    return bool{};
+}
+
+Return<void> GnssBatching::cleanup() {
+    // TODO implement
+    return Void();
+}
+
+// Methods from V2_0::IGnssBatching follow.
+Return<bool> GnssBatching::init_2_0(const sp<V2_0::IGnssBatchingCallback>& callback) {
+    sCallback = callback;
+    return true;
+}
+
+}  // namespace implementation
+}  // namespace V2_0
+}  // namespace gnss
+}  // namespace hardware
+}  // namespace android
diff --git a/gnss/2.0/default/GnssBatching.h b/gnss/2.0/default/GnssBatching.h
new file mode 100644
index 0000000..62ac580
--- /dev/null
+++ b/gnss/2.0/default/GnssBatching.h
@@ -0,0 +1,57 @@
+/*
+ * Copyright (C) 2019 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.
+ */
+
+#pragma once
+
+#include <android/hardware/gnss/2.0/IGnssBatching.h>
+#include <hidl/MQDescriptor.h>
+#include <hidl/Status.h>
+
+namespace android {
+namespace hardware {
+namespace gnss {
+namespace V2_0 {
+namespace implementation {
+
+using ::android::sp;
+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;
+
+struct GnssBatching : public IGnssBatching {
+    // Methods from ::android::hardware::gnss::V1_0::IGnssBatching follow.
+    Return<bool> init(const sp<V1_0::IGnssBatchingCallback>& callback) override;
+    Return<uint16_t> getBatchSize() override;
+    Return<bool> start(const V1_0::IGnssBatching::Options& options) override;
+    Return<void> flush() override;
+    Return<bool> stop() override;
+    Return<void> cleanup() override;
+
+    // Methods from V2_0::IGnssBatching follow.
+    Return<bool> init_2_0(const sp<V2_0::IGnssBatchingCallback>& callback) override;
+
+  private:
+    static sp<IGnssBatchingCallback> sCallback;
+};
+
+}  // namespace implementation
+}  // namespace V2_0
+}  // namespace gnss
+}  // namespace hardware
+}  // namespace android
diff --git a/gnss/2.0/default/GnssConfiguration.cpp b/gnss/2.0/default/GnssConfiguration.cpp
index 4389dd2..6bf1712 100644
--- a/gnss/2.0/default/GnssConfiguration.cpp
+++ b/gnss/2.0/default/GnssConfiguration.cpp
@@ -33,13 +33,11 @@
 }
 
 Return<bool> GnssConfiguration::setSuplVersion(uint32_t) {
-    // TODO implement
-    return bool{};
+    return true;
 }
 
 Return<bool> GnssConfiguration::setSuplMode(hidl_bitfield<SuplMode>) {
-    // TODO implement
-    return bool{};
+    return true;
 }
 
 Return<bool> GnssConfiguration::setGpsLock(hidl_bitfield<GpsLock> gpsLock) {
@@ -49,18 +47,15 @@
 }
 
 Return<bool> GnssConfiguration::setLppProfile(hidl_bitfield<LppProfile>) {
-    // TODO implement
-    return bool{};
+    return true;
 }
 
 Return<bool> GnssConfiguration::setGlonassPositioningProtocol(hidl_bitfield<GlonassPosProtocol>) {
-    // TODO implement
-    return bool{};
+    return true;
 }
 
 Return<bool> GnssConfiguration::setEmergencySuplPdn(bool) {
-    // TODO implement
-    return bool{};
+    return true;
 }
 
 // Methods from ::android::hardware::gnss::V1_1::IGnssConfiguration follow.
diff --git a/gnss/2.0/default/GnssMeasurement.cpp b/gnss/2.0/default/GnssMeasurement.cpp
index a62c2dd..93de89c 100644
--- a/gnss/2.0/default/GnssMeasurement.cpp
+++ b/gnss/2.0/default/GnssMeasurement.cpp
@@ -26,7 +26,7 @@
 namespace V2_0 {
 namespace implementation {
 
-using GnssConstellationType = V1_0::GnssConstellationType;
+using GnssConstellationType = V2_0::GnssConstellationType;
 using GnssMeasurementFlags = V1_0::IGnssMeasurementCallback::GnssMeasurementFlags;
 using GnssMeasurementState = V2_0::IGnssMeasurementCallback::GnssMeasurementState;
 
@@ -46,6 +46,7 @@
 }
 
 Return<void> GnssMeasurement::close() {
+    ALOGD("close");
     std::unique_lock<std::mutex> lock(mMutex);
     stop();
     sCallback = nullptr;
@@ -62,6 +63,7 @@
 // Methods from V2_0::IGnssMeasurement follow.
 Return<V1_0::IGnssMeasurement::GnssMeasurementStatus> GnssMeasurement::setCallback_2_0(
     const sp<V2_0::IGnssMeasurementCallback>& callback, bool) {
+    ALOGD("setCallback_2_0");
     std::unique_lock<std::mutex> lock(mMutex);
     sCallback = callback;
 
@@ -75,6 +77,7 @@
 }
 
 void GnssMeasurement::start() {
+    ALOGD("start");
     mIsActive = true;
     mThread = std::thread([this]() {
         while (mIsActive == true) {
@@ -87,6 +90,7 @@
 }
 
 void GnssMeasurement::stop() {
+    ALOGD("stop");
     mIsActive = false;
     if (mThread.joinable()) {
         mThread.join();
@@ -95,26 +99,27 @@
 
 GnssData GnssMeasurement::getMockMeasurement() {
     V1_0::IGnssMeasurementCallback::GnssMeasurement measurement_1_0 = {
-        .flags = (uint32_t)GnssMeasurementFlags::HAS_CARRIER_FREQUENCY,
-        .svid = (int16_t)6,
-        .constellation = GnssConstellationType::GLONASS,
-        .timeOffsetNs = 0.0,
-        .receivedSvTimeInNs = 8195997131077,
-        .receivedSvTimeUncertaintyInNs = 15,
-        .cN0DbHz = 30.0,
-        .pseudorangeRateMps = -484.13739013671875,
-        .pseudorangeRateUncertaintyMps = 1.0379999876022339,
-        .accumulatedDeltaRangeState = (uint32_t)
-            V1_0::IGnssMeasurementCallback::GnssAccumulatedDeltaRangeState::ADR_STATE_UNKNOWN,
-        .accumulatedDeltaRangeM = 0.0,
-        .accumulatedDeltaRangeUncertaintyM = 0.0,
-        .carrierFrequencyHz = 1.59975e+09,
-        .multipathIndicator =
-            V1_0::IGnssMeasurementCallback::GnssMultipathIndicator::INDICATOR_UNKNOWN};
+            .flags = (uint32_t)GnssMeasurementFlags::HAS_CARRIER_FREQUENCY,
+            .svid = (int16_t)6,
+            .constellation = V1_0::GnssConstellationType::UNKNOWN,
+            .timeOffsetNs = 0.0,
+            .receivedSvTimeInNs = 8195997131077,
+            .receivedSvTimeUncertaintyInNs = 15,
+            .cN0DbHz = 30.0,
+            .pseudorangeRateMps = -484.13739013671875,
+            .pseudorangeRateUncertaintyMps = 1.0379999876022339,
+            .accumulatedDeltaRangeState = (uint32_t)V1_0::IGnssMeasurementCallback::
+                    GnssAccumulatedDeltaRangeState::ADR_STATE_UNKNOWN,
+            .accumulatedDeltaRangeM = 0.0,
+            .accumulatedDeltaRangeUncertaintyM = 0.0,
+            .carrierFrequencyHz = 1.59975e+09,
+            .multipathIndicator =
+                    V1_0::IGnssMeasurementCallback::GnssMultipathIndicator::INDICATOR_UNKNOWN};
     V1_1::IGnssMeasurementCallback::GnssMeasurement measurement_1_1 = {.v1_0 = measurement_1_0};
     V2_0::IGnssMeasurementCallback::GnssMeasurement measurement_2_0 = {
             .v1_1 = measurement_1_1,
             .codeType = "C",
+            .constellation = GnssConstellationType::GLONASS,
             .state = GnssMeasurementState::STATE_CODE_LOCK | GnssMeasurementState::STATE_BIT_SYNC |
                      GnssMeasurementState::STATE_SUBFRAME_SYNC |
                      GnssMeasurementState::STATE_TOW_DECODED |
diff --git a/gnss/2.0/types.hal b/gnss/2.0/types.hal
index 21b64f9..3865727 100644
--- a/gnss/2.0/types.hal
+++ b/gnss/2.0/types.hal
@@ -72,4 +72,28 @@
      * needs to be estimated by syncing the notion of time via PTP or some other mechanism.
      */
     ElapsedRealtime elapsedRealtime;
-};
\ No newline at end of file
+};
+
+/**
+ * GNSS constellation type
+ *
+ * This is to specify the navigation satellite system, for example, as listed in Section 3.5 in
+ * RINEX Version 3.04.
+ */
+enum GnssConstellationType : uint8_t {
+    UNKNOWN = 0,
+    /** Global Positioning System. */
+    GPS     = 1,
+    /** Satellite-Based Augmentation System. */
+    SBAS    = 2,
+    /** Global Navigation Satellite System. */
+    GLONASS = 3,
+    /** Quasi-Zenith Satellite System. */
+    QZSS    = 4,
+    /** BeiDou Navigation Satellite System. */
+    BEIDOU  = 5,
+    /** Galileo Navigation Satellite System. */
+    GALILEO = 6,
+    /** Indian Regional Navigation Satellite System. */
+    IRNSS   = 7,
+};
diff --git a/gnss/2.0/vts/functional/gnss_hal_test.cpp b/gnss/2.0/vts/functional/gnss_hal_test.cpp
index b2b62fc..da6092b 100644
--- a/gnss/2.0/vts/functional/gnss_hal_test.cpp
+++ b/gnss/2.0/vts/functional/gnss_hal_test.cpp
@@ -26,6 +26,7 @@
 GnssHalTest::GnssHalTest()
     : info_called_count_(0),
       capabilities_called_count_(0),
+      measurement_corrections_capabilities_called_count_(0),
       location_called_count_(0),
       name_called_count_(0),
       notify_count_(0) {}
@@ -33,7 +34,7 @@
 void GnssHalTest::SetUp() {
     gnss_hal_ = ::testing::VtsHalHidlTargetTestBase::getService<IGnss>(
         GnssHidlEnvironment::Instance()->getServiceName<IGnss>());
-    list_gnss_sv_status_.clear();
+    list_vec_gnss_sv_info_.clear();
     ASSERT_NE(gnss_hal_, nullptr);
 
     SetUpGnssCallback();
@@ -43,6 +44,7 @@
     // Reset counters
     info_called_count_ = 0;
     capabilities_called_count_ = 0;
+    measurement_corrections_capabilities_called_count_ = 0;
     location_called_count_ = 0;
     name_called_count_ = 0;
     measurement_called_count_ = 0;
@@ -59,7 +61,7 @@
     gnss_cb_ = new GnssCallback(*this);
     ASSERT_NE(gnss_cb_, nullptr);
 
-    auto result = gnss_hal_->setCallback_1_1(gnss_cb_);
+    auto result = gnss_hal_->setCallback_2_0(gnss_cb_);
     if (!result.isOk()) {
         ALOGE("result of failed setCallback %s", result.description().c_str());
     }
@@ -77,16 +79,6 @@
     EXPECT_EQ(capabilities_called_count_, 1);
     EXPECT_EQ(info_called_count_, 1);
     EXPECT_EQ(name_called_count_, 1);
-
-    // Setup measurement corrections callback.
-    auto measurementCorrections = gnss_hal_->getExtensionMeasurementCorrections();
-    ASSERT_TRUE(measurementCorrections.isOk());
-    sp<IMeasurementCorrections> iMeasurementCorrections = measurementCorrections;
-    if (iMeasurementCorrections != nullptr) {
-        sp<IMeasurementCorrectionsCallback> iMeasurementCorrectionsCallback =
-                new GnssMeasurementCorrectionsCallback(*this);
-        iMeasurementCorrections->setCallback(iMeasurementCorrectionsCallback);
-    }
 }
 
 void GnssHalTest::StopAndClearLocations() {
@@ -193,11 +185,12 @@
         status = cv_.wait_for(lock, std::chrono::seconds(timeout_seconds));
         if (status == std::cv_status::timeout) return status;
     }
+    notify_count_--;
     return status;
 }
 
 Return<void> GnssHalTest::GnssCallback::gnssSetSystemInfoCb(
-    const IGnssCallback::GnssSystemInfo& info) {
+        const IGnssCallback_1_0::GnssSystemInfo& info) {
     ALOGI("Info received, year %d", info.yearOfHw);
     parent_.info_called_count_++;
     parent_.last_info_ = info;
@@ -248,10 +241,9 @@
     return Void();
 }
 
-Return<void> GnssHalTest::GnssCallback::gnssSvStatusCb(
-    const IGnssCallback::GnssSvStatus& svStatus) {
-    ALOGI("GnssSvStatus received");
-    parent_.list_gnss_sv_status_.emplace_back(svStatus);
+Return<void> GnssHalTest::GnssCallback::gnssSvStatusCb(const IGnssCallback_1_0::GnssSvStatus&) {
+    ALOGI("gnssSvStatusCb");
+
     return Void();
 }
 
@@ -272,3 +264,11 @@
     parent_.notify();
     return Void();
 }
+
+Return<void> GnssHalTest::GnssCallback::gnssSvStatusCb_2_0(
+        const hidl_vec<IGnssCallback_2_0::GnssSvInfo>& svInfoList) {
+    ALOGI("gnssSvStatusCb_2_0. Size = %d", (int)svInfoList.size());
+    parent_.list_vec_gnss_sv_info_.emplace_back(svInfoList);
+    parent_.notify();
+    return Void();
+}
diff --git a/gnss/2.0/vts/functional/gnss_hal_test.h b/gnss/2.0/vts/functional/gnss_hal_test.h
index 7354aea..737815f 100644
--- a/gnss/2.0/vts/functional/gnss_hal_test.h
+++ b/gnss/2.0/vts/functional/gnss_hal_test.h
@@ -25,17 +25,20 @@
 #include <list>
 #include <mutex>
 
+using android::hardware::hidl_vec;
 using android::hardware::Return;
 using android::hardware::Void;
 
 using android::hardware::gnss::measurement_corrections::V1_0::IMeasurementCorrectionsCallback;
 using android::hardware::gnss::V1_0::GnssLocationFlags;
 using android::hardware::gnss::V2_0::IGnss;
-using android::hardware::gnss::V2_0::IGnssCallback;
 
 using GnssLocation_1_0 = android::hardware::gnss::V1_0::GnssLocation;
 using GnssLocation_2_0 = android::hardware::gnss::V2_0::GnssLocation;
 
+using IGnssCallback_1_0 = android::hardware::gnss::V1_0::IGnssCallback;
+using IGnssCallback_2_0 = android::hardware::gnss::V2_0::IGnssCallback;
+
 using IGnssMeasurementCallback_1_0 = android::hardware::gnss::V1_0::IGnssMeasurementCallback;
 using IGnssMeasurementCallback_1_1 = android::hardware::gnss::V1_1::IGnssMeasurementCallback;
 using IGnssMeasurementCallback_2_0 = android::hardware::gnss::V2_0::IGnssMeasurementCallback;
@@ -77,8 +80,8 @@
     std::cv_status waitForMeasurementCorrectionsCapabilities(int timeout_seconds);
 
     /* Callback class for data & Event. */
-    class GnssCallback : public IGnssCallback {
-       public:
+    class GnssCallback : public IGnssCallback_2_0 {
+      public:
         GnssHalTest& parent_;
 
         GnssCallback(GnssHalTest& parent) : parent_(parent){};
@@ -86,7 +89,7 @@
         virtual ~GnssCallback() = default;
 
         // Dummy callback handlers
-        Return<void> gnssStatusCb(const IGnssCallback::GnssStatusValue /* status */) override {
+        Return<void> gnssStatusCb(const IGnssCallback_1_0::GnssStatusValue /* status */) override {
             return Void();
         }
         Return<void> gnssNmeaCb(int64_t /* timestamp */,
@@ -103,8 +106,8 @@
         Return<void> gnssNameCb(const android::hardware::hidl_string& name) override;
         Return<void> gnssLocationCb(const GnssLocation_1_0& location) override;
         Return<void> gnssSetCapabilitesCb(uint32_t capabilities) override;
-        Return<void> gnssSetSystemInfoCb(const IGnssCallback::GnssSystemInfo& info) override;
-        Return<void> gnssSvStatusCb(const IGnssCallback::GnssSvStatus& svStatus) override;
+        Return<void> gnssSetSystemInfoCb(const IGnssCallback_1_0::GnssSystemInfo& info) override;
+        Return<void> gnssSvStatusCb(const IGnssCallback_1_0::GnssSvStatus& svStatus) override;
 
         // New in v2.0
         Return<void> gnssLocationCb_2_0(const GnssLocation_2_0& location) override;
@@ -113,6 +116,8 @@
             return Void();
         }
         Return<void> gnssSetCapabilitiesCb_2_0(uint32_t capabilities) override;
+        Return<void> gnssSvStatusCb_2_0(
+                const hidl_vec<IGnssCallback_2_0::GnssSvInfo>& svInfoList) override;
 
       private:
         Return<void> gnssLocationCbImpl(const GnssLocation_2_0& location);
@@ -198,7 +203,7 @@
     void SetPositionMode(const int min_interval_msec, const bool low_power_mode);
 
     sp<IGnss> gnss_hal_;         // GNSS HAL to call into
-    sp<IGnssCallback> gnss_cb_;  // Primary callback interface
+    sp<IGnssCallback_2_0> gnss_cb_;  // Primary callback interface
 
     // TODO: make these variables thread-safe.
     /* Count of calls to set the following items, and the latest item (used by
@@ -211,16 +216,16 @@
     int measurement_called_count_;
     int name_called_count_;
 
-    IGnssCallback::GnssSystemInfo last_info_;
+    IGnssCallback_1_0::GnssSystemInfo last_info_;
     uint32_t last_capabilities_;
     uint32_t last_measurement_corrections_capabilities_;
     GnssLocation_2_0 last_location_;
     IGnssMeasurementCallback_2_0::GnssData last_measurement_;
     android::hardware::hidl_string last_name_;
 
-    list<IGnssCallback::GnssSvStatus> list_gnss_sv_status_;
+    list<hidl_vec<IGnssCallback_2_0::GnssSvInfo>> list_vec_gnss_sv_info_;
 
-   private:
+  private:
     std::mutex mtx_;
     std::condition_variable cv_;
     int notify_count_;
diff --git a/gnss/2.0/vts/functional/gnss_hal_test_cases.cpp b/gnss/2.0/vts/functional/gnss_hal_test_cases.cpp
index f3559c5..0682f84 100644
--- a/gnss/2.0/vts/functional/gnss_hal_test_cases.cpp
+++ b/gnss/2.0/vts/functional/gnss_hal_test_cases.cpp
@@ -32,12 +32,15 @@
 using IAGnss_2_0 = android::hardware::gnss::V2_0::IAGnss;
 using IAGnss_1_0 = android::hardware::gnss::V1_0::IAGnss;
 using IAGnssCallback_2_0 = android::hardware::gnss::V2_0::IAGnssCallback;
+using IGnssBatching_V1_0 = android::hardware::gnss::V1_0::IGnssBatching;
+using IGnssBatching_V2_0 = android::hardware::gnss::V2_0::IGnssBatching;
 
 using android::hardware::gnss::common::Utils;
 using android::hardware::gnss::measurement_corrections::V1_0::IMeasurementCorrections;
 using android::hardware::gnss::measurement_corrections::V1_0::MeasurementCorrections;
 using android::hardware::gnss::V1_0::IGnssNi;
 using android::hardware::gnss::V2_0::ElapsedRealtimeFlags;
+using android::hardware::gnss::V2_0::GnssConstellationType;
 using android::hardware::gnss::V2_0::IGnssCallback;
 using android::hardware::gnss::visibility_control::V1_0::IGnssVisibilityControl;
 
