diff --git a/neuralnetworks/1.1/vts/functional/VtsHalNeuralnetworks.h b/neuralnetworks/1.1/vts/functional/VtsHalNeuralnetworks.h
index 0050e52..a64268f 100644
--- a/neuralnetworks/1.1/vts/functional/VtsHalNeuralnetworks.h
+++ b/neuralnetworks/1.1/vts/functional/VtsHalNeuralnetworks.h
@@ -34,9 +34,11 @@
 namespace neuralnetworks {
 namespace V1_1 {
 
-using V1_0::Request;
 using V1_0::DeviceStatus;
 using V1_0::ErrorStatus;
+using V1_0::Operand;
+using V1_0::OperandType;
+using V1_0::Request;
 
 namespace vts {
 namespace functional {
diff --git a/neuralnetworks/1.2/Android.bp b/neuralnetworks/1.2/Android.bp
index d5ef49d..5a661e0 100644
--- a/neuralnetworks/1.2/Android.bp
+++ b/neuralnetworks/1.2/Android.bp
@@ -17,6 +17,8 @@
     ],
     types: [
         "Model",
+        "Operand",
+        "OperandType",
         "Operation",
         "OperationType",
     ],
diff --git a/neuralnetworks/1.2/IDevice.hal b/neuralnetworks/1.2/IDevice.hal
index 9cc23a2..aff4cf3 100644
--- a/neuralnetworks/1.2/IDevice.hal
+++ b/neuralnetworks/1.2/IDevice.hal
@@ -26,6 +26,36 @@
  */
 interface IDevice extends @1.1::IDevice {
     /**
+     * Get the version string of the driver implementation.
+     *
+     * The version string must be a unique token among the set of version strings of
+     * drivers of a specific device. The token identifies the device driver's
+     * implementation. The token must not be confused with the feature level which is solely
+     * defined by the interface version. This API is opaque to the Android framework, but the
+     * Android framework may use the information for debugging or to pass on to NNAPI applications.
+     *
+     * Application developers sometimes have specific requirements to ensure good user experiences,
+     * and they need more information to make intelligent decisions when the Android framework cannot.
+     * For example, combined with the device name and other information, the token can help
+     * NNAPI applications filter devices based on their needs:
+     *     - An application demands a certain level of performance, but a specific version of
+     *       the driver cannot meet that requirement because of a performance regression.
+     *       The application can blacklist the driver based on the version provided.
+     *     - An application has a minimum precision requirement, but certain versions of
+     *       the driver cannot meet that requirement because of bugs or certain optimizations.
+     *       The application can filter out versions of these drivers.
+     *
+     * @return status Error status returned from querying the version string. Must be:
+     *     - NONE if the query was successful
+     *     - DEVICE_UNAVAILABLE if driver is offline or busy
+     *     - GENERAL_FAILURE if the query resulted in an
+     *       unspecified error
+     * @return version The version string of the device implementation.
+     *     Must have nonzero length
+     */
+    getVersionString() generates (ErrorStatus status, string version);
+
+    /**
      * Gets the supported operations in a model.
      *
      * getSupportedOperations indicates which operations of a model are fully
diff --git a/neuralnetworks/1.2/types.hal b/neuralnetworks/1.2/types.hal
index bed1d5c..4