@@ -163,10 +166,13 @@
 }
 
 /*
- * TestGnssMeasurementCodeType:
- * Sets a GnssMeasurementCallback, waits for a measurement, and verifies the codeType is valid.
+ * TestGnssMeasurementFields:
+ * Sets a GnssMeasurementCallback, waits for a measurement, and verifies
+ * 1. codeType is valid,
+ * 2. constellation is valid.
+ * 3. state is valid.
  */
-TEST_F(GnssHalTest, TestGnssMeasurementCodeType) {
+TEST_F(GnssHalTest, TestGnssMeasurementFields) {
     const int kFirstGnssMeasurementTimeoutSeconds = 10;
 
     auto gnssMeasurement = gnss_hal_->getExtensionGnssMeasurement_2_0();
@@ -189,7 +195,23 @@
     EXPECT_EQ(measurement_called_count_, 1);
     ASSERT_TRUE(last_measurement_.measurements.size() > 0);
     for (auto measurement : last_measurement_.measurements) {
+        // Verify CodeType is valid.
         ASSERT_NE(measurement.codeType, "");
+
+        // Verify ConstellationType is valid.
+        ASSERT_TRUE(static_cast<uint8_t>(measurement.constellation) >=
+                            static_cast<uint8_t>(GnssConstellationType::UNKNOWN) &&
+                    static_cast<uint8_t>(measurement.constellation) <=
+                            static_cast<uint8_t>(GnssConstellationType::IRNSS));
+
+        // Verify State is valid.
+        ASSERT_TRUE(
+                static_cast<uint32_t>(measurement.state) >=
+                        static_cast<uint32_t>(IGnssMeasurementCallback_2_0::GnssMeasurementState::
+                                                      STATE_UNKNOWN) &&
+                static_cast<uint32_t>(measurement.state) <=
+                        static_cast<uint32_t>(IGnssMeasurementCallback_2_0::GnssMeasurementState::
+                                                      STATE_2ND_CODE_LOCK));
     }
 
     iGnssMeasurement->close();
@@ -272,6 +294,7 @@
  * capability flag is set.
  */
 TEST_F(GnssHalTest, TestGnssMeasurementCorrectionsCapabilities) {
+    // Setup measurement corrections callback.
     auto measurementCorrections = gnss_hal_->getExtensionMeasurementCorrections();
     ASSERT_TRUE(measurementCorrections.isOk());
     sp<IMeasurementCorrections> iMeasurementCorrections = measurementCorrections;
@@ -279,6 +302,10 @@
         return;
     }
 
+    sp<IMeasurementCorrectionsCallback> iMeasurementCorrectionsCallback =
+            new GnssMeasurementCorrectionsCallback(*this);
+    iMeasurementCorrections->setCallback(iMeasurementCorrectionsCallback);
+
     const int kMeasurementCorrectionsCapabilitiesTimeoutSeconds = 5;
     waitForMeasurementCorrectionsCapabilities(kMeasurementCorrectionsCapabilitiesTimeoutSeconds);
     ASSERT_TRUE(measurement_corrections_capabilities_called_count_ > 0);
@@ -301,6 +328,10 @@
         return;
     }
 
+    sp<IMeasurementCorrectionsCallback> iMeasurementCorrectionsCallback =
+            new GnssMeasurementCorrectionsCallback(*this);
+    iMeasurementCorrections->setCallback(iMeasurementCorrectionsCallback);
+
     const int kMeasurementCorrectionsCapabilitiesTimeoutSeconds = 5;
     waitForMeasurementCorrectionsCapabilities(kMeasurementCorrectionsCapabilitiesTimeoutSeconds);
     ASSERT_TRUE(measurement_corrections_capabilities_called_count_ > 0);
@@ -337,9 +368,9 @@
     wait(kFirstGnssMeasurementTimeoutSeconds);
     EXPECT_EQ(measurement_called_count_, 1);
 
-    ASSERT_TRUE((int)last_measurement_.elapsedRealtime.flags >= 0 &&
-                (int)last_measurement_.elapsedRealtime.flags <=
-                        (int)ElapsedRealtimeFlags::HAS_TIME_UNCERTAINTY_NS);
+    ASSERT_TRUE((int)last_measurement_.elapsedRealtime.flags <=
+                (int)(ElapsedRealtimeFlags::HAS_TIMESTAMP_NS |
+                      ElapsedRealtimeFlags::HAS_TIME_UNCERTAINTY_NS));
 
     // We expect a non-zero timestamp when set.
     if (last_measurement_.elapsedRealtime.flags & ElapsedRealtimeFlags::HAS_TIMESTAMP_NS) {
@@ -352,9 +383,9 @@
 TEST_F(GnssHalTest, TestGnssLocationElapsedRealtime) {
     StartAndCheckFirstLocation();
 
-    ASSERT_TRUE((int)last_location_.elapsedRealtime.flags >= 0 &&
-                (int)last_location_.elapsedRealtime.flags <=
-                        (int)ElapsedRealtimeFlags::HAS_TIME_UNCERTAINTY_NS);
+    ASSERT_TRUE((int)last_location_.elapsedRealtime.flags <=
+                (int)(ElapsedRealtimeFlags::HAS_TIMESTAMP_NS |
+                      ElapsedRealtimeFlags::HAS_TIME_UNCERTAINTY_NS));
 
     // We expect a non-zero timestamp when set.
     if (last_location_.elapsedRealtime.flags & ElapsedRealtimeFlags::HAS_TIMESTAMP_NS) {
@@ -370,3 +401,20 @@
     gnss_hal_->injectBestLocation_2_0(last_location_);
     StopAndClearLocations();
 }
+
+/*
+ * TestGnssBatchingExtension:
+ * Gets the GnssBatchingExtension and verifies that it supports either the @1.0::IGnssBatching
+ * or @2.0::IGnssBatching extension.
+ */
+TEST_F(GnssHalTest, TestGnssBatchingExtension) {
+    auto gnssBatching_V2_0 = gnss_hal_->getExtensionGnssBatching_2_0();
+    ASSERT_TRUE(gnssBatching_V2_0.isOk());
+
+    auto gnssBatching_V1_0 = gnss_hal_->getExtensionGnssBatching();
+    ASSERT_TRUE(gnssBatching_V1_0.isOk());
+
+    sp<IGnssBatching_V1_0> iGnssBatching_V1_0 = gnssBatching_V1_0;
+    sp<IGnssBatching_V2_0> iGnssBatching_V2_0 = gnssBatching_V2_0;
+    ASSERT_TRUE(iGnssBatching_V1_0 != nullptr || iGnssBatching_V2_0 != nullptr);
+}
diff --git a/keymaster/4.0/support/Keymaster.cpp b/keymaster/4.0/support/Keymaster.cpp
index 9325cc0..e8db074 100644
--- a/keymaster/4.0/support/Keymaster.cpp
+++ b/keymaster/4.0/support/Keymaster.cpp
@@ -106,6 +106,19 @@
     return result;
 }
 
+void Keymaster::logIfKeymasterVendorError(ErrorCode ec) const {
+    static constexpr int32_t k_keymaster_vendor_error_code_range_max = -10000;
+    if (static_cast<int32_t>(ec) <= k_keymaster_vendor_error_code_range_max) {
+        const auto& versionInfo = halVersion();
+        LOG(ERROR) << "Keymaster reported error: " << static_cast<int32_t>(ec) << "\n"
+                   << "NOTE: This is an error in the vendor specific error range.\n"
+                   << "      Refer to the vendor of the implementation for details.\n"
+                   << "      Implementation name: " << versionInfo.keymasterName << "\n"
+                   << "      Vendor name:         " << versionInfo.authorName << "\n"
+                   << "      MajorVersion:        " << versionInfo.majorVersion;
+    }
+}
+
 Keymaster::KeymasterSet Keymaster::enumerateAvailableDevices() {
     auto serviceManager = IServiceManager::getService();
     CHECK(serviceManager) << "Could not retrieve ServiceManager";
diff --git a/keymaster/4.0/support/include/keymasterV4_0/Keymaster.h b/keymaster/4.0/support/include/keymasterV4_0/Keymaster.h
index 458053a..43a34b0 100644
--- a/keymaster/4.0/support/include/keymasterV4_0/Keymaster.h
+++ b/keymaster/4.0/support/include/keymasterV4_0/Keymaster.h
@@ -65,6 +65,12 @@
     const hidl_string& instanceName() const { return instanceName_; }
 
     /**
+     * If ec is in the vendor error code range (<-10000), logs the fact to logcat.
+     * There are no side effects otherwise.
+     */
+    void logIfKeymasterVendorError(ErrorCode ec) const;
+
+    /**
      * Returns all available Keymaster3 and Keymaster4 instances, in order of most secure to least
      * secure (as defined by VersionResult::operator<).
      */
diff --git a/keymaster/4.0/support/keymaster_utils.cpp b/keymaster/4.0/support/keymaster_utils.cpp
index 729e1c1..e35fdd3 100644
--- a/keymaster/4.0/support/keymaster_utils.cpp
+++ b/keymaster/4.0/support/keymaster_utils.cpp
@@ -21,7 +21,9 @@
 namespace hardware {
 
 inline static bool operator<(const hidl_vec<uint8_t>& a, const hidl_vec<uint8_t>& b) {
-    return memcmp(a.data(), b.data(), std::min(a.size(), b.size())) == -1;
+    auto result = memcmp(a.data(), b.data(), std::min(a.size(), b.size()));
+    if (!result) return a.size() < b.size();
+    return result < 0;
 }
 
 template <size_t SIZE>
diff --git a/neuralnetworks/1.0/vts/functional/GeneratedTestHarness.cpp b/neuralnetworks/1.0/vts/functional/GeneratedTestHarness.cpp
index f5cb0d7..106f332 100644
--- a/neuralnetworks/1.0/vts/functional/GeneratedTestHarness.cpp
+++ b/neuralnetworks/1.0/vts/functional/GeneratedTestHarness.cpp
@@ -52,6 +52,7 @@
 using ::test_helper::MixedTyped;
 using ::test_helper::MixedTypedExample;
 using ::test_helper::resize_accordingly;
+using HidlToken = hidl_array<uint8_t, static_cast<uint32_t>(Constant::BYTE_SIZE_OF_CACHE_TOKEN)>;
 
 template <typename T>
 void copy_back_(std::map<int, std::vector<T>>* dst, const std::vector<RequestArgument>& ra,
@@ -540,7 +541,8 @@
     sp<PreparedModelCallback> preparedModelCallback = new PreparedModelCallback();
     ASSERT_NE(nullptr, preparedModelCallback.get());
     Return<ErrorStatus> prepareLaunchStatus = device->prepareModel_1_2(
-        model, ExecutionPreference::FAST_SINGLE_ANSWER, preparedModelCallback);
+            model, ExecutionPreference::FAST_SINGLE_ANSWER, hidl_vec<hidl_handle>(),
+            hidl_vec<hidl_handle>(), HidlToken(), preparedModelCallback);
     ASSERT_TRUE(prepareLaunchStatus.isOk());
     ASSERT_EQ(ErrorStatus::NONE, static_cast<ErrorStatus>(prepareLaunchStatus));
 
diff --git a/neuralnetworks/1.2/IDevice.hal b/neuralnetworks/1.2/IDevice.hal
index b9fa388..d83f9e6 100644
--- a/neuralnetworks/1.2/IDevice.hal
+++ b/neuralnetworks/1.2/IDevice.hal
@@ -76,6 +76,17 @@
     getType() generates (ErrorStatus status, DeviceType type);
 
     /**
+     * Gets the capabilities of a driver.
+     *
+     * @return status Error status of the call, must be:
+     *                - NONE if successful
+     *                - DEVICE_UNAVAILABLE if driver is offline or busy
+     *                - GENERAL_FAILURE if there is an unspecified error
+     * @return capabilities Capabilities of the driver.
+     */
+    getCapabilities_1_2() generates (ErrorStatus status, Capabilities capabilities);
+
+    /**
      * Gets information about extensions supported by the driver implementation.
      *
      * All extension operations and operands must be fully supported for the
@@ -113,44 +124,83 @@
             generates (ErrorStatus status, vec<bool> supportedOperations);
 
     /**
-     * Gets whether the driver supports compilation caching.
+     * Gets the caching requirements of the driver implementation.
      *
-     * isCachingSupported indicates whether the driver supports compilation caching.
-     * Even if so, the driver may still choose not to cache certain compiled models.
+     * There are two types of cache file descriptors provided to the driver: model cache
+     * and data cache.
      *
-     * If the device reports the caching is not supported, the user may avoid calling
-     * IDevice::prepareModelFromCache and IPreparedModel::saveToCache.
+     * The data cache is for caching constant data, possibly including preprocessed
+     * and transformed tensor buffers. Any modification to the data cache should
+     * have no worse effect than generating bad output values at execution time.
+     *
+     * The model cache is for caching security-sensitive data such as compiled
+     * executable machine code in the device's native binary format. A modification
+     * to the model cache may affect the driver's execution behavior, and a malicious
+     * client could make use of this to execute beyond the granted permission. Thus,
+     * the driver must always check whether the model cache is corrupted before
+     * preparing the model from cache.
+     *
+     * getNumberOfCacheFilesNeeded returns how many of each type of cache files the driver
+     * implementation needs to cache a single prepared model. Returning 0 for both types
+     * indicates compilation caching is not supported by this driver. The driver may
+     * still choose not to cache certain compiled models even if it reports that caching
+     * is supported.
+     *
+     * If the device reports that caching is not supported, the user may avoid calling
+     * IDevice::prepareModelFromCache or providing cache file descriptors to
+     * IDevice::prepareModel_1_2.
      *
      * @return status Error status of the call, must be:
      *     - NONE if successful
      *     - DEVICE_UNAVAILABLE if driver is offline or busy
      *     - GENERAL_FAILURE if there is an unspecified error
-     * @return supported A boolean indicating whether the driver supports compilation
-     *                   caching. Even on returning true, the driver may still choose
-     *                   not to cache certain compiled models.
+     * @return numModelCache An unsigned integer indicating how many files for model cache
+     *                       the driver needs to cache a single prepared model. It must
+     *                       be less than or equal to Constant::MAX_NUMBER_OF_CACHE_FILES.
+     * @return numDataCache An unsigned integer indicating how many files for data cache
+     *                      the driver needs to cache a single prepared model. It must
+     *                      be less than or equal to Constant::MAX_NUMBER_OF_CACHE_FILES.
      */
-    isCachingSupported() generates (ErrorStatus status, bool supported);
+    getNumberOfCacheFilesNeeded()
+            generates (ErrorStatus status, uint32_t numModelCache, uint32_t numDataCache);
 
     /**
-     * Creates a prepared model for execution.
+     * Asynchronously creates a prepared model for execution and optionally saves it
+     * into cache files.
      *
-     * prepareModel is used to make any necessary transformations or alternative
+     * prepareModel is used to make any necessary transformations to or alternative
      * representations to a model for execution, possibly including
      * transformations on the constant data, optimization on the model's graph,
      * or compilation into the device's native binary format. The model itself
      * is not changed.
      *
+     * Optionally, caching information may be provided for the driver to save
+     * the prepared model to cache files for faster model compilation time
+     * when the same model preparation is requested in the future. There are
+     * two types of cache file handles provided to the driver: model cache
+     * and data cache. For more information on the two types of cache handles,
+     * refer to getNumberOfCacheFilesNeeded.
+     *
+     * The file descriptors must be opened with read and write permission. A file may
+     * have any size, and the corresponding file descriptor may have any offset. The
+     * driver must truncate a file to zero size before writing to that file. The file
+     * descriptors may be closed by the client once the asynchronous preparation has
+     * finished. The driver must dup a file descriptor if it wants to get access to
+     * the cache file later.
+     *
      * The model is prepared asynchronously with respect to the caller. The
-     * prepareModel function must verify the inputs to the prepareModel function
-     * are correct. If there is an error, prepareModel must immediately invoke
+     * prepareModel function must verify the inputs to the preparedModel function
+     * related to preparing the model (as opposed to saving the prepared model to
+     * cache) are correct. If there is an error, prepareModel must immediately invoke
      * the callback with the appropriate ErrorStatus value and nullptr for the
-     * IPreparedModel, then return with the same ErrorStatus. If the inputs to
-     * the prepareModel function are valid and there is no error, prepareModel
-     * must launch an asynchronous task to prepare the model in the background,
-     * and immediately return from prepareModel with ErrorStatus::NONE. If the
-     * asynchronous task fails to launch, prepareModel must immediately invoke
-     * the callback with ErrorStatus::GENERAL_FAILURE and nullptr for the
-     * IPreparedModel, then return with ErrorStatus::GENERAL_FAILURE.
+     * IPreparedModel, then return with the same ErrorStatus. If the inputs to the
+     * prepareModel function that are related to preparing the model are valid and
+     * there is no error, prepareModel must launch an asynchronous task
+     * to prepare the model in the background, and immediately return from
+     * prepareModel with ErrorStatus::NONE. If the asynchronous task fails to launch,
+     * prepareModel must immediately invoke the callback with
+     * ErrorStatus::GENERAL_FAILURE and nullptr for the IPreparedModel, then return
+     * with ErrorStatus::GENERAL_FAILURE.
      *
      * When the asynchronous task has finished preparing the model, it must
      * immediately invoke the callback function provided as an input to
@@ -160,6 +210,14 @@
      * the callback object must be invoked with the appropriate ErrorStatus
      * value and nullptr for the IPreparedModel.
      *
+     * Optionally, the driver may save the prepared model to cache during the
+     * asynchronous preparation. Any error that occurs when saving to cache must
+     * not affect the status of preparing the model. Even if the input arguments
+     * related to the cache may be invalid, or the driver may fail to save to cache,
+     * the prepareModel function must finish preparing the model. The driver
+     * may choose not to save to cache even if the caching information is
+     * provided and valid.
+     *
      * The only information that may be unknown to the model at this stage is
      * the shape of the tensors, which may only be known at execution time. As
      * such, some driver services may return partially prepared models, where
@@ -173,6 +231,26 @@
      * @param model The model to be prepared for execution.
      * @param preference Indicates the intended execution behavior of a prepared
      *     model.
+     * @param modelCache A vector of handles with each entry holding exactly one
+     *     cache file descriptor for the security-sensitive cache. The length of
+     *     the vector must either be 0 indicating that caching information is not provided,
+     *     or match the numModelCache returned from getNumberOfCacheFilesNeeded. The cache
+     *     handles will be provided in the same order when retrieving the
+     *     preparedModel from cache files with prepareModelFromCache.
+     * @param dataCache A vector of handles with each entry holding exactly one
+     *     cache file descriptor for the constants' cache. The length of
+     *     the vector must either be 0 indicating that caching information is not provided,
+     *     or match the numDataCache returned from getNumberOfCacheFilesNeeded. The cache
+     *     handles will be provided in the same order when retrieving the
+     *     preparedModel from cache files with prepareModelFromCache.
+     * @param token A caching token of length Constant::BYTE_SIZE_OF_CACHE_TOKEN
+     *     identifying the prepared model. The same token will be provided when retrieving
+     *     the prepared model from the cache files with prepareModelFromCache.
+     *     Tokens should be chosen to have a low rate of collision for a particular
+     *     application. The driver cannot detect a collision; a collision will result
+     *     in a failed execution or in a successful execution that produces incorrect
+     *     output values. If both modelCache and dataCache are empty indicating that
+     *     caching information is not provided, this token must be ignored.
      * @param callback A callback object used to return the error status of
      *     preparing the model for execution and the prepared model if
      *     successful, nullptr otherwise. The callback object's notify function
@@ -182,9 +260,12 @@
      *     - NONE if preparation task is successfully launched
      *     - DEVICE_UNAVAILABLE if driver is offline or busy
      *     - GENERAL_FAILURE if there is an unspecified error
-     *     - INVALID_ARGUMENT if one of the input arguments is invalid
+     *     - INVALID_ARGUMENT if one of the input arguments related to preparing the
+     *       model is invalid
      */
     prepareModel_1_2(Model model, ExecutionPreference preference,
+                     vec<handle> modelCache, vec<handle> dataCache,
+                     uint8_t[Constant:BYTE_SIZE_OF_CACHE_TOKEN] token,
                      IPreparedModelCallback callback)
           generates (ErrorStatus status);
 
@@ -192,22 +273,17 @@
      * Creates a prepared model from cache files for execution.
      *
      * prepareModelFromCache is used to retrieve a prepared model directly from
-     * cache files to avoid slow model compilation time. There are exactly two
-     * cache file descriptors provided to the driver: modelCache and dataCache.
+     * cache files to avoid slow model compilation time. There are
+     * two types of cache file handles provided to the driver: model cache
+     * and data cache. For more information on the two types of cache handles,
+     * refer to getNumberOfCacheFilesNeeded.
      *
-     * The dataCache is for caching constant data, possibly including preprocessed
-     * and transformed tensor buffers. Any modification to the dataCache should
-     * have no worse effect than generating bad output values at execution time.
-     *
-     * The modelCache is for caching security-sensitive data such as compiled
-     * executable machine code in the device's native binary format. A modification
-     * to the modelCache may affect the driver's execution behavior, and a malicious
-     * client could make use of this to execute beyond the granted permission. Thus,
-     * the driver must always check whether the modelCache is corrupted before preparing
-     * the model from cache.
-     *
-     * The two file descriptors may be closed by the client once the asynchronous
-     * preparation has finished. The driver has to copy all the data it needs.
+     * The file descriptors must be opened with read and write permission. A file may
+     * have any size, and the corresponding file descriptor may have any offset. The
+     * driver must truncate a file to zero size before writing to that file. The file
+     * descriptors may be closed by the client once the asynchronous preparation has
+     * finished. The driver must dup a file descriptor if it wants to get access to
+     * the cache file later.
      *
      * The model is prepared asynchronously with respect to the caller. The
      * prepareModelFromCache function must verify the inputs to the
@@ -241,13 +317,17 @@
      * used with different shapes of inputs on different (possibly concurrent)
      * executions.
      *
-     * @param modelCache A handle holding exactly one cache file descriptor for the
-     *     security-sensitive cache.
-     * @param dataCache A handle holding exactly one cache file descriptor for the
-     *     constants' cache.
+     * @param modelCache A vector of handles with each entry holding exactly one
+     *     cache file descriptor for the security-sensitive cache. The length of
+     *     the vector must match the numModelCache returned from getNumberOfCacheFilesNeeded.
+     *     The cache handles will be provided in the same order as with prepareModel_1_2.
+     * @param dataCache A vector of handles with each entry holding exactly one
+     *     cache file descriptor for the constants' cache. The length of the vector
+     *     must match the numDataCache returned from getNumberOfCacheFilesNeeded.
+     *     The cache handles will be provided in the same order as with prepareModel_1_2.
      * @param token A caching token of length Constant::BYTE_SIZE_OF_CACHE_TOKEN
      *     identifying the prepared model. It is the same token provided when saving
-     *     the cache files with IPreparedModel::saveToCache. Tokens should be chosen
+     *     the cache files with prepareModel_1_2. Tokens should be chosen
      *     to have a low rate of collision for a particular application. The driver
      *     cannot detect a collision; a collision will result in a failed execution
      *     or in a successful execution that produces incorrect output values.
@@ -263,7 +343,7 @@
      *       unspecified error
      *     - INVALID_ARGUMENT if one of the input arguments is invalid
      */
-    prepareModelFromCache(handle modelCache, handle dataCache,
+    prepareModelFromCache(vec<handle> modelCache, vec<handle> dataCache,
                           uint8_t[Constant:BYTE_SIZE_OF_CACHE_TOKEN] token,
                           IPreparedModelCallback callback)
             generates (ErrorStatus status);
diff --git a/neuralnetworks/1.2/IPreparedModel.hal b/neuralnetworks/1.2/IPreparedModel.hal
index 757d5f1..5d2d80f 100644
--- a/neuralnetworks/1.2/IPreparedModel.hal
+++ b/neuralnetworks/1.2/IPreparedModel.hal
@@ -157,62 +157,4 @@
                             fmq_sync<FmqRequestDatum> requestChannel,
                             fmq_sync<FmqResultDatum> resultChannel)
                  generates (ErrorStatus status, IBurstContext context);
-
-    /*
-     * Saves the prepared model to cache files.
-     *
-     * saveToCache is used to save a prepared model to cache files for faster
-     * model compilation time when the same model preparation is requested in
-     * the future. There are exactly two cache file descriptors provided to the
-     * driver: modelCache and dataCache.
-     *
-     * The dataCache is for caching constant data, possibly including preprocessed
-     * and transformed tensor buffers. Any modification to the dataCache should
-     * have no worse effect than generating bad output values at execution time.
-     *
-     * The modelCache is for caching security-sensitive data such as compiled
-     * executable machine code in the device's native binary format. A modification
-     * to the modelCache may affect the driver's execution behavior, and a malicious
-     * client could make use of this to execute beyond the granted permission. Thus,
-     * the driver must always check whether the modelCache is corrupted before preparing
-     * the model from cache.
-     *
-     * The two file descriptors must point to two zero-length files with offset
-     * positioned at the beginning of the file. The file descriptors may be closed
-     * by the client once the method has returned.
-     *
-     * If the driver decides not to save the prepared model without looking at the
-     * input arguments to the saveToCache function, saveToCache must return with
-     * ErrorStatus::GENERAL_FAILURE. Otherwise, the saveToCache function must verify
-     * the input arguments to the saveToCache function are valid, and return with
-     * ErrorStatus::INVALID_ARGUMENT if not. If the inputs are valid but the driver
-     * could not save the prepared model, saveToCache must return with the appropriate
-     * ErrorStatus. Otherwise, it must write the cache files and return
-     * ErrorStatus::NONE. Unless saveToCache returns ErrorStatus::NONE, the contents
-     * of the cache files are undefined.
-     *
-     * @param modelCache A handle holding exactly one cache file descriptor for the
-     *                   security-sensitive cache.
-     * @param dataCache A handle holding exactly one cache file descriptor for the
-     *                  constants' cache.
-     * @param token A caching token of length Constant::BYTE_SIZE_OF_CACHE_TOKEN
-     *              identifying the prepared model. The same token will be provided
-     *              when retrieving the prepared model from cache files with
-     *              IDevice::prepareModelFromCache. Tokens should be chosen to have
-     *              a low rate of collision for a particular application. The driver
-     *              cannot detect a collision; a collision will result in a failed
-     *              execution or in a successful execution that produces incorrect
-     *              output values.
-     * @return status Error status of saveToCache, must be:
-     *                - NONE if saveToCache is performed successfully
-     *                - DEVICE_UNAVAILABLE if driver is offline or busy
-     *                - GENERAL_FAILURE if the driver could not save the
-     *                  prepared model or if there is an unspecified error
-     *                - INVALID_ARGUMENT if one of the input arguments is invalid,
-     *                  unless the driver decides not to save the prepared model
-     *                  without looking at the input arguments
-     */
-    saveToCache(handle modelCache, handle dataCache,
-                uint8_t[Constant:BYTE_SIZE_OF_CACHE_TOKEN] token)
-        generates (ErrorStatus status);
 };
diff --git a/neuralnetworks/1.2/types.hal b/neuralnetworks/1.2/types.hal
index f2e02b8..8c57796 100644
--- a/neuralnetworks/1.2/types.hal
+++ b/neuralnetworks/1.2/types.hal
@@ -30,6 +30,11 @@
      * The byte size of the cache token.
      */
     BYTE_SIZE_OF_CACHE_TOKEN = 32,
+
+    /**
+     * The maximum number of files for each type of cache in compilation caching.
+     */
+    MAX_NUMBER_OF_CACHE_FILES = 32,
 };
 
 enum OperandType : @1.0::OperandType {
@@ -182,6 +187,10 @@
      *     input2.dimension = {5, 4, 3, 1}
      *     output.dimension = {5, 4, 3, 2}
      *
+     * Since API level 29, generic zero-sized input tensor is supported. Zero
+     * dimension is only compatible with 0 or 1. The size of the output
+     * dimension is zero if either of corresponding input dimension is zero.
+     *
      * Supported tensor {@link OperandType}:
      * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
      * * {@link OperandType::TENSOR_FLOAT32}
@@ -231,7 +240,8 @@
      *
      * Inputs (explicit padding):
      * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
-     *      the input.
+     *      the input. Since API level 29, zero batches is supported for this
+     *      tensor.
      * * 1: An {@link OperandType::INT32} scalar, specifying the padding on
      *      the left, in the ‘width’ dimension.
      * * 2: An {@link OperandType::INT32} scalar, specifying the padding on
@@ -257,7 +267,8 @@
      *
      * Inputs (implicit padding):
      * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
-     *      the input.
+     *      the input. Since API level 29, zero batches is supported for this
+     *      tensor.
      * * 1: An {@link OperandType::INT32} scalar, specifying the implicit
      *      padding scheme, has to be one of the
      *      following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
@@ -304,6 +315,7 @@
      *            Before API level 29, all input tensors of
      *            {@link OperandType::TENSOR_QUANT8_ASYMM}
      *            must have the same scale and zeroPoint as the output tensor.
+     *            Since API level 29, zero-sized tensors are supported.
      * * n: An {@link OperandType::INT32} scalar, specifying the
      *      concatenation axis.
      *
@@ -361,7 +373,8 @@
      *
      * Inputs (explicit padding):
      * * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
-     *      specifying the input.
+     *      specifying the input. Since API level 29, zero batches is supported
+     *      for this tensor.
      * * 1: A 4-D tensor, of shape
      *      [depth_out, filter_height, filter_width, depth_in], specifying the
      *      filter. For tensor of type
@@ -408,7 +421,8 @@
      *
      * Inputs (implicit padding):
      * * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
-     *      specifying the input.
+     *      specifying the input. Since API level 29, zero batches is supported
+     *      for this tensor.
      * * 1: A 4-D tensor, of shape
      *      [depth_out, filter_height, filter_width, depth_in], specifying the
      *      filter. For tensor of type
@@ -450,11 +464,10 @@
      *
      * Outputs:
      * * 0: The output 4-D tensor, of shape
-     *      [batches, out_height, out_width, depth_out]. For output tensor of
-     *      {@link OperandType::TENSOR_QUANT8_ASYMM}, the following condition
-     *      must be satisfied: output_scale > input_scale * filter_scale (for
-     *      filter tensor of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}
-     *      this condition must be true for all filter scales).
+     *      [batches, out_height, out_width, depth_out]. Before API level 29,
+     *      for output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}, the
+     *      following condition must be satisfied:
+     *      output_scale > input_scale * filter_scale
      *
      * Available since API level 27.
      */
@@ -600,11 +613,10 @@
      *
      * Outputs:
      * * 0: The output 4-D tensor, of shape
-     *      [batches, out_height, out_width, depth_out]. For output tensor of
-     *      {@link OperandType::TENSOR_QUANT8_ASYMM}, the following condition
-     *      must be satisfied: output_scale > input_scale * filter_scale (for
-     *      filter tensor of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}
-     *      this condition must be true for all filter scales).
+     *      [batches, out_height, out_width, depth_out]. Before API level 29,
+     *      for output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}, the
+     *      following condition must be satisfied:
+     *      output_scale > input_scale * filter_scale
      *
      * Available since API level 27.
      */
@@ -672,7 +684,7 @@
      * Supported tensor rank: up to 4
      *
      * Inputs:
-     * * 0: A tensor.
+     * * 0: A tensor. Since API level 29, this tensor may be zero-sized.
      *
      * Outputs:
      * * 0: A tensor with the same shape as input0.
@@ -765,7 +777,8 @@
      *      [batch_size, input_size], where "input_size" corresponds to the
      *      number of inputs to the layer, matching the second dimension of
      *      weights, and "batch_size" is calculated by dividing the number of
-     *      elements by "input_size".
+     *      elements by "input_size". Since API level 29, zero batch_size is
+     *      supported for this tensor.
      * * 1: A 2-D tensor, specifying the weights, of shape
      *      [num_units, input_size], where "num_units" corresponds to the number
      *      of output nodes.
@@ -780,10 +793,10 @@
      *      invoke on the result.
      *
      * Outputs:
-     * * 0: The output tensor, of shape [batch_size, num_units]. For output
-     *      tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}, the following
-     *      condition must be satisfied:
-     *      output_scale > input_scale * filter_scale.
+     * * 0: The output tensor, of shape [batch_size, num_units]. Before API
+     *      level 29, For output tensor of {@link
+     *      OperandType::TENSOR_QUANT8_ASYMM}, the following condition must be
+     *      satisfied: output_scale > input_scale * filter_scale.
      *
      * Available since API level 27.
      */
@@ -861,6 +874,7 @@
      * Supported tensor {@link OperandType}:
      * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
      * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM} (since API level 29)
      *
      * Supported tensor rank: up to 4
      * Tensors with rank less than 4 are only supported since API level 29.
@@ -875,6 +889,8 @@
      *
      * Outputs:
      * * 0: A tensor of the same {@link OperandType} and same shape as input0.
+     *      For {@link OperandType::TENSOR_QUANT8_ASYMM},
+     *      the scale must be 1.f / 128 and the zeroPoint must be 128.
      *
      * Available since API level 27.
      */
@@ -905,7 +921,8 @@
      *
      * Inputs (explicit padding):
      * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
-     *      the input.
+     *      the input. Since API level 29, zero batches is supported for this
+     *      tensor.
      * * 1: An {@link OperandType::INT32} scalar, specifying the padding on
      *      the left, in the ‘width’ dimension.
      * * 2: An {@link OperandType::INT32} scalar, specifying the padding on
@@ -931,7 +948,8 @@
      *
      * Inputs (implicit padding):
      * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
-     *      the input.
+     *      the input. Since API level 29, zero batches is supported for this
+     *      tensor.
      * * 1: An {@link OperandType::INT32} scalar, specifying the implicit
      *      padding scheme, has to be one of the
      *      following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
@@ -1021,7 +1039,8 @@
      * Supported tensor rank: up to 4.
      *
      * Inputs:
-     * * 0: A tensor, specifying the input.
+     * * 0: A tensor, specifying the input. Since API level 29, this tensor may
+     *      be zero-sized.
      *
      * Outputs:
      * * 0: The output tensor of same shape as input0.
@@ -1333,7 +1352,8 @@
      *
      * Inputs (explicit padding):
      * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
-     *      the input.
+     *      the input. Since API level 29, zero batches is supported for this
+     *      tensor.
      * * 1: An {@link OperandType::INT32} scalar, specifying the padding on
      *      the left, in the ‘width’ dimension.
      * * 2: An {@link OperandType::INT32} scalar, specifying the padding on
@@ -1359,7 +1379,8 @@
      *
      * Inputs (implicit padding):
      * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
-     *      the input.
+     *      the input. Since API level 29, zero batches is supported for this
+     *      tensor.
      * * 1: An {@link OperandType::INT32} scalar, specifying the implicit
      *      padding scheme, has to be one of the
      *      following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
@@ -1406,6 +1427,10 @@
      * * {@link OperandType::TENSOR_FLOAT32}
      * * {@link OperandType::TENSOR_QUANT8_ASYMM}
      *
+     * Since API level 29, generic zero-sized input tensor is supported. Zero
+     * dimension is only compatible with 0 or 1. The size of the output
+     * dimension is zero if either of corresponding input dimension is zero.
+     *
      * Supported tensor rank: up to 4
      *
      * Inputs:
@@ -1441,7 +1466,8 @@
      * Supported tensor rank: up to 4.
      *
      * Inputs:
-     * * 0: A tensor, specifying the input.
+     * * 0: A tensor, specifying the input. Since API level 29, this tensor may
+     *      be zero-sized.
      *
      * Outputs:
      * * 0: The output tensor of same shape as input0.
@@ -1465,7 +1491,8 @@
      * Supported tensor rank: up to 4.
      *
      * Inputs:
-     * * 0: A tensor, specifying the input.
+     * * 0: A tensor, specifying the input. Since API level 29, this tensor may
+     *      be zero-sized.
      *
      * Outputs:
      * * 0: The output tensor of same shape as input0.
@@ -1489,7 +1516,8 @@
      * Supported tensor rank: up to 4.
      *
      * Inputs:
-     * * 0: A tensor, specifying the input.
+     * * 0: A tensor, specifying the input. Since API level 29, this tensor may
+     *      be zero-sized.
      *
      * Outputs:
      * * 0: The output tensor of same shape as input0.
@@ -1541,9 +1569,12 @@
      * [batch, height, width, channels]. Alternatively, the data layout could
      * be NCHW, the data storage order of: [batch, channels, height, width].
      *
-     * Inputs:
+     * Both resizing by shape and resizing by scale are supported.
+     *
+     * Inputs (resizing by shape):
      * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
-     *      the input.
+     *      the input. Since API level 29, zero batches is supported for this
+     *      tensor.
      * * 1: An {@link OperandType::INT32} scalar, specifying the output
      *      height of the output tensor.
      * * 2: An {@link OperandType::INT32} scalar, specifying the output
@@ -1552,6 +1583,24 @@
      *      Set to true to specify NCHW data layout for input0 and output0.
      *      Available since API level 29.
      *
+     * Inputs (resizing by scale, since API level 29):
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
+     *      the input. Zero batches is supported for this tensor.
+     * * 1: A scalar, specifying height_scale, the scaling factor of the height
+     *      dimension from the input tensor to the output tensor. The output
+     *      height is calculated as new_height = floor(height * height_scale).
+     *      The scalar must be of {@link OperandType::FLOAT16} if input0 is
+     *      of {@link OperandType::TENSOR_FLOAT16} and of
+     *      {@link OperandType::FLOAT32} otherwise.
+     * * 2: A scalar, specifying width_scale, the scaling factor of the width
+     *      dimension from the input tensor to the output tensor. The output
+     *      width is calculated as new_width = floor(width * width_scale).
+     *      The scalar must be of {@link OperandType::FLOAT16} if input0 is
+     *      of {@link OperandType::TENSOR_FLOAT16} and of
+     *      {@link OperandType::FLOAT32} otherwise.
+     * * 3: An optional {@link OperandType::BOOL} scalar, default to false.
+     *      Set to true to specify NCHW data layout for input0 and output0.
+     *
      * Outputs:
      * * 0: The output 4-D tensor, of shape
      *      [batches, new_height, new_width, depth].
@@ -1637,7 +1686,8 @@
      * Tensors with rank other than 2 or 4 are only supported since API level 29.
      *
      * Inputs:
-     * * 0: A 2-D or 4-D tensor, specifying the tensor to be reshaped.
+     * * 0: A 2-D or 4-D tensor, specifying the tensor to be reshaped. Since
+     *      API level 29, this tensor may be zero-sized.
      * * 1: A scalar, specifying the positive scaling factor for the exponent,
      *      beta. If input0 is of {@link OperandType::TENSOR_FLOAT32} or
      *      {@link OperandType::TENSOR_QUANT8_ASYMM}, the scalar must be of
@@ -1795,7 +1845,8 @@
      * Supported tensor rank: up to 4.
      *
      * Inputs:
-     * * 0: A tensor, specifying the input.
+     * * 0: A tensor, specifying the input. Since API level 29, this tensor may
+     *      be zero-sized.
      *
      * Outputs:
      * * 0: The output tensor of same shape as input0.
@@ -1862,6 +1913,10 @@
      *     input2.dimension = {5, 4, 3, 1}
      *     output.dimension = {5, 4, 3, 2}
      *
+     * Since API level 29, generic zero-sized input tensor is supported. Zero
+     * dimension is only compatible with 0 or 1. The size of the output
+     * dimension is zero if either of corresponding input dimension is zero.
+     *
      * Supported tensor {@link OperandType}:
      * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
      * * {@link OperandType::TENSOR_FLOAT32}
@@ -2095,6 +2150,10 @@
      *     input2.dimension = {5, 4, 3, 1}
      *     output.dimension = {5, 4, 3, 2}
      *
+     * Since API level 29, generic zero-sized input tensor is supported. Zero
+     * dimension is only compatible with 0 or 1. The size of the output
+     * dimension is zero if either of corresponding input dimension is zero.
+     *
      * Supported tensor {@link OperandType}:
      * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
      * * {@link OperandType::TENSOR_FLOAT32}
@@ -2135,6 +2194,7 @@
      *
      * Inputs:
      * * 0: An n-D tensor, specifying the tensor to be transposed.
+     *      Since API level 29, this tensor may be zero-sized.
      * * 1: An optional 1-D Tensor of {@link OperandType::TENSOR_INT32},
      *      the permutation of the dimensions of the input tensor.
      *
@@ -2231,7 +2291,8 @@
      * * 0: A 2-D Tensor of shape [num_rois, 4], specifying the locations of the
      *      bounding box proposals, each line with format [x1, y1, x2, y2].
      *      For tensor of type {@link OperandType::TENSOR_QUANT16_ASYMM},
-     *      the zeroPoint must be 0 and the scale must be 0.125.
+     *      the zeroPoint must be 0 and the scale must be 0.125. Zero num_rois
+     *      is supported for this tensor.
      * * 1: A 2-D Tensor of shape [num_rois, num_classes * 4], specifying the
      *      bounding box delta for each region of interest and each class. The
      *      bounding box deltas are organized in the following order
@@ -2240,10 +2301,12 @@
      *      and height, dw and dh is the log-scale relative correction factor
      *      for the width and height. For input0 of type
      *      {@link OperandType::TENSOR_QUANT16_ASYMM}, this tensor should be
-     *      of {@link OperandType::TENSOR_QUANT8_ASYMM}.
+     *      of {@link OperandType::TENSOR_QUANT8_ASYMM}. Zero num_rois is
+     *      supported for this tensor.
      * * 2: An 1-D {@link OperandType::TENSOR_INT32} tensor, of shape
      *      [num_rois], specifying the batch index of each box. Boxes with
-     *      the same batch index are grouped together.
+     *      the same batch index are grouped together. Zero num_rois is
+     *      supported for this tensor.
      * * 3: A 2-D Tensor of shape [batches, 2], specifying the information of
      *      each image in the batch, each line with format
      *      [image_height, image_width].
@@ -2272,113 +2335,113 @@
      * Inputs:
      * * 0: The input.
      *      A 3-D tensor of shape:
-     *        If time-major: [max_time, batch_size, output_size]
-     *        If batch-major: [batch_size, max_time, output_size]
+     *        If time-major: [max_time, batch_size, input_size]
+     *        If batch-major: [batch_size, max_time, input_size]
      *      where "max_time" is the number of timesteps (sequence length),
      *      "batch_size" corresponds to the batching dimension, and
      *      "input_size" is the size of the input.
      * * 1: The forward input-to-input weights. Optional.
-     *      A 2-D tensor of shape [num_units, input_size], where “num_units”
-     *      corresponds to the number of cell units.
+     *      A 2-D tensor of shape [fw_num_units, input_size], where “fw_num_units”
+     *      corresponds to the number of forward cell units.
      * * 2: The forward input-to-forget weights.
-     *      A 2-D tensor of shape [num_units, input_size].
+     *      A 2-D tensor of shape [fw_num_units, input_size].
      * * 3: The forward input-to-cell weights.
-     *      A 2-D tensor of shape [num_units, input_size].
+     *      A 2-D tensor of shape [fw_num_units, input_size].
      * * 4: The forward input-to-output weights.
-     *      A 2-D tensor of shape [num_units, input_size].
+     *      A 2-D tensor of shape [fw_num_units, input_size].
      * * 5: The forward recurrent-to-input weights. Optional.
-     *      A 2-D tensor of shape [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.
+     *      A 2-D tensor of shape [fw_num_units, fw_output_size], where “fw_output_size”
+     *      corresponds to either the number of cell units (i.e., fw_num_units),
+     *      or the second dimension of the “fw_projection_weights”, if defined.
      * * 6: The forward recurrent-to-forget weights.
-     *      A 2-D tensor of shape [num_units, output_size].
+     *      A 2-D tensor of shape [fw_num_units, fw_output_size].
      * * 7: The forward recurrent-to-cell weights.
-     *      A 2-D tensor of shape [num_units, output_size].
+     *      A 2-D tensor of shape [fw_num_units, fw_output_size].
      * * 8: The forward recurrent-to-output weights.
-     *      A 2-D tensor of shape [num_units, output_size].
+     *      A 2-D tensor of shape [fw_num_units, fw_output_size].
      * * 9: The forward cell-to-input weights. Optional.
-     *      A 1-D tensor of shape [num_units].
+     *      A 1-D tensor of shape [fw_num_units].
      * * 10: The forward cell-to-forget weights. Optional.
-     *       A 1-D tensor of shape [num_units].
+     *       A 1-D tensor of shape [fw_num_units].
      * * 11: The forward cell-to-output weights. Optional.
-     *       A 1-D tensor of shape [num_units].
+     *       A 1-D tensor of shape [fw_num_units].
      * * 12: The forward input gate bias. Optional.
-     *       A 1-D tensor of shape [num_units].
+     *       A 1-D tensor of shape [fw_num_units].
      * * 13: The forward forget gate bias.
-     *       A 1-D tensor of shape [num_units].
+     *       A 1-D tensor of shape [fw_num_units].
      * * 14: The forward cell gate bias.
-     *       A 1-D tensor of shape [num_units].
+     *       A 1-D tensor of shape [fw_num_units].
      * * 15: The forward output gate bias.
-     *       A 1-D tensor of shape [num_units].
+     *       A 1-D tensor of shape [fw_num_units].
      * * 16: The forward projection weights. Optional.
-     *       A 2-D tensor of shape [output_size, num_units].
+     *       A 2-D tensor of shape [fw_output_size, fw_num_units].
      * * 17: The forward projection bias. Optional.
-     *       A 1-D tensor of shape [output_size].
+     *       A 1-D tensor of shape [fw_output_size].
      * * 18: The backward input-to-input weights. Optional.
-     *       A 2-D tensor of shape [num_units, input_size], where “num_units”
-     *       corresponds to the number of cell units.
+     *       A 2-D tensor of shape [bw_num_units, input_size], where “bw_num_units”
+     *       corresponds to the number of backward cell units.
      * * 19: The backward input-to-forget weights.
-     *       A 2-D tensor of shape [num_units, input_size].
+     *       A 2-D tensor of shape [bw_num_units, input_size].
      * * 20: The backward input-to-cell weights.
-     *       A 2-D tensor of shape [num_units, input_size].
+     *       A 2-D tensor of shape [bw_num_units, input_size].
      * * 21: The backward input-to-output weights.
-     *       A 2-D tensor of shape [num_units, input_size].
+     *       A 2-D tensor of shape [bw_num_units, input_size].
      * * 22: The backward recurrent-to-input weights. Optional.
-     *       A 2-D tensor of shape [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.
+     *       A 2-D tensor of shape [bw_num_units, bw_output_size], where “bw_output_size”
+     *       corresponds to either the number of cell units (i.e., “bw_num_units”),
+     *       or the second dimension of the “bw_projection_weights”, if defined.
      * * 23: The backward recurrent-to-forget weights.
-     *       A 2-D tensor of shape [num_units, output_size].
+     *       A 2-D tensor of shape [bw_num_units, bw_output_size].
      * * 24: The backward recurrent-to-cell weights.
-     *       A 2-D tensor of shape [num_units, output_size].
+     *       A 2-D tensor of shape [bw_num_units, bw_output_size].
      * * 25: The backward recurrent-to-output weights.
-     *       A 2-D tensor of shape [num_units, output_size].
+     *       A 2-D tensor of shape [bw_num_units, bw_output_size].
      * * 26: The backward cell-to-input weights. Optional.
-     *       A 1-D tensor of shape [num_units].
+     *       A 1-D tensor of shape [bw_num_units].
      * * 27: The backward cell-to-forget weights. Optional.
-     *       A 1-D tensor of shape [num_units].
+     *       A 1-D tensor of shape [bw_num_units].
      * * 28: The backward cell-to-output weights. Optional.
-     *       A 1-D tensor of shape [num_units].
+     *       A 1-D tensor of shape [bw_num_units].
      * * 29: The backward input gate bias. Optional.
-     *       A 1-D tensor of shape [num_units].
+     *       A 1-D tensor of shape [bw_num_units].
      * * 30: The backward forget gate bias.
-     *       A 1-D tensor of shape [num_units].
+     *       A 1-D tensor of shape [bw_num_units].
      * * 31: The backward cell gate bias.
-     *       A 1-D tensor of shape [num_units].
+     *       A 1-D tensor of shape [bw_num_units].
      * * 32: The backward output gate bias.
-     *       A 1-D tensor of shape [num_units].
+     *       A 1-D tensor of shape [bw_num_units].
      * * 33: The backward projection weights. Optional.
-     *       A 2-D tensor of shape [output_size, num_units].
+     *       A 2-D tensor of shape [bw_output_size, bw_num_units].
      * * 34: The backward projection bias. Optional.
-     *       A 1-D tensor of shape [output_size].
+     *       A 1-D tensor of shape [bw_output_size].
      * * 35: The forward input activation state.
-     *       A 2-D tensor of shape [batch_size, output_size].
+     *       A 2-D tensor of shape [batch_size, bw_output_size].
      * * 36: The forward input cell state.
-     *       A 2-D tensor of shape [batch_size, num_units].
+     *       A 2-D tensor of shape [batch_size, bw_num_units].
      * * 37: The backward input activation state.
-     *       A 2-D tensor of shape [batch_size, output_size].
+     *       A 2-D tensor of shape [batch_size, bw_output_size].
      * * 38: The backward input cell state.
-     *       A 2-D tensor of shape [batch_size, num_units].
+     *       A 2-D tensor of shape [batch_size, bw_num_units].
      * * 39: The auxiliary input. Optional.
      *       A 3-D tensor of shape [max_time, batch_size, input_size], where “batch_size”
      *       corresponds to the batching dimension, and “input_size” is the size
      *       of the input.
      * * 40: The forward auxiliary input-to-input weights. Optional.
-     *       A 2-D tensor of shape [num_units, input_size].
+     *       A 2-D tensor of shape [fw_num_units, input_size].
      * * 41: The forward auxiliary input-to-forget weights. Optional.
-     *       A 2-D tensor of shape [num_units, input_size].
+     *       A 2-D tensor of shape [fw_num_units, input_size].
      * * 42: The forward auxiliary input-to-cell weights. Optional.
-     *       A 2-D tensor of shape [num_units, input_size].
+     *       A 2-D tensor of shape [fw_num_units, input_size].
      * * 43: The forward auxiliary input-to-output weights. Optional.
-     *       A 2-D tensor of shape [num_units, input_size].
+     *       A 2-D tensor of shape [fw_num_units, input_size].
      * * 44: The backward auxiliary input-to-input weights. Optional.
-     *       A 2-D tensor of shape [num_units, input_size].
+     *       A 2-D tensor of shape [bw_num_units, input_size].
      * * 45: The backward auxiliary input-to-forget weights. Optional.
-     *       A 2-D tensor of shape [num_units, input_size].
+     *       A 2-D tensor of shape [bw_num_units, input_size].
      * * 46: The backward auxiliary input-to-cell weights. Optional.
-     *       A 2-D tensor of shape [num_units, input_size].
+     *       A 2-D tensor of shape [bw_num_units, input_size].
      * * 47: The backward auxiliary input-to-output weights. Optional.
-     *       A 2-D tensor of shape [num_units, input_size].
+     *       A 2-D tensor of shape [bw_num_units, input_size].
      * * 48: The activation function.
      *       A value indicating the activation function:
      *       <ul>
@@ -2410,16 +2473,46 @@
      * * 52: time_major
      *       An {@link OperandType::BOOL} scalar specifying the shape format
      *       of input and output tensors.
+     * * 53: The forward input layer normalization weights. Optional.
+     *       A 1-D tensor of shape [fw_num_units]. Used to rescale normalized inputs
+     *       to activation at input gate.
+     * * 54: The forward forget layer normalization weights. Optional.
+     *       A 1-D tensor of shape [fw_num_units]. Used to rescale normalized inputs
+     *       to activation at forget gate.
+     * * 55: The forward cell layer normalization weights. Optional.
+     *       A 1-D tensor of shape [fw_num_units]. Used to rescale normalized inputs
+     *       to activation at cell gate.
+     * * 56: The forward output layer normalization weights. Optional.
+     *       A 1-D tensor of shape [fw_num_units]. Used to rescale normalized inputs
+     *       to activation at output gate.
+     * * 57: The backward input layer normalization weights. Optional.
+     *       A 1-D tensor of shape [bw_num_units]. Used to rescale normalized inputs
+     *       to activation at input gate.
+     * * 58: The backward forget layer normalization weights. Optional.
+     *       A 1-D tensor of shape [bw_num_units]. Used to rescale normalized inputs
+     *       to activation at forget gate.
+     * * 59: The backward cell layer normalization weights. Optional.
+     *       A 1-D tensor of shape [bw_num_units]. Used to rescale normalized inputs
+     *       to activation at cell gate.
+     * * 60: The backward output layer normalization weights. Optional.
+     *       A 1-D tensor of shape [bw_num_units]. Used to rescale normalized inputs
+     *       to activation at output gate.
      *
      * Outputs:
      * * 0: The forward output.
      *      A 3-D tensor of shape:
-     *        If time-major: [max_time, batch_size, output_size]
-     *        If batch-major: [batch_size, max_time, output_size]
+     *        If time-major and not merge_outputs:
+     *          [max_time, batch_size, fw_output_size]
+     *        If time-major and merge_outputs:
+     *          [max_time, batch_size, fw_output_size + bw_output_size]
+     *        If batch-major and not merge_outputs:
+     *          [batch_size, max_time, fw_output_size]
+     *        If batch-major and merge_outputs:
+     *          [batch_size, max_time, fw_output_size + bw_output_size]
      * * 1: The backward output.  Unused if merge_outputs is true.
      *      A 3-D tensor of shape:
-     *        If time-major: [max_time, batch_size, output_size]
-     *        If batch-major: [batch_size, max_time, output_size]
+     *        If time-major: [max_time, batch_size, bw_output_size]
+     *        If batch-major: [batch_size, max_time, bw_output_size]
      *
      * Available since API level 29.
      */
@@ -2547,10 +2640,17 @@
     /**
      * Greedily selects a subset of bounding boxes in descending order of score.
      *
-     * This op applies hard NMS algorithm to each class. In each loop of
-     * execution, the box with maximum score gets selected, and any boxes with
-     * the intersection-over-union (IOU) greater than a threshold are removed
-     * from the pending set.
+     * This op applies NMS algorithm to each class. In each loop of execution,
+     * the box with maximum score gets selected and removed from the pending set.
+     * The scores of the rest of boxes are lowered according to the
+     * intersection-over-union (IOU) overlapping with the previously selected
+     * boxes and a specified NMS kernel method. Any boxes with score less
+     * than a threshold are removed from the pending set.
+     *
+     * Three NMS kernels are supported:
+     * * Hard:     score_new = score_old * (1 if IoU < threshold else 0)
+     * * Linear:   score_new = score_old * (1 if IoU < threshold else 1 - IoU)
+     * * Gaussian: score_new = score_old * exp(- IoU^2 / sigma)
      *
      * Axis-aligned bounding boxes are represented by its upper-left corner
      * coordinate (x1,y1) and lower-right corner coordinate (x2,y2). A valid
@@ -2564,25 +2664,34 @@
      * Inputs:
      * * 0: A 2-D Tensor of shape [num_rois, num_classes], specifying the score
      *      of each bounding box proposal. The boxes are grouped by batches in the
-     *      first dimension.
+     *      first dimension. Zero num_rois is supported for this tensor.
      * * 1: A 2-D Tensor specifying the bounding boxes of shape
      *      [num_rois, num_classes * 4], organized in the order [x1, y1, x2, y2].
      *      The boxes are grouped by batches in the first dimension. The sequential
      *      order of the boxes corresponds with input0. For input0 of type
      *      {@link OperandType::TENSOR_QUANT8_ASYMM}, this tensor should be of
      *      {@link OperandType::TENSOR_QUANT16_ASYMM}, with zeroPoint of 0 and
-     *      scale of 0.125.
+     *      scale of 0.125. Zero num_rois is supported for this tensor.
      * * 2: A 1-D {@link OperandType::TENSOR_INT32} tensor, of shape
      *      [num_rois], specifying the batch index of each box. Boxes with
      *      the same batch index are grouped together.
      * * 3: An {@link OperandType::FLOAT32} scalar, score_threshold. Boxes
      *      with scores lower than the threshold are filtered before sending
      *      to the NMS algorithm.
-     * * 4: An {@link OperandType::FLOAT32} scalar, specifying the IoU
-     *      threshold.
-     * * 5: An {@link OperandType::INT32} scalar, specifying the maximum
+     * * 4: An {@link OperandType::INT32} scalar, specifying the maximum
      *      number of selected bounding boxes for each image. Set to a negative
      *      value for unlimited number of output bounding boxes.
+     * * 5: An {@link OperandType::INT32} scalar, specifying the NMS
+     *      kernel method, options are 0:hard, 1:linear, 2:gaussian.
+     * * 6: An {@link OperandType::FLOAT32} scalar, specifying the IoU
+     *      threshold in hard and linear NMS kernel. This field is ignored if
+     *      gaussian kernel is selected.
+     * * 7: An {@link OperandType::FLOAT32} scalar, specifying the sigma in
+     *      gaussian NMS kernel. This field is ignored if gaussian kernel is
+     *      not selected.
+     * * 8: An {@link OperandType::FLOAT32} scalar, nms_score_threshold.
+     *      Boxes with scores lower than the threshold are dropped during the
+     *      score updating phase in soft NMS.
      *
      * Outputs:
      * * 0: A 1-D Tensor of the same {@link OperandType} as input0, with shape
@@ -2600,8 +2709,8 @@
      *      [num_output_rois], specifying the class of each output box. The
      *      sequential order of the boxes corresponds with output0.
      * * 3: A 1-D {@link OperandType::TENSOR_INT32} tensor, of shape
-     *      [num_rois], specifying the batch index of each box. Boxes with
-     *      the same batch index are grouped together.
+     *      [num_output_rois], specifying the batch index of each box. Boxes
+     *      with the same batch index are grouped together.
      *
      * Available since API level 29.
      */
@@ -2937,8 +3046,8 @@
      *      For type of {@link OperandType::TENSOR_QUANT16_ASYMM}, the
      *      scale must be 0.125 and the zero point must be 0.
      * * 2: A 1-D {@link OperandType::TENSOR_INT32} tensor, of shape
-     *      [num_rois], specifying the batch index of each box. Boxes with
-     *      the same batch index are grouped together.
+     *      [num_output_rois], specifying the batch index of each box. Boxes
+     *      with the same batch index are grouped together.
      *
      * Available since API level 29.
      */
@@ -3122,11 +3231,7 @@
      *
      * Outputs:
      * * 0: The output 4-D tensor, of shape
-     *      [batches, out_height, out_width, depth_out]. For output tensor of
-     *      {@link OperandType::TENSOR_QUANT8_ASYMM}, the following condition
-     *      must be satisfied: output_scale > input_scale * filter_scale (for
-     *      filter tensor of type {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}
-     *      this condition must be true for all filter scales).
+     *      [batches, out_height, out_width, depth_out].
      *
      * Available since API level 29.
      */
@@ -3608,7 +3713,7 @@
      * Supported tensor rank: from 1
      *
      * Inputs:
-     * * 0: A tensor.
+     * * 0: A tensor, may be zero-sized.
      *
      * Outputs:
      * * 0: The output tensor of same shape as input0, but with
@@ -3940,10 +4045,12 @@
      *      the regions of interest, each line with format [x1, y1, x2, y2].
      *      For input0 of type {@link OperandType::TENSOR_QUANT8_ASYMM},
      *      this tensor should be of {@link OperandType::TENSOR_QUANT16_ASYMM},
-     *      with zeroPoint of 0 and scale of 0.125.
+     *      with zeroPoint of 0 and scale of 0.125. Zero num_rois is
+     *      supported for this tensor.
      * * 2: An 1-D {@link OperandType::TENSOR_INT32} tensor, of shape
      *      [num_rois], specifying the batch index of each box. Boxes with
-     *      the same batch index are grouped together.
+     *      the same batch index are grouped together. Zero num_rois is
+     *      supported for this tensor.
      * * 3: An {@link OperandType::INT32} scalar, specifying the output
      *      height of the output tensor.
      * * 4: An {@link OperandType::INT32} scalar, specifying the output
@@ -4108,7 +4215,7 @@
      * Supported tensor rank: from 1
      *
      * Inputs:
-     * * 0: An n-D tensor to take slice from.
+     * * 0: An n-D tensor to take slice from, may be zero-sized.
      * * 1: A 1-D tensor of type {@link OperandType::TENSOR_INT32} specifying
      *      the beginning indices of the slice in each dimension.
      * * 2: A 1-D tensor of type {@link OperandType::TENSOR_INT32} specifying
@@ -4331,11 +4438,7 @@
      *
      * Outputs:
      * * 0: The output 4-D tensor, of shape
-     *      [batches, out_height, out_width, depth_out]. For output tensor of
-     *      {@link OperandType::TENSOR_QUANT8_ASYMM}, the following condition
-     *      must be satisfied: output_scale > input_scale * filter_scale (for
-     *      filter tensor of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}
-     *      this condition must be true for all filter scales).
+     *      [batches, out_height, out_width, depth_out].
      *
      * Available since API level 29.
      */
@@ -4367,9 +4470,9 @@
      * Inputs:
      * * 0: The input (\f$x_t\f$).
      *      A 3-D tensor of shape:
-     *        If time-major: [max_time, batch_size, output_size]
-     *        If batch-major: [batch_size, max_time, output_size]
-     *      where “max_size” is the number of timesteps (sequence length),
+     *        If time-major: [max_time, batch_size, input_size]
+     *        If batch-major: [batch_size, max_time, input_size]
+     *      where “max_time” is the number of timesteps (sequence length),
      *      “batch_size” corresponds to the batching dimension, and
      *      “input_size” is the size of the input.
      * * 1: The input-to-input weights (\f$W_{xi}\f$). Optional.
@@ -4429,16 +4532,16 @@
      *      projection layer, such that values are bound within
      *      [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled.
      * * 23:Time-major if true, batch-major if false.
-     * * 24:The input layer normalization weights.
+     * * 24:The input layer normalization weights. Optional.
      *      A 1-D tensor of shape [num_units]. Used to rescale normalized inputs
      *      to activation at input gate.
-     * * 25:The forget layer normalization weights.
+     * * 25:The forget layer normalization weights. Optional.
      *      A 1-D tensor of shape [num_units]. Used to rescale normalized inputs
      *      to activation at forget gate.
-     * * 26:The cell layer normalization weights.
+     * * 26:The cell layer normalization weights. Optional.
      *      A 1-D tensor of shape [num_units]. Used to rescale normalized inputs
      *      to activation at cell gate.
-     * * 27:The output layer normalization weights.
+     * * 27:The output layer normalization weights. Optional.
      *      A 1-D tensor of shape [num_units]. Used to rescale normalized inputs
      *      to activation at output gate.
      *
@@ -4526,9 +4629,11 @@
      * [batch, height, width, channels]. Alternatively, the data layout could
      * be NCHW, the data storage order of: [batch, channels, height, width].
      *
-     * Inputs:
+     * Both resizing by shape and resizing by scale are supported.
+     *
+     * Inputs (resizing by shape):
      * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
-     *      the input.
+     *      the input. Zero batches is supported for this tensor.
      * * 1: An {@link OperandType::INT32} scalar, specifying the output
      *      height of the output tensor.
      * * 2: An {@link OperandType::INT32} scalar, specifying the output
@@ -4536,6 +4641,24 @@
      * * 3: An {@link OperandType::BOOL} scalar, default to false.
      *      Set to true to specify NCHW data layout for input0 and output0.
      *
+     * Inputs (resizing by scale):
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
+     *      the input. Zero batches is supported for this tensor.
+     * * 1: A scalar, specifying height_scale, the scaling factor of the height
+     *      dimension from the input tensor to the output tensor. The output
+     *      height is calculated as new_height = floor(height * height_scale).
+     *      The scalar must be of {@link OperandType::FLOAT16} if input0 is
+     *      of {@link OperandType::TENSOR_FLOAT16} and of
+     *      {@link OperandType::FLOAT32} otherwise.
+     * * 2: A scalar, specifying width_scale, the scaling factor of the width
+     *      dimension from the input tensor to the output tensor. The output
+     *      width is calculated as new_width = floor(width * width_scale).
+     *      The scalar must be of {@link OperandType::FLOAT16} if input0 is
+     *      of {@link OperandType::TENSOR_FLOAT16} and of
+     *      {@link OperandType::FLOAT32} otherwise.
+     * * 3: An {@link OperandType::BOOL} scalar, default to false.
+     *      Set to true to specify NCHW data layout for input0 and output0.
+     *
      * Outputs:
      * * 0: The output 4-D tensor, of shape
      *      [batches, new_height, new_width, depth].
@@ -4593,6 +4716,39 @@
 };
 
 /**
+ * The capabilities of a driver.
+ *
+ * Performance of an operation comes from the type of its first operand.
+ * This represents performance for non extension operand types.
+ */
+struct Capabilities {
+    /**
+     * Driver performance when operating on float32 data but performing
+     * calculations with range and/or precision as low as that of the IEEE
+     * 754 16-bit floating-point format.
+     */
+    PerformanceInfo relaxedFloat32toFloat16PerformanceScalar;
+    PerformanceInfo relaxedFloat32toFloat16PerformanceTensor;
+
+    /**
+     * Driver performance when operating on a particular data type.
+     * In the case of float32 data, this is used when the calculations
+     * are not relaxed.
+     */
+    struct OperandPerformance {
+        OperandType type;
+        PerformanceInfo info;
+    };
+
+    /**
+     * Performance by operand type. Must be sorted by OperandType.
+     * If a particular OperandType is not present in operandPerformance,
+     * its performance is treated as { .execTime = FLT_MAX, .powerUsage = FLT_MAX }.
+     */
+    vec<OperandPerformance> operandPerformance;
+};
+
+/**
  * Describes one operation of the model's graph.
  */
 struct Operation {
diff --git a/neuralnetworks/1.2/vts/functional/BasicTests.cpp b/neuralnetworks/1.2/vts/functional/BasicTests.cpp
index 365a750..5c269df 100644
--- a/neuralnetworks/1.2/vts/functional/BasicTests.cpp
+++ b/neuralnetworks/1.2/vts/functional/BasicTests.cpp
@@ -25,7 +25,7 @@
 namespace vts {
 namespace functional {
 
-using V1_1::Capabilities;
+using V1_0::PerformanceInfo;
 
 // create device test
 TEST_F(NeuralnetworksHidlTest, CreateDevice) {}
@@ -37,6 +37,31 @@
     EXPECT_EQ(DeviceStatus::AVAILABLE, static_cast<DeviceStatus>(status));
 }
 
+// initialization
+TEST_F(NeuralnetworksHidlTest, GetCapabilitiesTest) {
+    using OperandPerformance = Capabilities::OperandPerformance;
+    Return<void> ret = device->getCapabilities_1_2([](ErrorStatus status,
+                                                      const Capabilities& capabilities) {
+        EXPECT_EQ(ErrorStatus::NONE, status);
+
+        auto isPositive = [](const PerformanceInfo& perf) {
+            return perf.execTime > 0.0f && perf.powerUsage > 0.0f;
+        };
+
+        EXPECT_TRUE(isPositive(capabilities.relaxedFloat32toFloat16PerformanceScalar));
+        EXPECT_TRUE(isPositive(capabilities.relaxedFloat32toFloat16PerformanceTensor));
+        const auto& opPerf = capabilities.operandPerformance;
+        EXPECT_TRUE(std::all_of(
+                opPerf.begin(), opPerf.end(),
+                [isPositive](const OperandPerformance& a) { return isPositive(a.info); }));
+        EXPECT_TRUE(std::is_sorted(opPerf.begin(), opPerf.end(),
+                                   [](const OperandPerformance& a, const OperandPerformance& b) {
+                                       return a.type < b.type;
+                                   }));
+    });
+    EXPECT_TRUE(ret.isOk());
+}
+
 // device version test
 TEST_F(NeuralnetworksHidlTest, GetDeviceVersionStringTest) {
     Return<void> ret = device->getVersionString([](ErrorStatus status, const hidl_string& version) {
@@ -77,10 +102,15 @@
     EXPECT_TRUE(ret.isOk());
 }
 
-// isCachingSupported test
-TEST_F(NeuralnetworksHidlTest, IsCachingSupported) {
-    Return<void> ret = device->isCachingSupported(
-            [](ErrorStatus status, bool) { EXPECT_EQ(ErrorStatus::NONE, status); });
+// getNumberOfCacheFilesNeeded test
+TEST_F(NeuralnetworksHidlTest, getNumberOfCacheFilesNeeded) {
+    Return<void> ret = device->getNumberOfCacheFilesNeeded(
+            [](ErrorStatus status, uint32_t numModelCache, uint32_t numDataCache) {
+                EXPECT_EQ(ErrorStatus::NONE, status);
+                EXPECT_LE(numModelCache,
+                          static_cast<uint32_t>(Constant::MAX_NUMBER_OF_CACHE_FILES));
+                EXPECT_LE(numDataCache, static_cast<uint32_t>(Constant::MAX_NUMBER_OF_CACHE_FILES));
+            });
     EXPECT_TRUE(ret.isOk());
 }
 }  // namespace functional
diff --git a/neuralnetworks/1.2/vts/functional/CompilationCachingTests.cpp b/neuralnetworks/1.2/vts/functional/CompilationCachingTests.cpp
index 00989e5..167fc09 100644
--- a/neuralnetworks/1.2/vts/functional/CompilationCachingTests.cpp
+++ b/neuralnetworks/1.2/vts/functional/CompilationCachingTests.cpp
@@ -54,29 +54,39 @@
 [[maybe_unused]] auto dummy_createTestModel = createTestModel_dynamic_output_shape;
 [[maybe_unused]] auto dummy_get_examples = get_examples_dynamic_output_shape;
 
-enum class AccessMode { READ_ONLY, WRITE_ONLY };
+enum class AccessMode { READ_WRITE, READ_ONLY, WRITE_ONLY };
 
-void createCacheHandle(const std::vector<std::string>& files, AccessMode mode,
-                       hidl_handle* handle) {
-    std::vector<int> fds;
-    for (const auto& file : files) {
-        int fd;
-        if (mode == AccessMode::READ_ONLY) {
-            fd = open(file.c_str(), O_RDONLY);
-        } else if (mode == AccessMode::WRITE_ONLY) {
-            fd = open(file.c_str(), O_WRONLY | O_TRUNC | O_CREAT, S_IRUSR | S_IWUSR);
-        } else {
-            FAIL();
+// Creates cache handles based on provided file groups.
+// The outer vector corresponds to handles and the inner vector is for fds held by each handle.
+void createCacheHandles(const std::vector<std::vector<std::string>>& fileGroups,
+                        const std::vector<AccessMode>& mode, hidl_vec<hidl_handle>* handles) {
+    handles->resize(fileGroups.size());
+    for (uint32_t i = 0; i < fileGroups.size(); i++) {
+        std::vector<int> fds;
+        for (const auto& file : fileGroups[i]) {
+            int fd;
+            if (mode[i] == AccessMode::READ_ONLY) {
+                fd = open(file.c_str(), O_RDONLY);
+            } else if (mode[i] == AccessMode::WRITE_ONLY) {
+                fd = open(file.c_str(), O_WRONLY | O_CREAT, S_IRUSR | S_IWUSR);
+            } else if (mode[i] == AccessMode::READ_WRITE) {
+                fd = open(file.c_str(), O_RDWR | O_CREAT, S_IRUSR | S_IWUSR);
+            } else {
+                FAIL();
+            }
+            ASSERT_GE(fd, 0);
+            fds.push_back(fd);
         }
-        ASSERT_GE(fd, 0);
-        fds.push_back(fd);
+        native_handle_t* cacheNativeHandle = native_handle_create(fds.size(), 0);
+        ASSERT_NE(cacheNativeHandle, nullptr);
+        std::copy(fds.begin(), fds.end(), &cacheNativeHandle->data[0]);
+        (*handles)[i].setTo(cacheNativeHandle, /*shouldOwn=*/true);
     }
-    native_handle_t* cacheNativeHandle = native_handle_create(fds.size(), 0);
-    ASSERT_NE(cacheNativeHandle, nullptr);
-    for (uint32_t i = 0; i < fds.size(); i++) {
-        cacheNativeHandle->data[i] = fds[i];
-    }
-    handle->setTo(cacheNativeHandle, /*shouldOwn=*/true);
+}
+
+void createCacheHandles(const std::vector<std::vector<std::string>>& fileGroups, AccessMode mode,
+                        hidl_vec<hidl_handle>* handles) {
+    createCacheHandles(fileGroups, std::vector<AccessMode>(fileGroups.size(), mode), handles);
 }
 
 }  // namespace
@@ -88,38 +98,43 @@
         NeuralnetworksHidlTest::SetUp();
         ASSERT_NE(device.get(), nullptr);
 
-        // Create cache directory. The cache directory and cache files are always created to test
-        // the behavior of prepareModelFromCache, even when caching is not supported.
+        // Create cache directory. The cache directory and a temporary cache file is always created
+        // to test the behavior of prepareModelFromCache, even when caching is not supported.
         char cacheDirTemp[] = "/data/local/tmp/TestCompilationCachingXXXXXX";
         char* cacheDir = mkdtemp(cacheDirTemp);
         ASSERT_NE(cacheDir, nullptr);
         mCacheDir = cacheDir;
+        mCacheDir.push_back('/');
 
-        // Create empty cache files.
-        mCache1 = mCacheDir + "/cache1";
-        mCache2 = mCacheDir + "/cache2";
-        mCache3 = mCacheDir + "/cache3";
-        // A dummy handle, use AccessMode::WRITE_ONLY for createCacheHandle to create files.
-        hidl_handle handle;
-        createCacheHandle({mCache1, mCache2, mCache3}, AccessMode::WRITE_ONLY, &handle);
-
-        // Check if caching is supported.
-        bool isCachingSupported;
-        Return<void> ret = device->isCachingSupported(
-                [&isCachingSupported](ErrorStatus status, bool supported) {
+        Return<void> ret = device->getNumberOfCacheFilesNeeded(
+                [this](ErrorStatus status, uint32_t numModelCache, uint32_t numDataCache) {
                     EXPECT_EQ(ErrorStatus::NONE, status);
-                    isCachingSupported = supported;
+                    mNumModelCache = numModelCache;
+                    mNumDataCache = numDataCache;
                 });
         EXPECT_TRUE(ret.isOk());
-        if (isCachingSupported) {
-            mIsCachingSupported = true;
-        } else {
+        mIsCachingSupported = mNumModelCache > 0 || mNumDataCache > 0;
+
+        // Create empty cache files.
+        mTmpCache = mCacheDir + "tmp";
+        for (uint32_t i = 0; i < mNumModelCache; i++) {
+            mModelCache.push_back({mCacheDir + "model" + std::to_string(i)});
+        }
+        for (uint32_t i = 0; i < mNumDataCache; i++) {
+            mDataCache.push_back({mCacheDir + "data" + std::to_string(i)});
+        }
+        // Dummy handles, use AccessMode::WRITE_ONLY for createCacheHandles to create files.
+        hidl_vec<hidl_handle> modelHandle, dataHandle, tmpHandle;
+        createCacheHandles(mModelCache, AccessMode::WRITE_ONLY, &modelHandle);
+        createCacheHandles(mDataCache, AccessMode::WRITE_ONLY, &dataHandle);
+        createCacheHandles({{mTmpCache}}, AccessMode::WRITE_ONLY, &tmpHandle);
+
+        if (!mIsCachingSupported) {
             LOG(INFO) << "NN VTS: Early termination of test because vendor service does not "
                          "support compilation caching.";
             std::cout << "[          ]   Early termination of test because vendor service does not "
                          "support compilation caching."
                       << std::endl;
-            mIsCachingSupported = false;
         }
     }
 
@@ -127,22 +142,49 @@
         // The tmp directory is only removed when the driver reports caching not supported,
         // otherwise it is kept for debugging purpose.
         if (!mIsCachingSupported) {
-            remove(mCache1.c_str());
-            remove(mCache2.c_str());
-            remove(mCache3.c_str());
+            remove(mTmpCache.c_str());
             rmdir(mCacheDir.c_str());
         }
         NeuralnetworksHidlTest::TearDown();
     }
 
-    void saveModelToCache(sp<IPreparedModel> preparedModel, const hidl_handle& cache1,
-                          const hidl_handle& cache2, ErrorStatus* status) {
-        // Save IPreparedModel to cache.
+    void saveModelToCache(const V1_2::Model& model, const hidl_vec<hidl_handle>& modelCache,
+                          const hidl_vec<hidl_handle>& dataCache, bool* supported,
+                          sp<IPreparedModel>* preparedModel = nullptr) {
+        if (preparedModel != nullptr) *preparedModel = nullptr;
+
+        // See if service can handle model.
+        bool fullySupportsModel = false;
+        Return<void> supportedCall = device->getSupportedOperations_1_2(
+                model,
+                [&fullySupportsModel, &model](ErrorStatus status, const hidl_vec<bool>& supported) {
+                    ASSERT_EQ(ErrorStatus::NONE, status);
+                    ASSERT_EQ(supported.size(), model.operations.size());
+                    fullySupportsModel = std::all_of(supported.begin(), supported.end(),
+                                                     [](bool valid) { return valid; });
+                });
+        ASSERT_TRUE(supportedCall.isOk());
+        *supported = fullySupportsModel;
+        if (!fullySupportsModel) return;
+
+        // Launch prepare model.
+        sp<PreparedModelCallback> preparedModelCallback = new PreparedModelCallback();
+        ASSERT_NE(nullptr, preparedModelCallback.get());
         hidl_array<uint8_t, sizeof(mToken)> cacheToken(mToken);
-        Return<ErrorStatus> saveToCacheStatus =
-                preparedModel->saveToCache(cache1, cache2, cacheToken);
-        ASSERT_TRUE(saveToCacheStatus.isOk());
-        *status = static_cast<ErrorStatus>(saveToCacheStatus);
+        Return<ErrorStatus> prepareLaunchStatus =
+                device->prepareModel_1_2(model, ExecutionPreference::FAST_SINGLE_ANSWER, modelCache,
+                                         dataCache, cacheToken, preparedModelCallback);
+        ASSERT_TRUE(prepareLaunchStatus.isOk());
+        ASSERT_EQ(static_cast<ErrorStatus>(prepareLaunchStatus), ErrorStatus::NONE);
+
+        // Retrieve prepared model.
+        preparedModelCallback->wait();
+        ASSERT_EQ(preparedModelCallback->getStatus(), ErrorStatus::NONE);
+        if (preparedModel != nullptr) {
+            *preparedModel =
+                    V1_2::IPreparedModel::castFrom(preparedModelCallback->getPreparedModel())
+                            .withDefault(nullptr);
+        }
     }
 
     bool checkEarlyTermination(ErrorStatus status) {
@@ -157,14 +199,27 @@
         return false;
     }
 
-    void prepareModelFromCache(const hidl_handle& cache1, const hidl_handle& cache2,
+    bool checkEarlyTermination(bool supported) {
+        if (!supported) {
+            LOG(INFO) << "NN VTS: Early termination of test because vendor service cannot "
+                         "prepare model that it does not support.";
+            std::cout << "[          ]   Early termination of test because vendor service cannot "
+                         "prepare model that it does not support."
+                      << std::endl;
+            return true;
+        }
+        return false;
+    }
+
+    void prepareModelFromCache(const hidl_vec<hidl_handle>& modelCache,
+                               const hidl_vec<hidl_handle>& dataCache,
                                sp<IPreparedModel>* preparedModel, ErrorStatus* status) {
         // Launch prepare model from cache.
         sp<PreparedModelCallback> preparedModelCallback = new PreparedModelCallback();
         ASSERT_NE(nullptr, preparedModelCallback.get());
         hidl_array<uint8_t, sizeof(mToken)> cacheToken(mToken);
-        Return<ErrorStatus> prepareLaunchStatus =
-                device->prepareModelFromCache(cache1, cache2, cacheToken, preparedModelCallback);
+        Return<ErrorStatus> prepareLaunchStatus = device->prepareModelFromCache(
+                modelCache, dataCache, cacheToken, preparedModelCallback);
         ASSERT_TRUE(prepareLaunchStatus.isOk());
         if (static_cast<ErrorStatus>(prepareLaunchStatus) != ErrorStatus::NONE) {
             *preparedModel = nullptr;
@@ -179,49 +234,54 @@
                                  .withDefault(nullptr);
     }
 
+    // Absolute path to the temporary cache directory.
     std::string mCacheDir;
-    std::string mCache1;
-    std::string mCache2;
-    std::string mCache3;
+
+    // Groups of file paths for model and data cache in the tmp cache directory, initialized with
+    // outer_size = mNum{Model|Data}Cache, inner_size = 1. The outer vector corresponds to handles
+    // and the inner vector is for fds held by each handle.
+    std::vector<std::vector<std::string>> mModelCache;
+    std::vector<std::vector<std::string>> mDataCache;
+
+    // A separate temporary file path in the tmp cache directory.
+    std::string mTmpCache;
+
     uint8_t mToken[static_cast<uint32_t>(Constant::BYTE_SIZE_OF_CACHE_TOKEN)] = {};
-    bool mIsCachingSupported;
+    uint32_t mNumModelCache;
+    uint32_t mNumDataCache;
+    uint32_t mIsCachingSupported;
 };
 
 TEST_F(CompilationCachingTest, CacheSavingAndRetrieval) {
     // Create test HIDL model and compile.
     Model testModel = createTestModel();
     sp<IPreparedModel> preparedModel = nullptr;
-    generated_tests::PrepareModel(device, testModel, &preparedModel);
-    // Terminate early if the driver cannot prepare the model.
-    if (preparedModel == nullptr) return;
 
     // Save the compilation to cache.
     {
-        ErrorStatus status;
-        hidl_handle cache1, cache2;
-        createCacheHandle({mCache1}, AccessMode::WRITE_ONLY, &cache1);
-        createCacheHandle({mCache2}, AccessMode::WRITE_ONLY, &cache2);
-        saveModelToCache(preparedModel, cache1, cache2, &status);
-        if (!mIsCachingSupported) {
-            EXPECT_EQ(status, ErrorStatus::GENERAL_FAILURE);
-        } else {
-            if (checkEarlyTermination(status)) return;
-            ASSERT_EQ(status, ErrorStatus::NONE);
-        }
+        bool supported;
+        hidl_vec<hidl_handle> modelCache, dataCache;
+        createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+        createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+        saveModelToCache(testModel, modelCache, dataCache, &supported);
+        if (checkEarlyTermination(supported)) return;
     }
 
     // Retrieve preparedModel from cache.
     {
         preparedModel = nullptr;
         ErrorStatus status;
-        hidl_handle cache1, cache2;
-        createCacheHandle({mCache1}, AccessMode::READ_ONLY, &cache1);
-        createCacheHandle({mCache2}, AccessMode::READ_ONLY, &cache2);
-        prepareModelFromCache(cache1, cache2, &preparedModel, &status);
+        hidl_vec<hidl_handle> modelCache, dataCache;
+        createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+        createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+        prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
         if (!mIsCachingSupported) {
             ASSERT_EQ(status, ErrorStatus::GENERAL_FAILURE);
             ASSERT_EQ(preparedModel, nullptr);
             return;
+        } else if (checkEarlyTermination(status)) {
+            ASSERT_EQ(preparedModel, nullptr);
+            return;
         } else {
             ASSERT_EQ(status, ErrorStatus::NONE);
             ASSERT_NE(preparedModel, nullptr);
@@ -238,41 +298,54 @@
     // Create test HIDL model and compile.
     Model testModel = createTestModel();
     sp<IPreparedModel> preparedModel = nullptr;
-    generated_tests::PrepareModel(device, testModel, &preparedModel);
-    // Terminate early if the driver cannot prepare the model.
-    if (preparedModel == nullptr) return;
 
     // Save the compilation to cache.
     {
-        ErrorStatus status;
-        hidl_handle cache1, cache2;
-        createCacheHandle({mCache1}, AccessMode::WRITE_ONLY, &cache1);
-        createCacheHandle({mCache2}, AccessMode::WRITE_ONLY, &cache2);
-        saveModelToCache(preparedModel, cache1, cache2, &status);
-        if (!mIsCachingSupported) {
-            EXPECT_EQ(status, ErrorStatus::GENERAL_FAILURE);
-        } else {
-            if (checkEarlyTermination(status)) return;
-            ASSERT_EQ(status, ErrorStatus::NONE);
+        bool supported;
+        hidl_vec<hidl_handle> modelCache, dataCache;
+        createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+        createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+        uint8_t dummyBytes[] = {0, 0};
+        // Write a dummy integer to the cache.
+        // The driver should be able to handle non-empty cache and non-zero fd offset.
+        for (uint32_t i = 0; i < modelCache.size(); i++) {
+            ASSERT_EQ(write(modelCache[i].getNativeHandle()->data[0], &dummyBytes,
+                            sizeof(dummyBytes)),
+                      sizeof(dummyBytes));
         }
+        for (uint32_t i = 0; i < dataCache.size(); i++) {
+            ASSERT_EQ(
+                    write(dataCache[i].getNativeHandle()->data[0], &dummyBytes, sizeof(dummyBytes)),
+                    sizeof(dummyBytes));
+        }
+        saveModelToCache(testModel, modelCache, dataCache, &supported);
+        if (checkEarlyTermination(supported)) return;
     }
 
     // Retrieve preparedModel from cache.
     {
         preparedModel = nullptr;
         ErrorStatus status;
-        hidl_handle cache1, cache2;
-        createCacheHandle({mCache1}, AccessMode::READ_ONLY, &cache1);
-        createCacheHandle({mCache2}, AccessMode::READ_ONLY, &cache2);
+        hidl_vec<hidl_handle> modelCache, dataCache;
+        createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+        createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
         uint8_t dummyByte = 0;
-        // Advance offset by one byte.
-        ASSERT_GE(read(cache1.getNativeHandle()->data[0], &dummyByte, 1), 0);
-        ASSERT_GE(read(cache2.getNativeHandle()->data[0], &dummyByte, 1), 0);
-        prepareModelFromCache(cache1, cache2, &preparedModel, &status);
+        // Advance the offset of each handle by one byte.
+        // The driver should be able to handle non-zero fd offset.
+        for (uint32_t i = 0; i < modelCache.size(); i++) {
+            ASSERT_GE(read(modelCache[i].getNativeHandle()->data[0], &dummyByte, 1), 0);
+        }
+        for (uint32_t i = 0; i < dataCache.size(); i++) {
+            ASSERT_GE(read(dataCache[i].getNativeHandle()->data[0], &dummyByte, 1), 0);
+        }
+        prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
         if (!mIsCachingSupported) {
             ASSERT_EQ(status, ErrorStatus::GENERAL_FAILURE);
             ASSERT_EQ(preparedModel, nullptr);
             return;
+        } else if (checkEarlyTermination(status)) {
+            ASSERT_EQ(preparedModel, nullptr);
+            return;
         } else {
             ASSERT_EQ(status, ErrorStatus::NONE);
             ASSERT_NE(preparedModel, nullptr);
@@ -285,234 +358,512 @@
                                            /*testDynamicOutputShape=*/false);
 }
 
+TEST_F(CompilationCachingTest, SaveToCacheInvalidNumCache) {
+    // Create test HIDL model and compile.
+    Model testModel = createTestModel();
+
+    // Test with number of model cache files greater than mNumModelCache.
+    {
+        bool supported;
+        hidl_vec<hidl_handle> modelCache, dataCache;
+        // Pass an additional cache file for model cache.
+        mModelCache.push_back({mTmpCache});
+        createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+        createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+        mModelCache.pop_back();
+        sp<IPreparedModel> preparedModel = nullptr;
+        saveModelToCache(testModel, modelCache, dataCache, &supported, &preparedModel);
+        if (checkEarlyTermination(supported)) return;
+        ASSERT_NE(preparedModel, nullptr);
+        // Execute and verify results.
+        generated_tests::EvaluatePreparedModel(preparedModel, [](int) { return false; },
+                                               get_examples(),
+                                               testModel.relaxComputationFloat32toFloat16,
+                                               /*testDynamicOutputShape=*/false);
+        // Check if prepareModelFromCache fails.
+        preparedModel = nullptr;
+        ErrorStatus status;
+        prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+        if (status != ErrorStatus::INVALID_ARGUMENT) {
+            ASSERT_EQ(status, ErrorStatus::GENERAL_FAILURE);
+        }
+        ASSERT_EQ(preparedModel, nullptr);
+    }
+
+    // Test with number of model cache files smaller than mNumModelCache.
+    if (mModelCache.size() > 0) {
+        bool supported;
+        hidl_vec<hidl_handle> modelCache, dataCache;
+        // Pop out the last cache file.
+        auto tmp = mModelCache.back();
+        mModelCache.pop_back();
+        createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+        createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+        mModelCache.push_back(tmp);
+        sp<IPreparedModel> preparedModel = nullptr;
+        saveModelToCache(testModel, modelCache, dataCache, &supported, &preparedModel);
+        if (checkEarlyTermination(supported)) return;
+        ASSERT_NE(preparedModel, nullptr);
+        // Execute and verify results.
+        generated_tests::EvaluatePreparedModel(preparedModel, [](int) { return false; },
+                                               get_examples(),
+                                               testModel.relaxComputationFloat32toFloat16,
+                                               /*testDynamicOutputShape=*/false);
+        // Check if prepareModelFromCache fails.
+        preparedModel = nullptr;
+        ErrorStatus status;
+        prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+        if (status != ErrorStatus::INVALID_ARGUMENT) {
+            ASSERT_EQ(status, ErrorStatus::GENERAL_FAILURE);
+        }
+        ASSERT_EQ(preparedModel, nullptr);
+    }
+
+    // Test with number of data cache files greater than mNumDataCache.
+    {
+        bool supported;
+        hidl_vec<hidl_handle> modelCache, dataCache;
+        // Pass an additional cache file for data cache.
+        mDataCache.push_back({mTmpCache});
+        createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+        createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+        mDataCache.pop_back();
+        sp<IPreparedModel> preparedModel = nullptr;
+        saveModelToCache(testModel, modelCache, dataCache, &supported, &preparedModel);
+        if (checkEarlyTermination(supported)) return;
+        ASSERT_NE(preparedModel, nullptr);
+        // Execute and verify results.
+        generated_tests::EvaluatePreparedModel(preparedModel, [](int) { return false; },
+                                               get_examples(),
+                                               testModel.relaxComputationFloat32toFloat16,
+                                               /*testDynamicOutputShape=*/false);
+        // Check if prepareModelFromCache fails.
+        preparedModel = nullptr;
+        ErrorStatus status;
+        prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+        if (status != ErrorStatus::INVALID_ARGUMENT) {
+            ASSERT_EQ(status, ErrorStatus::GENERAL_FAILURE);
+        }
+        ASSERT_EQ(preparedModel, nullptr);
+    }
+
+    // Test with number of data cache files smaller than mNumDataCache.
+    if (mDataCache.size() > 0) {
+        bool supported;
+        hidl_vec<hidl_handle> modelCache, dataCache;
+        // Pop out the last cache file.
+        auto tmp = mDataCache.back();
+        mDataCache.pop_back();
+        createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+        createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+        mDataCache.push_back(tmp);
+        sp<IPreparedModel> preparedModel = nullptr;
+        saveModelToCache(testModel, modelCache, dataCache, &supported, &preparedModel);
+        if (checkEarlyTermination(supported)) return;
+        ASSERT_NE(preparedModel, nullptr);
+        // Execute and verify results.
+        generated_tests::EvaluatePreparedModel(preparedModel, [](int) { return false; },
+                                               get_examples(),
+                                               testModel.relaxComputationFloat32toFloat16,
+                                               /*testDynamicOutputShape=*/false);
+        // Check if prepareModelFromCache fails.
+        preparedModel = nullptr;
+        ErrorStatus status;
+        prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+        if (status != ErrorStatus::INVALID_ARGUMENT) {
+            ASSERT_EQ(status, ErrorStatus::GENERAL_FAILURE);
+        }
+        ASSERT_EQ(preparedModel, nullptr);
+    }
+}
+
+TEST_F(CompilationCachingTest, PrepareModelFromCacheInvalidNumCache) {
+    // Create test HIDL model and compile.
+    Model testModel = createTestModel();
+
+    // Save the compilation to cache.
+    {
+        bool supported;
+        hidl_vec<hidl_handle> modelCache, dataCache;
+        createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+        createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+        saveModelToCache(testModel, modelCache, dataCache, &supported);
+        if (checkEarlyTermination(supported)) return;
+    }
+
+    // Test with number of model cache files greater than mNumModelCache.
+    {
+        sp<IPreparedModel> preparedModel = nullptr;
+        ErrorStatus status;
+        hidl_vec<hidl_handle> modelCache, dataCache;
+        mModelCache.push_back({mTmpCache});
+        createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+        createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+        mModelCache.pop_back();
+        prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+        if (status != ErrorStatus::GENERAL_FAILURE) {
+            ASSERT_EQ(status, ErrorStatus::INVALID_ARGUMENT);
+        }
+        ASSERT_EQ(preparedModel, nullptr);
+    }
+
+    // Test with number of model cache files smaller than mNumModelCache.
+    if (mModelCache.size() > 0) {
+        sp<IPreparedModel> preparedModel = nullptr;
+        ErrorStatus status;
+        hidl_vec<hidl_handle> modelCache, dataCache;
+        auto tmp = mModelCache.back();
+        mModelCache.pop_back();
+        createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+        createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+        mModelCache.push_back(tmp);
+        prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+        if (status != ErrorStatus::GENERAL_FAILURE) {
+            ASSERT_EQ(status, ErrorStatus::INVALID_ARGUMENT);
+        }
+        ASSERT_EQ(preparedModel, nullptr);
+    }
+
+    // Test with number of data cache files greater than mNumDataCache.
+    {
+        sp<IPreparedModel> preparedModel = nullptr;
+        ErrorStatus status;
+        hidl_vec<hidl_handle> modelCache, dataCache;
+        mDataCache.push_back({mTmpCache});
+        createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+        createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+        mDataCache.pop_back();
+        prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+        if (status != ErrorStatus::GENERAL_FAILURE) {
+            ASSERT_EQ(status, ErrorStatus::INVALID_ARGUMENT);
+        }
+        ASSERT_EQ(preparedModel, nullptr);
+    }
+
+    // Test with number of data cache files smaller than mNumDataCache.
+    if (mDataCache.size() > 0) {
+        sp<IPreparedModel> preparedModel = nullptr;
+        ErrorStatus status;
+        hidl_vec<hidl_handle> modelCache, dataCache;
+        auto tmp = mDataCache.back();
+        mDataCache.pop_back();
+        createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+        createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+        mDataCache.push_back(tmp);
+        prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+        if (status != ErrorStatus::GENERAL_FAILURE) {
+            ASSERT_EQ(status, ErrorStatus::INVALID_ARGUMENT);
+        }
+        ASSERT_EQ(preparedModel, nullptr);
+    }
+}
+
 TEST_F(CompilationCachingTest, SaveToCacheInvalidNumFd) {
     // Create test HIDL model and compile.
     Model testModel = createTestModel();
-    sp<IPreparedModel> preparedModel = nullptr;
-    generated_tests::PrepareModel(device, testModel, &preparedModel);
-    // Terminate early if the driver cannot prepare the model.
-    if (preparedModel == nullptr) return;
 
-    // cache1 with invalid NumFd.
-    {
+    // Go through each handle in model cache, test with NumFd greater than 1.
+    for (uint32_t i = 0; i < mNumModelCache; i++) {
+        bool supported;
+        hidl_vec<hidl_handle> modelCache, dataCache;
+        // Pass an invalid number of fds for handle i.
+        mModelCache[i].push_back(mTmpCache);
+        createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+        createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+        mModelCache[i].pop_back();
+        sp<IPreparedModel> preparedModel = nullptr;
+        saveModelToCache(testModel, modelCache, dataCache, &supported, &preparedModel);
+        if (checkEarlyTermination(supported)) return;
+        ASSERT_NE(preparedModel, nullptr);
+        // Execute and verify results.
+        generated_tests::EvaluatePreparedModel(preparedModel, [](int) { return false; },
+                                               get_examples(),
+                                               testModel.relaxComputationFloat32toFloat16,
+                                               /*testDynamicOutputShape=*/false);
+        // Check if prepareModelFromCache fails.
+        preparedModel = nullptr;
         ErrorStatus status;
-        hidl_handle cache1, cache2;
-        createCacheHandle({mCache1, mCache3}, AccessMode::WRITE_ONLY, &cache1);
-        createCacheHandle({mCache2}, AccessMode::WRITE_ONLY, &cache2);
-        saveModelToCache(preparedModel, cache1, cache2, &status);
-        if (status != ErrorStatus::GENERAL_FAILURE) {
-            ASSERT_EQ(status, ErrorStatus::INVALID_ARGUMENT);
+        prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+        if (status != ErrorStatus::INVALID_ARGUMENT) {
+            ASSERT_EQ(status, ErrorStatus::GENERAL_FAILURE);
         }
+        ASSERT_EQ(preparedModel, nullptr);
     }
 
-    // cache2 with invalid NumFd.
-    {
+    // Go through each handle in model cache, test with NumFd equal to 0.
+    for (uint32_t i = 0; i < mNumModelCache; i++) {
+        bool supported;
+        hidl_vec<hidl_handle> modelCache, dataCache;
+        // Pass an invalid number of fds for handle i.
+        auto tmp = mModelCache[i].back();
+        mModelCache[i].pop_back();
+        createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+        createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+        mModelCache[i].push_back(tmp);
+        sp<IPreparedModel> preparedModel = nullptr;
+        saveModelToCache(testModel, modelCache, dataCache, &supported, &preparedModel);
+        if (checkEarlyTermination(supported)) return;
+        ASSERT_NE(preparedModel, nullptr);
+        // Execute and verify results.
+        generated_tests::EvaluatePreparedModel(preparedModel, [](int) { return false; },
+                                               get_examples(),
+                                               testModel.relaxComputationFloat32toFloat16,
+                                               /*testDynamicOutputShape=*/false);
+        // Check if prepareModelFromCache fails.
+        preparedModel = nullptr;
         ErrorStatus status;
-        hidl_handle cache1, cache2;
-        createCacheHandle({mCache1}, AccessMode::WRITE_ONLY, &cache1);
-        createCacheHandle({mCache2, mCache3}, AccessMode::WRITE_ONLY, &cache2);
-        saveModelToCache(preparedModel, cache1, cache2, &status);
-        if (status != ErrorStatus::GENERAL_FAILURE) {
-            ASSERT_EQ(status, ErrorStatus::INVALID_ARGUMENT);
+        prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+        if (status != ErrorStatus::INVALID_ARGUMENT) {
+            ASSERT_EQ(status, ErrorStatus::GENERAL_FAILURE);
         }
+        ASSERT_EQ(preparedModel, nullptr);
+    }
+
+    // Go through each handle in data cache, test with NumFd greater than 1.
+    for (uint32_t i = 0; i < mNumDataCache; i++) {
+        bool supported;
+        hidl_vec<hidl_handle> modelCache, dataCache;
+        // Pass an invalid number of fds for handle i.
+        mDataCache[i].push_back(mTmpCache);
+        createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+        createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+        mDataCache[i].pop_back();
+        sp<IPreparedModel> preparedModel = nullptr;
+        saveModelToCache(testModel, modelCache, dataCache, &supported, &preparedModel);
+        if (checkEarlyTermination(supported)) return;
+        ASSERT_NE(preparedModel, nullptr);
+        // Execute and verify results.
+        generated_tests::EvaluatePreparedModel(preparedModel, [](int) { return false; },
+                                               get_examples(),
+                                               testModel.relaxComputationFloat32toFloat16,
+                                               /*testDynamicOutputShape=*/false);
+        // Check if prepareModelFromCache fails.
+        preparedModel = nullptr;
+        ErrorStatus status;
+        prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+        if (status != ErrorStatus::INVALID_ARGUMENT) {
+            ASSERT_EQ(status, ErrorStatus::GENERAL_FAILURE);
+        }
+        ASSERT_EQ(preparedModel, nullptr);
+    }
+
+    // Go through each handle in data cache, test with NumFd equal to 0.
+    for (uint32_t i = 0; i < mNumDataCache; i++) {
+        bool supported;
+        hidl_vec<hidl_handle> modelCache, dataCache;
+        // Pass an invalid number of fds for handle i.
+        auto tmp = mDataCache[i].back();
+        mDataCache[i].pop_back();
+        createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+        createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+        mDataCache[i].push_back(tmp);
+        sp<IPreparedModel> preparedModel = nullptr;
+        saveModelToCache(testModel, modelCache, dataCache, &supported, &preparedModel);
+        if (checkEarlyTermination(supported)) return;
+        ASSERT_NE(preparedModel, nullptr);
+        // Execute and verify results.
+        generated_tests::EvaluatePreparedModel(preparedModel, [](int) { return false; },
+                                               get_examples(),
+                                               testModel.relaxComputationFloat32toFloat16,
+                                               /*testDynamicOutputShape=*/false);
+        // Check if prepareModelFromCache fails.
+        preparedModel = nullptr;
+        ErrorStatus status;
+        prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+        if (status != ErrorStatus::INVALID_ARGUMENT) {
+            ASSERT_EQ(status, ErrorStatus::GENERAL_FAILURE);
+        }
+        ASSERT_EQ(preparedModel, nullptr);
     }
 }
 
 TEST_F(CompilationCachingTest, PrepareModelFromCacheInvalidNumFd) {
     // Create test HIDL model and compile.
     Model testModel = createTestModel();
-    sp<IPreparedModel> preparedModel = nullptr;
-    generated_tests::PrepareModel(device, testModel, &preparedModel);
-    // Terminate early if the driver cannot prepare the model.
-    if (preparedModel == nullptr) return;
 
     // Save the compilation to cache.
     {
-        ErrorStatus status;
-        hidl_handle cache1, cache2;
-        createCacheHandle({mCache1}, AccessMode::WRITE_ONLY, &cache1);
-        createCacheHandle({mCache2}, AccessMode::WRITE_ONLY, &cache2);
-        saveModelToCache(preparedModel, cache1, cache2, &status);
-        if (status != ErrorStatus::GENERAL_FAILURE) {
-            ASSERT_EQ(status, ErrorStatus::NONE);
-        }
+        bool supported;
+        hidl_vec<hidl_handle> modelCache, dataCache;
+        createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+        createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+        saveModelToCache(testModel, modelCache, dataCache, &supported);
+        if (checkEarlyTermination(supported)) return;
     }
 
-    // cache1 with invalid NumFd.
-    {
-        preparedModel = nullptr;
+    // Go through each handle in model cache, test with NumFd greater than 1.
+    for (uint32_t i = 0; i < mNumModelCache; i++) {
+        sp<IPreparedModel> preparedModel = nullptr;
         ErrorStatus status;
-        hidl_handle cache1, cache2;
-        createCacheHandle({mCache1, mCache3}, AccessMode::READ_ONLY, &cache1);
-        createCacheHandle({mCache2}, AccessMode::READ_ONLY, &cache2);
-        prepareModelFromCache(cache1, cache2, &preparedModel, &status);
+        hidl_vec<hidl_handle> modelCache, dataCache;
+        mModelCache[i].push_back(mTmpCache);
+        createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+        createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+        mModelCache[i].pop_back();
+        prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
         if (status != ErrorStatus::GENERAL_FAILURE) {
             ASSERT_EQ(status, ErrorStatus::INVALID_ARGUMENT);
-            ASSERT_EQ(preparedModel, nullptr);
         }
+        ASSERT_EQ(preparedModel, nullptr);
     }
 
-    // cache2 with invalid NumFd.
-    {
-        preparedModel = nullptr;
+    // Go through each handle in model cache, test with NumFd equal to 0.
+    for (uint32_t i = 0; i < mNumModelCache; i++) {
+        sp<IPreparedModel> preparedModel = nullptr;
         ErrorStatus status;
-        hidl_handle cache1, cache2;
-        createCacheHandle({mCache1}, AccessMode::READ_ONLY, &cache1);
-        createCacheHandle({mCache2, mCache3}, AccessMode::READ_ONLY, &cache2);
-        prepareModelFromCache(cache1, cache2, &preparedModel, &status);
+        hidl_vec<hidl_handle> modelCache, dataCache;
+        auto tmp = mModelCache[i].back();
+        mModelCache[i].pop_back();
+        createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+        createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+        mModelCache[i].push_back(tmp);
+        prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
         if (status != ErrorStatus::GENERAL_FAILURE) {
             ASSERT_EQ(status, ErrorStatus::INVALID_ARGUMENT);
-            ASSERT_EQ(preparedModel, nullptr);
         }
+        ASSERT_EQ(preparedModel, nullptr);
+    }
+
+    // Go through each handle in data cache, test with NumFd greater than 1.
+    for (uint32_t i = 0; i < mNumDataCache; i++) {
+        sp<IPreparedModel> preparedModel = nullptr;
+        ErrorStatus status;
+        hidl_vec<hidl_handle> modelCache, dataCache;
+        mDataCache[i].push_back(mTmpCache);
+        createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+        createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+        mDataCache[i].pop_back();
+        prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+        if (status != ErrorStatus::GENERAL_FAILURE) {
+            ASSERT_EQ(status, ErrorStatus::INVALID_ARGUMENT);
+        }
+        ASSERT_EQ(preparedModel, nullptr);
+    }
+
+    // Go through each handle in data cache, test with NumFd equal to 0.
+    for (uint32_t i = 0; i < mNumDataCache; i++) {
+        sp<IPreparedModel> preparedModel = nullptr;
+        ErrorStatus status;
+        hidl_vec<hidl_handle> modelCache, dataCache;
+        auto tmp = mDataCache[i].back();
+        mDataCache[i].pop_back();
+        createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+        createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+        mDataCache[i].push_back(tmp);
+        prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+        if (status != ErrorStatus::GENERAL_FAILURE) {
+            ASSERT_EQ(status, ErrorStatus::INVALID_ARGUMENT);
+        }
+        ASSERT_EQ(preparedModel, nullptr);
     }
 }
 
 TEST_F(CompilationCachingTest, SaveToCacheInvalidAccessMode) {
     // Create test HIDL model and compile.
     Model testModel = createTestModel();
-    sp<IPreparedModel> preparedModel = nullptr;
-    generated_tests::PrepareModel(device, testModel, &preparedModel);
-    // Terminate early if the driver cannot prepare the model.
-    if (preparedModel == nullptr) return;
+    std::vector<AccessMode> modelCacheMode(mNumModelCache, AccessMode::READ_WRITE);
+    std::vector<AccessMode> dataCacheMode(mNumDataCache, AccessMode::READ_WRITE);
 
-    // cache1 with invalid access mode.
-    {
+    // Go through each handle in model cache, test with invalid access mode.
+    for (uint32_t i = 0; i < mNumModelCache; i++) {
+        bool supported;
+        hidl_vec<hidl_handle> modelCache, dataCache;
+        modelCacheMode[i] = AccessMode::READ_ONLY;
+        createCacheHandles(mModelCache, modelCacheMode, &modelCache);
+        createCacheHandles(mDataCache, dataCacheMode, &dataCache);
+        modelCacheMode[i] = AccessMode::READ_WRITE;
+        sp<IPreparedModel> preparedModel = nullptr;
+        saveModelToCache(testModel, modelCache, dataCache, &supported, &preparedModel);
+        if (checkEarlyTermination(supported)) return;
+        ASSERT_NE(preparedModel, nullptr);
+        // Execute and verify results.
+        generated_tests::EvaluatePreparedModel(preparedModel, [](int) { return false; },
+                                               get_examples(),
+                                               testModel.relaxComputationFloat32toFloat16,
+                                               /*testDynamicOutputShape=*/false);
+        // Check if prepareModelFromCache fails.
+        preparedModel = nullptr;
         ErrorStatus status;
-        hidl_handle cache1, cache2;
-        createCacheHandle({mCache1}, AccessMode::READ_ONLY, &cache1);
-        createCacheHandle({mCache2}, AccessMode::WRITE_ONLY, &cache2);
-        saveModelToCache(preparedModel, cache1, cache2, &status);
-        ASSERT_EQ(status, ErrorStatus::GENERAL_FAILURE);
+        prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+        if (status != ErrorStatus::INVALID_ARGUMENT) {
+            ASSERT_EQ(status, ErrorStatus::GENERAL_FAILURE);
+        }
+        ASSERT_EQ(preparedModel, nullptr);
     }
 
-    // cache2 with invalid access mode.
-    {
+    // Go through each handle in data cache, test with invalid access mode.
+    for (uint32_t i = 0; i < mNumDataCache; i++) {
+        bool supported;
+        hidl_vec<hidl_handle> modelCache, dataCache;
+        dataCacheMode[i] = AccessMode::READ_ONLY;
+        createCacheHandles(mModelCache, modelCacheMode, &modelCache);
+        createCacheHandles(mDataCache, dataCacheMode, &dataCache);
+        dataCacheMode[i] = AccessMode::READ_WRITE;
+        sp<IPreparedModel> preparedModel = nullptr;
+        saveModelToCache(testModel, modelCache, dataCache, &supported, &preparedModel);
+        if (checkEarlyTermination(supported)) return;
+        ASSERT_NE(preparedModel, nullptr);
+        // Execute and verify results.
+        generated_tests::EvaluatePreparedModel(preparedModel, [](int) { return false; },
+                                               get_examples(),
+                                               testModel.relaxComputationFloat32toFloat16,
+                                               /*testDynamicOutputShape=*/false);
+        // Check if prepareModelFromCache fails.
+        preparedModel = nullptr;
         ErrorStatus status;
-        hidl_handle cache1, cache2;
-        createCacheHandle({mCache1}, AccessMode::WRITE_ONLY, &cache1);
-        createCacheHandle({mCache2}, AccessMode::READ_ONLY, &cache2);
-        saveModelToCache(preparedModel, cache1, cache2, &status);
-        ASSERT_EQ(status, ErrorStatus::GENERAL_FAILURE);
+        prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+        if (status != ErrorStatus::INVALID_ARGUMENT) {
+            ASSERT_EQ(status, ErrorStatus::GENERAL_FAILURE);
+        }
+        ASSERT_EQ(preparedModel, nullptr);
     }
 }
 
 TEST_F(CompilationCachingTest, PrepareModelFromCacheInvalidAccessMode) {
     // Create test HIDL model and compile.
     Model testModel = createTestModel();
-    sp<IPreparedModel> preparedModel = nullptr;
-    generated_tests::PrepareModel(device, testModel, &preparedModel);
-    // Terminate early if the driver cannot prepare the model.
-    if (preparedModel == nullptr) return;
+    std::vector<AccessMode> modelCacheMode(mNumModelCache, AccessMode::READ_WRITE);
+    std::vector<AccessMode> dataCacheMode(mNumDataCache, AccessMode::READ_WRITE);
 
     // Save the compilation to cache.
     {
-        ErrorStatus status;
-        hidl_handle cache1, cache2;
-        createCacheHandle({mCache1}, AccessMode::WRITE_ONLY, &cache1);
-        createCacheHandle({mCache2}, AccessMode::WRITE_ONLY, &cache2);
-        saveModelToCache(preparedModel, cache1, cache2, &status);
-        if (status != ErrorStatus::GENERAL_FAILURE) {
-            ASSERT_EQ(status, ErrorStatus::NONE);
-        }
+        bool supported;
+        hidl_vec<hidl_handle> modelCache, dataCache;
+        createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+        createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+        saveModelToCache(testModel, modelCache, dataCache, &supported);
+        if (checkEarlyTermination(supported)) return;
     }
 
-    // cache1 with invalid access mode.
-    {
-        preparedModel = nullptr;
+    // Go through each handle in model cache, test with invalid access mode.
+    for (uint32_t i = 0; i < mNumModelCache; i++) {
+        sp<IPreparedModel> preparedModel = nullptr;
         ErrorStatus status;
-        hidl_handle cache1, cache2;
-        createCacheHandle({mCache1}, AccessMode::WRITE_ONLY, &cache1);
-        createCacheHandle({mCache2}, AccessMode::READ_ONLY, &cache2);
-        prepareModelFromCache(cache1, cache2, &preparedModel, &status);
+        hidl_vec<hidl_handle> modelCache, dataCache;
+        modelCacheMode[i] = AccessMode::WRITE_ONLY;
+        createCacheHandles(mModelCache, modelCacheMode, &modelCache);
+        createCacheHandles(mDataCache, dataCacheMode, &dataCache);
+        modelCacheMode[i] = AccessMode::READ_WRITE;
+        prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
         ASSERT_EQ(status, ErrorStatus::GENERAL_FAILURE);
         ASSERT_EQ(preparedModel, nullptr);
     }
 
-    // cache2 with invalid access mode.
-    {
-        preparedModel = nullptr;
+    // Go through each handle in data cache, test with invalid access mode.
+    for (uint32_t i = 0; i < mNumDataCache; i++) {
+        sp<IPreparedModel> preparedModel = nullptr;
         ErrorStatus status;
-        hidl_handle cache1, cache2;
-        createCacheHandle({mCache1}, AccessMode::READ_ONLY, &cache1);
-        createCacheHandle({mCache2}, AccessMode::WRITE_ONLY, &cache2);
-        prepareModelFromCache(cache1, cache2, &preparedModel, &status);
+        hidl_vec<hidl_handle> modelCache, dataCache;
+        dataCacheMode[i] = AccessMode::WRITE_ONLY;
+        createCacheHandles(mModelCache, modelCacheMode, &modelCache);
+        createCacheHandles(mDataCache, dataCacheMode, &dataCache);
+        dataCacheMode[i] = AccessMode::READ_WRITE;
+        prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
         ASSERT_EQ(status, ErrorStatus::GENERAL_FAILURE);
         ASSERT_EQ(preparedModel, nullptr);
     }
 }
 
-TEST_F(CompilationCachingTest, SaveToCacheInvalidOffset) {
-    // Create test HIDL model and compile.
-    Model testModel = createTestModel();
-    sp<IPreparedModel> preparedModel = nullptr;
-    generated_tests::PrepareModel(device, testModel, &preparedModel);
-    // Terminate early if the driver cannot prepare the model.
-    if (preparedModel == nullptr) return;
-
-    // cache1 with invalid file descriptor offset.
-    {
-        ErrorStatus status;
-        hidl_handle cache1, cache2;
-        createCacheHandle({mCache1}, AccessMode::WRITE_ONLY, &cache1);
-        createCacheHandle({mCache2}, AccessMode::WRITE_ONLY, &cache2);
-        uint8_t dummyByte = 0;
-        // Advance offset by one byte.
-        ASSERT_EQ(write(cache1.getNativeHandle()->data[0], &dummyByte, 1), 1);
-        saveModelToCache(preparedModel, cache1, cache2, &status);
-        ASSERT_EQ(status, ErrorStatus::GENERAL_FAILURE);
-    }
-
-    // cache2 with invalid file descriptor offset.
-    {
-        ErrorStatus status;
-        hidl_handle cache1, cache2;
-        createCacheHandle({mCache1}, AccessMode::WRITE_ONLY, &cache1);
-        createCacheHandle({mCache2}, AccessMode::WRITE_ONLY, &cache2);
-        uint8_t dummyByte = 0;
-        // Advance offset by one byte.
-        ASSERT_EQ(write(cache2.getNativeHandle()->data[0], &dummyByte, 1), 1);
-        saveModelToCache(preparedModel, cache1, cache2, &status);
-        ASSERT_EQ(status, ErrorStatus::GENERAL_FAILURE);
-    }
-}
-
-TEST_F(CompilationCachingTest, SaveToCacheInvalidFileSize) {
-    // Create test HIDL model and compile.
-    Model testModel = createTestModel();
-    sp<IPreparedModel> preparedModel = nullptr;
-    generated_tests::PrepareModel(device, testModel, &preparedModel);
-    // Terminate early if the driver cannot prepare the model.
-    if (preparedModel == nullptr) return;
-
-    // cache1 with invalid file size.
-    {
-        ErrorStatus status;
-        hidl_handle cache1, cache2;
-        createCacheHandle({mCache1}, AccessMode::WRITE_ONLY, &cache1);
-        createCacheHandle({mCache2}, AccessMode::WRITE_ONLY, &cache2);
-        uint8_t dummyByte = 0;
-        // Write one byte and seek back to the beginning.
-        ASSERT_EQ(write(cache1.getNativeHandle()->data[0], &dummyByte, 1), 1);
-        ASSERT_EQ(lseek(cache1.getNativeHandle()->data[0], 0, SEEK_SET), 0);
-        saveModelToCache(preparedModel, cache1, cache2, &status);
-        ASSERT_EQ(status, ErrorStatus::GENERAL_FAILURE);
-    }
-
-    // cache2 with invalid file size.
-    {
-        ErrorStatus status;
-        hidl_handle cache1, cache2;
-        createCacheHandle({mCache1}, AccessMode::WRITE_ONLY, &cache1);
-        createCacheHandle({mCache2}, AccessMode::WRITE_ONLY, &cache2);
-        uint8_t dummyByte = 0;
-        // Write one byte and seek back to the beginning.
-        ASSERT_EQ(write(cache2.getNativeHandle()->data[0], &dummyByte, 1), 1);
-        ASSERT_EQ(lseek(cache2.getNativeHandle()->data[0], 0, SEEK_SET), 0);
-        saveModelToCache(preparedModel, cache1, cache2, &status);
-        ASSERT_EQ(status, ErrorStatus::GENERAL_FAILURE);
-    }
-}
-
 class CompilationCachingSecurityTest : public CompilationCachingTest,
                                        public ::testing::WithParamInterface<uint32_t> {
   protected:
@@ -537,44 +888,44 @@
 
     // Create test HIDL model and compile.
     Model testModel = createTestModel();
-    sp<IPreparedModel> preparedModel = nullptr;
-    generated_tests::PrepareModel(device, testModel, &preparedModel);
-    // Terminate early if the driver cannot prepare the model.
-    if (preparedModel == nullptr) return;
 
-    // Save the compilation to cache.
-    {
-        ErrorStatus status;
-        hidl_handle cache1, cache2;
-        createCacheHandle({mCache1}, AccessMode::WRITE_ONLY, &cache1);
-        createCacheHandle({mCache2}, AccessMode::WRITE_ONLY, &cache2);
-        saveModelToCache(preparedModel, cache1, cache2, &status);
-        if (checkEarlyTermination(status)) return;
-        ASSERT_EQ(status, ErrorStatus::NONE);
-    }
+    for (uint32_t i = 0; i < mNumModelCache; i++) {
+        // Save the compilation to cache.
+        {
+            bool supported;
+            hidl_vec<hidl_handle> modelCache, dataCache;
+            createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+            createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+            saveModelToCache(testModel, modelCache, dataCache, &supported);
+            if (checkEarlyTermination(supported)) return;
+        }
 
-    // Randomly flip one single bit of the cache entry.
-    FILE* pFile = fopen(mCache1.c_str(), "r+");
-    ASSERT_EQ(fseek(pFile, 0, SEEK_END), 0);
-    long int fileSize = ftell(pFile);
-    ASSERT_GT(fileSize, 0);
-    ASSERT_EQ(fseek(pFile, getRandomInt(0l, fileSize - 1), SEEK_SET), 0);
-    int readByte = fgetc(pFile);
-    ASSERT_NE(readByte, EOF);
-    ASSERT_EQ(fseek(pFile, -1, SEEK_CUR), 0);
-    ASSERT_NE(fputc(static_cast<uint8_t>(readByte) ^ (1U << getRandomInt(0, 7)), pFile), EOF);
-    fclose(pFile);
+        // Randomly flip one single bit of the cache entry.
+        FILE* pFile = fopen(mModelCache[i][0].c_str(), "r+");
+        ASSERT_EQ(fseek(pFile, 0, SEEK_END), 0);
+        long int fileSize = ftell(pFile);
+        if (fileSize == 0) {
+            fclose(pFile);
+            continue;
+        }
+        ASSERT_EQ(fseek(pFile, getRandomInt(0l, fileSize - 1), SEEK_SET), 0);
+        int readByte = fgetc(pFile);
+        ASSERT_NE(readByte, EOF);
+        ASSERT_EQ(fseek(pFile, -1, SEEK_CUR), 0);
+        ASSERT_NE(fputc(static_cast<uint8_t>(readByte) ^ (1U << getRandomInt(0, 7)), pFile), EOF);
+        fclose(pFile);
 
-    // Retrieve preparedModel from cache, expect failure.
-    {
-        preparedModel = nullptr;
-        ErrorStatus status;
-        hidl_handle cache1, cache2;
-        createCacheHandle({mCache1}, AccessMode::READ_ONLY, &cache1);
-        createCacheHandle({mCache2}, AccessMode::READ_ONLY, &cache2);
-        prepareModelFromCache(cache1, cache2, &preparedModel, &status);
-        ASSERT_EQ(status, ErrorStatus::GENERAL_FAILURE);
-        ASSERT_EQ(preparedModel, nullptr);
+        // Retrieve preparedModel from cache, expect failure.
+        {
+            sp<IPreparedModel> preparedModel = nullptr;
+            ErrorStatus status;
+            hidl_vec<hidl_handle> modelCache, dataCache;
+            createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+            createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+            prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+            ASSERT_EQ(status, ErrorStatus::GENERAL_FAILURE);
+            ASSERT_EQ(preparedModel, nullptr);
+        }
     }
 }
 
@@ -583,40 +934,37 @@
 
     // Create test HIDL model and compile.
     Model testModel = createTestModel();
-    sp<IPreparedModel> preparedModel = nullptr;
-    generated_tests::PrepareModel(device, testModel, &preparedModel);
-    // Terminate early if the driver cannot prepare the model.
-    if (preparedModel == nullptr) return;
 
-    // Save the compilation to cache.
-    {
-        ErrorStatus status;
-        hidl_handle cache1, cache2;
-        createCacheHandle({mCache1}, AccessMode::WRITE_ONLY, &cache1);
-        createCacheHandle({mCache2}, AccessMode::WRITE_ONLY, &cache2);
-        saveModelToCache(preparedModel, cache1, cache2, &status);
-        if (checkEarlyTermination(status)) return;
-        ASSERT_EQ(status, ErrorStatus::NONE);
-    }
+    for (uint32_t i = 0; i < mNumModelCache; i++) {
+        // Save the compilation to cache.
+        {
+            bool supported;
+            hidl_vec<hidl_handle> modelCache, dataCache;
+            createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+            createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+            saveModelToCache(testModel, modelCache, dataCache, &supported);
+            if (checkEarlyTermination(supported)) return;
+        }
 
-    // Randomly append bytes to the cache entry.
-    FILE* pFile = fopen(mCache1.c_str(), "a");
-    uint32_t appendLength = getRandomInt(1, 256);
-    for (uint32_t i = 0; i < appendLength; i++) {
-        ASSERT_NE(fputc(getRandomInt<uint8_t>(0, 255), pFile), EOF);
-    }
-    fclose(pFile);
+        // Randomly append bytes to the cache entry.
+        FILE* pFile = fopen(mModelCache[i][0].c_str(), "a");
+        uint32_t appendLength = getRandomInt(1, 256);
+        for (uint32_t i = 0; i < appendLength; i++) {
+            ASSERT_NE(fputc(getRandomInt<uint8_t>(0, 255), pFile), EOF);
+        }
+        fclose(pFile);
 
-    // Retrieve preparedModel from cache, expect failure.
-    {
-        preparedModel = nullptr;
-        ErrorStatus status;
-        hidl_handle cache1, cache2;
-        createCacheHandle({mCache1}, AccessMode::READ_ONLY, &cache1);
-        createCacheHandle({mCache2}, AccessMode::READ_ONLY, &cache2);
-        prepareModelFromCache(cache1, cache2, &preparedModel, &status);
-        ASSERT_EQ(status, ErrorStatus::GENERAL_FAILURE);
-        ASSERT_EQ(preparedModel, nullptr);
+        // Retrieve preparedModel from cache, expect failure.
+        {
+            sp<IPreparedModel> preparedModel = nullptr;
+            ErrorStatus status;
+            hidl_vec<hidl_handle> modelCache, dataCache;
+            createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+            createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+            prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
+            ASSERT_EQ(status, ErrorStatus::GENERAL_FAILURE);
+            ASSERT_EQ(preparedModel, nullptr);
+        }
     }
 }
 
@@ -625,20 +973,15 @@
 
     // Create test HIDL model and compile.
     Model testModel = createTestModel();
-    sp<IPreparedModel> preparedModel = nullptr;
-    generated_tests::PrepareModel(device, testModel, &preparedModel);
-    // Terminate early if the driver cannot prepare the model.
-    if (preparedModel == nullptr) return;
 
     // Save the compilation to cache.
     {
-        ErrorStatus status;
-        hidl_handle cache1, cache2;
-        createCacheHandle({mCache1}, AccessMode::WRITE_ONLY, &cache1);
-        createCacheHandle({mCache2}, AccessMode::WRITE_ONLY, &cache2);
-        saveModelToCache(preparedModel, cache1, cache2, &status);
-        if (checkEarlyTermination(status)) return;
-        ASSERT_EQ(status, ErrorStatus::NONE);
+        bool supported;
+        hidl_vec<hidl_handle> modelCache, dataCache;
+        createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+        createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+        saveModelToCache(testModel, modelCache, dataCache, &supported);
+        if (checkEarlyTermination(supported)) return;
     }
 
     // Randomly flip one single bit in mToken.
@@ -647,12 +990,12 @@
 
     // Retrieve the preparedModel from cache, expect failure.
     {
-        preparedModel = nullptr;
+        sp<IPreparedModel> preparedModel = nullptr;
         ErrorStatus status;
-        hidl_handle cache1, cache2;
-        createCacheHandle({mCache1}, AccessMode::READ_ONLY, &cache1);
-        createCacheHandle({mCache2}, AccessMode::READ_ONLY, &cache2);
-        prepareModelFromCache(cache1, cache2, &preparedModel, &status);
+        hidl_vec<hidl_handle> modelCache, dataCache;
+        createCacheHandles(mModelCache, AccessMode::READ_WRITE, &modelCache);
+        createCacheHandles(mDataCache, AccessMode::READ_WRITE, &dataCache);
+        prepareModelFromCache(modelCache, dataCache, &preparedModel, &status);
         ASSERT_EQ(status, ErrorStatus::GENERAL_FAILURE);
         ASSERT_EQ(preparedModel, nullptr);
     }
diff --git a/neuralnetworks/1.2/vts/functional/ValidateModel.cpp b/neuralnetworks/1.2/vts/functional/ValidateModel.cpp
index c2330b5..2988211 100644
--- a/neuralnetworks/1.2/vts/functional/ValidateModel.cpp
+++ b/neuralnetworks/1.2/vts/functional/ValidateModel.cpp
@@ -33,6 +33,7 @@
 
 using ::android::hardware::neuralnetworks::V1_2::implementation::ExecutionCallback;
 using ::android::hardware::neuralnetworks::V1_2::implementation::PreparedModelCallback;
+using HidlToken = hidl_array<uint8_t, static_cast<uint32_t>(Constant::BYTE_SIZE_OF_CACHE_TOKEN)>;
 
 ///////////////////////// UTILITY FUNCTIONS /////////////////////////
 
@@ -54,7 +55,8 @@
     sp<PreparedModelCallback> preparedModelCallback = new PreparedModelCallback();
     ASSERT_NE(nullptr, preparedModelCallback.get());
     Return<ErrorStatus> prepareLaunchStatus =
-        device->prepareModel_1_2(model, preference, preparedModelCallback);
+            device->prepareModel_1_2(model, preference, hidl_vec<hidl_handle>(),
+                                     hidl_vec<hidl_handle>(), HidlToken(), preparedModelCallback);
     ASSERT_TRUE(prepareLaunchStatus.isOk());
     ASSERT_EQ(ErrorStatus::INVALID_ARGUMENT, static_cast<ErrorStatus>(prepareLaunchStatus));
 
diff --git a/neuralnetworks/1.2/vts/functional/ValidateRequest.cpp b/neuralnetworks/1.2/vts/functional/ValidateRequest.cpp
index d411da4..b15f657 100644
--- a/neuralnetworks/1.2/vts/functional/ValidateRequest.cpp
+++ b/neuralnetworks/1.2/vts/functional/ValidateRequest.cpp
@@ -37,6 +37,7 @@
 using ::android::hardware::neuralnetworks::V1_2::implementation::ExecutionCallback;
 using ::android::hardware::neuralnetworks::V1_2::implementation::PreparedModelCallback;
 using ::android::hidl::memory::V1_0::IMemory;
+using HidlToken = hidl_array<uint8_t, static_cast<uint32_t>(Constant::BYTE_SIZE_OF_CACHE_TOKEN)>;
 using test_helper::for_all;
 using test_helper::MixedTyped;
 using test_helper::MixedTypedExample;
@@ -66,7 +67,8 @@
     sp<PreparedModelCallback> preparedModelCallback = new PreparedModelCallback();
     ASSERT_NE(nullptr, preparedModelCallback.get());
     Return<ErrorStatus> prepareLaunchStatus = device->prepareModel_1_2(
-        model, ExecutionPreference::FAST_SINGLE_ANSWER, preparedModelCallback);
+            model, ExecutionPreference::FAST_SINGLE_ANSWER, hidl_vec<hidl_handle>(),
+            hidl_vec<hidl_handle>(), HidlToken(), preparedModelCallback);
     ASSERT_TRUE(prepareLaunchStatus.isOk());
     ASSERT_EQ(ErrorStatus::NONE, static_cast<ErrorStatus>(prepareLaunchStatus));
 
diff --git a/thermal/2.0/default/Thermal.cpp b/thermal/2.0/default/Thermal.cpp
index 0ef4b63..bbbecb8 100644
--- a/thermal/2.0/default/Thermal.cpp
+++ b/thermal/2.0/default/Thermal.cpp
@@ -38,46 +38,47 @@
 std::set<sp<IThermalChangedCallback>> gCallbacks;
 
 static const Temperature_1_0 kTemp_1_0 = {
-    .type = static_cast<::android::hardware::thermal::V1_0::TemperatureType>(TemperatureType::CPU),
-    .name = "test temperature sensor",
-    .currentValue = 98.6,
-    .throttlingThreshold = 58,
-    .shutdownThreshold = 60.0,
-    .vrThrottlingThreshold = 59.0,
+        .type = static_cast<::android::hardware::thermal::V1_0::TemperatureType>(
+                TemperatureType::SKIN),
+        .name = "test temperature sensor",
+        .currentValue = 30.8,
+        .throttlingThreshold = 48.0,
+        .shutdownThreshold = 60.0,
+        .vrThrottlingThreshold = 49.0,
 };
 
 static const Temperature_2_0 kTemp_2_0 = {
-    .type = TemperatureType::SKIN,
-    .name = "test temperature sensor",
-    .value = 98.6,
-    .throttlingStatus = ThrottlingSeverity::CRITICAL,
+        .type = TemperatureType::SKIN,
+        .name = "test temperature sensor",
+        .value = 30.8,
+        .throttlingStatus = ThrottlingSeverity::NONE,
 };
 
 static const TemperatureThreshold kTempThreshold = {
-    .type = TemperatureType::SKIN,
-    .name = "test temperature sensor",
-    .hotThrottlingThresholds = {{NAN, NAN, NAN, NAN, NAN, NAN, NAN}},
-    .coldThrottlingThresholds = {{NAN, NAN, NAN, NAN, NAN, NAN, NAN}},
-    .vrThrottlingThreshold = NAN,
+        .type = TemperatureType::SKIN,
+        .name = "test temperature sensor",
+        .hotThrottlingThresholds = {{NAN, NAN, NAN, 48.0, NAN, NAN, 60.0}},
+        .coldThrottlingThresholds = {{NAN, NAN, NAN, NAN, NAN, NAN, NAN}},
+        .vrThrottlingThreshold = 49.0,
 };
 
 static const CoolingDevice_1_0 kCooling_1_0 = {
-    .type = ::android::hardware::thermal::V1_0::CoolingType::FAN_RPM,
-    .name = "test cooling device",
-    .currentValue = 100.0,
+        .type = ::android::hardware::thermal::V1_0::CoolingType::FAN_RPM,
+        .name = "test cooling device",
+        .currentValue = 100.0,
 };
 
 static const CoolingDevice_2_0 kCooling_2_0 = {
-    .type = CoolingType::CPU,
-    .name = "test cooling device",
-    .value = 1,
+        .type = CoolingType::FAN,
+        .name = "test cooling device",
+        .value = 100,
 };
 
 static const CpuUsage kCpuUsage = {
-    .name = "cpu_name",
-    .active = 0,
-    .total = 0,
-    .isOnline = true,
+        .name = "cpu_name",
+        .active = 0,
+        .total = 0,
+        .isOnline = true,
 };
 
 // Methods from ::android::hardware::thermal::V1_0::IThermal follow.
diff --git a/vibrator/1.3/Android.bp b/vibrator/1.3/Android.bp
index 28370d6..a2ff784 100644
--- a/vibrator/1.3/Android.bp
+++ b/vibrator/1.3/Android.bp
@@ -8,6 +8,7 @@
     },
     srcs: [
         "IVibrator.hal",
+        "types.hal",
     ],
     interfaces: [
         "android.hardware.vibrator@1.0",
diff --git a/vibrator/1.3/IVibrator.hal b/vibrator/1.3/IVibrator.hal
index 01c2801..1c870ee 100644
--- a/vibrator/1.3/IVibrator.hal
+++ b/vibrator/1.3/IVibrator.hal
@@ -16,6 +16,7 @@
 
 package android.hardware.vibrator@1.3;
 
+import @1.0::EffectStrength;
 import @1.0::Status;
 import @1.2::IVibrator;
 
@@ -41,4 +42,18 @@
    *                not supported by the device.
    */
   setExternalControl(bool enabled) generates (Status status);
+
+  /**
+   * Fire off a predefined haptic event.
+   *
+   * @param event The type of haptic event to trigger.
+   * @return status Whether the effect was successfully performed or not. Must
+   *     return Status::UNSUPPORTED_OPERATION if the effect is not supported.
+   * @return lengthMs The length of time the event is expected to take in
+   *     milliseconds. This doesn't need to be perfectly accurate, but should be a reasonable
+   *     approximation. Should be a positive, non-zero value if the returned status is Status::OK,
+   *     and set to 0 otherwise.
+   */
+  perform_1_3(Effect effect, EffectStrength strength)
+          generates (Status status, uint32_t lengthMs);
 };
diff --git a/vibrator/1.3/example/Vibrator.cpp b/vibrator/1.3/example/Vibrator.cpp
index bb9a057..0cb37e6 100644
--- a/vibrator/1.3/example/Vibrator.cpp
+++ b/vibrator/1.3/example/Vibrator.cpp
@@ -74,22 +74,9 @@
 
 // Methods from ::android::hardware::vibrator::V1_2::IVibrator follow.
 
-Return<void> Vibrator::perform_1_2(Effect effect, EffectStrength strength, perform_cb _hidl_cb) {
-    uint8_t amplitude;
-    uint32_t ms;
-    Status status;
-
-    ALOGI("Perform: Effect %s\n", effectToName(effect));
-
-    amplitude = strengthToAmplitude(strength);
-    setAmplitude(amplitude);
-
-    ms = effectToMs(effect);
-    status = activate(ms);
-
-    _hidl_cb(status, ms);
-
-    return Void();
+Return<void> Vibrator::perform_1_2(V1_2::Effect effect, EffectStrength strength,
+                                   perform_cb _hidl_cb) {
+    return perform_1_3(static_cast<V1_3::Effect>(effect), strength, _hidl_cb);
 }
 
 // Methods from ::android::hardware::vibrator::V1_3::IVibrator follow.
@@ -110,6 +97,32 @@
     }
 }
 
+Return<void> Vibrator::perform_1_3(Effect effect, EffectStrength strength, perform_cb _hidl_cb) {
+    uint8_t amplitude;
+    uint32_t ms;
+    Status status = Status::OK;
+
+    ALOGI("Perform: Effect %s\n", effectToName(effect).c_str());
+
+    amplitude = strengthToAmplitude(strength, &status);
+    if (status != Status::OK) {
+        _hidl_cb(status, 0);
+        return Void();
+    }
+    setAmplitude(amplitude);
+
+    ms = effectToMs(effect, &status);
+    if (status != Status::OK) {
+        _hidl_cb(status, 0);
+        return Void();
+    }
+    status = activate(ms);
+
+    _hidl_cb(status, ms);
+
+    return Void();
+}
+
 // Private methods follow.
 
 Status Vibrator::enable(bool enabled) {
@@ -173,17 +186,18 @@
     static_cast<Vibrator*>(sigval.sival_ptr)->timeout();
 }
 
-const char* Vibrator::effectToName(Effect effect) {
-    return toString(effect).c_str();
+const std::string Vibrator::effectToName(Effect effect) {
+    return toString(effect);
 }
 
-uint32_t Vibrator::effectToMs(Effect effect) {
+uint32_t Vibrator::effectToMs(Effect effect, Status* status) {
     switch (effect) {
         case Effect::CLICK:
             return 10;
         case Effect::DOUBLE_CLICK:
             return 15;
         case Effect::TICK:
+        case Effect::TEXTURE_TICK:
             return 5;
         case Effect::THUD:
             return 5;
@@ -222,9 +236,11 @@
         case Effect::RINGTONE_15:
             return 30000;
     }
+    *status = Status::UNSUPPORTED_OPERATION;
+    return 0;
 }
 
-uint8_t Vibrator::strengthToAmplitude(EffectStrength strength) {
+uint8_t Vibrator::strengthToAmplitude(EffectStrength strength, Status* status) {
     switch (strength) {
         case EffectStrength::LIGHT:
             return 128;
@@ -233,6 +249,8 @@
         case EffectStrength::STRONG:
             return 255;
     }
+    *status = Status::UNSUPPORTED_OPERATION;
+    return 0;
 }
 
 }  // namespace implementation
diff --git a/vibrator/1.3/example/Vibrator.h b/vibrator/1.3/example/Vibrator.h
index a931b63..64e8e1b 100644
--- a/vibrator/1.3/example/Vibrator.h
+++ b/vibrator/1.3/example/Vibrator.h
@@ -27,7 +27,6 @@
 
 using android::hardware::vibrator::V1_0::EffectStrength;
 using android::hardware::vibrator::V1_0::Status;
-using android::hardware::vibrator::V1_2::Effect;
 
 class Vibrator : public IVibrator {
   public:
@@ -46,11 +45,13 @@
                              perform_cb _hidl_cb) override;
 
     // Methods from ::android::hardware::vibrator::V1_2::IVibrator follow.
-    Return<void> perform_1_2(Effect effect, EffectStrength strength, perform_cb _hidl_cb) override;
+    Return<void> perform_1_2(V1_2::Effect effect, EffectStrength strength,
+                             perform_cb _hidl_cb) override;
 
     // Methods from ::android::hardware::vibrator::V1_3::IVibrator follow.
     Return<bool> supportsExternalControl() override;
     Return<Status> setExternalControl(bool enabled) override;
+    Return<void> perform_1_3(Effect effect, EffectStrength strength, perform_cb _hidl_cb) override;
 
   private:
     Status enable(bool enabled);
@@ -58,9 +59,9 @@
     void timeout();
 
     static void timerCallback(union sigval sigval);
-    static const char* effectToName(Effect effect);
-    static uint32_t effectToMs(Effect effect);
-    static uint8_t strengthToAmplitude(EffectStrength strength);
+    static const std::string effectToName(Effect effect);
+    static uint32_t effectToMs(Effect effect, Status* status);
+    static uint8_t strengthToAmplitude(EffectStrength strength, Status* status);
 
   private:
     bool mEnabled{false};
diff --git a/vibrator/1.3/types.hal b/vibrator/1.3/types.hal
new file mode 100644
index 0000000..ceb62a5
--- /dev/null
+++ b/vibrator/1.3/types.hal
@@ -0,0 +1,30 @@
+/*
+ * Copyright (C) 2019 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package android.hardware.vibrator@1.3;
+
+import @1.2::Effect;
+
+enum Effect : @1.2::Effect {
+     /**
+      * A soft tick effect meant to be played as a texture.
+      *
+      * A soft, short sensation like the tick of a clock. Unlike regular effects, texture effects
+      * are expected to be played multiple times in quick succession, replicating a specific
+      * texture to the user as a form of haptic feedback.
+      */
+     TEXTURE_TICK
+};
diff --git a/vibrator/1.3/vts/functional/VtsHalVibratorV1_3TargetTest.cpp b/vibrator/1.3/vts/functional/VtsHalVibratorV1_3TargetTest.cpp
index a67d1dc..818f9c7 100644
--- a/vibrator/1.3/vts/functional/VtsHalVibratorV1_3TargetTest.cpp
+++ b/vibrator/1.3/vts/functional/VtsHalVibratorV1_3TargetTest.cpp
@@ -24,9 +24,16 @@
 #include <unistd.h>
 
 using ::android::sp;
+using ::android::hardware::hidl_enum_range;
+using ::android::hardware::Return;
+using ::android::hardware::Void;
+using ::android::hardware::vibrator::V1_0::EffectStrength;
 using ::android::hardware::vibrator::V1_0::Status;
+using ::android::hardware::vibrator::V1_3::Effect;
 using ::android::hardware::vibrator::V1_3::IVibrator;
 
+#define EXPECT_OK(ret) ASSERT_TRUE((ret).isOk())
+
 // Test environment for Vibrator HIDL HAL.
 class VibratorHidlEnvironment : public ::testing::VtsHalHidlTargetTestEnvBase {
    public:
@@ -71,6 +78,74 @@
     }
 }
 
+static void validatePerformEffectUnsupportedOperation(Status status, uint32_t lengthMs) {
+    ASSERT_EQ(Status::UNSUPPORTED_OPERATION, status);
+    ASSERT_EQ(static_cast<uint32_t>(0), lengthMs)
+            << "Effects that return UNSUPPORTED_OPERATION must have a duration of zero";
+}
+
+static void validatePerformEffect(Status status, uint32_t lengthMs) {
+    ASSERT_TRUE(status == Status::OK || status == Status::UNSUPPORTED_OPERATION);
+    if (status == Status::OK) {
+        ASSERT_LT(static_cast<uint32_t>(0), lengthMs)
+                << "Effects that return OK must return a positive duration";
+    } else {
+        validatePerformEffectUnsupportedOperation(status, lengthMs);
+    }
+}
+
+/*
+ * Test to make sure effects within the valid range return are either supported and return OK with
+ * a valid duration, or are unsupported and return UNSUPPORTED_OPERATION with a duration of 0.
+ */
+TEST_F(VibratorHidlTest_1_3, PerformEffect_1_3) {
+    for (const auto& effect : hidl_enum_range<Effect>()) {
+        for (const auto& strength : hidl_enum_range<EffectStrength>()) {
+            EXPECT_OK(vibrator->perform_1_3(effect, strength, validatePerformEffect));
+        }
+    }
+}
+
+/*
+ * Test to make sure effect values above the valid range are rejected.
+ */
+TEST_F(VibratorHidlTest_1_3, PerformEffect_1_3_BadEffects_AboveValidRange) {
+    Effect effect = *std::prev(hidl_enum_range<Effect>().end());
+    Effect badEffect = static_cast<Effect>(static_cast<int32_t>(effect) + 1);
+    EXPECT_OK(vibrator->perform_1_3(badEffect, EffectStrength::LIGHT,
+                                    validatePerformEffectUnsupportedOperation));
+}
+
+/*
+ * Test to make sure effect values below the valid range are rejected.
+ */
+TEST_F(VibratorHidlTest_1_3, PerformEffect_1_3_BadEffects_BelowValidRange) {
+    Effect effect = *hidl_enum_range<Effect>().begin();
+    Effect badEffect = static_cast<Effect>(static_cast<int32_t>(effect) - 1);
+    EXPECT_OK(vibrator->perform_1_3(badEffect, EffectStrength::LIGHT,
+                                    validatePerformEffectUnsupportedOperation));
+}
+
+/*
+ * Test to make sure strength values above the valid range are rejected.
+ */
+TEST_F(VibratorHidlTest_1_3, PerformEffect_1_3_BadStrength_AboveValidRange) {
+    EffectStrength strength = *std::prev(hidl_enum_range<EffectStrength>().end());
+    EffectStrength badStrength = static_cast<EffectStrength>(static_cast<int32_t>(strength) + 1);
+    EXPECT_OK(vibrator->perform_1_3(Effect::THUD, badStrength,
+                                    validatePerformEffectUnsupportedOperation));
+}
+
+/*
+ * Test to make sure strength values below the valid range are rejected.
+ */
+TEST_F(VibratorHidlTest_1_3, PerformEffect_1_3_BadStrength_BelowValidRange) {
+    EffectStrength strength = *hidl_enum_range<EffectStrength>().begin();
+    EffectStrength badStrength = static_cast<EffectStrength>(static_cast<int32_t>(strength) - 1);
+    EXPECT_OK(vibrator->perform_1_3(Effect::THUD, badStrength,
+                                    validatePerformEffectUnsupportedOperation));
+}
+
 int main(int argc, char** argv) {
     ::testing::AddGlobalTestEnvironment(VibratorHidlEnvironment::Instance());
     ::testing::InitGoogleTest(&argc, argv);
diff --git a/wifi/1.3/IWifiChip.hal b/wifi/1.3/IWifiChip.hal
index fc6dbac..72cee89 100644
--- a/wifi/1.3/IWifiChip.hal
+++ b/wifi/1.3/IWifiChip.hal
@@ -65,10 +65,14 @@
     /**
      * API to set the wifi latency mode
      *
-     * Latency mode determines whether or not to optimize for reducing wifi
-     * latency as a tradeoff with other wifi functionality such as scanning,
-     * roaming, etc. This optimization is suitable for some applications such
-     * as gaming and virtual reality applications.
+     * The latency mode is a hint to the HAL to enable or disable Wi-Fi latency
+     * optimization. The optimization should be enabled if the mode is set to |LOW|
+     * and should be disabled if the mode is set to |NORMAL|.
+     * Wi-Fi latency optimization may trade-off latency against other Wi-Fi
+     * functionality such as scanning, roaming, etc. but it should not result in
+     * completely halting this functionality.
+     *
+     * The low latency mode targets applications such as gaming and virtual reality.
      */
     setLatencyMode(LatencyMode mode) generates (WifiStatus status);
 
diff --git a/wifi/1.3/default/tests/mock_wifi_legacy_hal.h b/wifi/1.3/default/tests/mock_wifi_legacy_hal.h
index deb3a5a..65fd115 100644
--- a/wifi/1.3/default/tests/mock_wifi_legacy_hal.h
+++ b/wifi/1.3/default/tests/mock_wifi_legacy_hal.h
@@ -39,6 +39,10 @@
     MOCK_METHOD2(registerRadioModeChangeCallbackHandler,
                  wifi_error(const std::string&,
                             const on_radio_mode_change_callback&));
+    MOCK_METHOD1(getFirmwareVersion, std::pair<wifi_error, std::string>(
+                 const std::string& iface_name));
+    MOCK_METHOD1(getDriverVersion, std::pair<wifi_error, std::string>(
+                 const std::string& iface_name));
     MOCK_METHOD2(nanRegisterCallbackHandlers,
                  wifi_error(const std::string&, const NanCallbackHandlers&));
     MOCK_METHOD2(nanDisableRequest,
diff --git a/wifi/1.3/default/wifi_legacy_hal.h b/wifi/1.3/default/wifi_legacy_hal.h
index 70a919f..4d6beb3 100644
--- a/wifi/1.3/default/wifi_legacy_hal.h
+++ b/wifi/1.3/default/wifi_legacy_hal.h
@@ -184,9 +184,9 @@
     // Checks if legacy HAL has successfully started
     bool isStarted();
     // Wrappers for all the functions in the legacy HAL function table.
-    std::pair<wifi_error, std::string> getDriverVersion(
+    virtual std::pair<wifi_error, std::string> getDriverVersion(
         const std::string& iface_name);
-    std::pair<wifi_error, std::string> getFirmwareVersion(
+    virtual std::pair<wifi_error, std::string> getFirmwareVersion(
         const std::string& iface_name);
     std::pair<wifi_error, std::vector<uint8_t>> requestDriverMemoryDump(
         const std::string& iface_name);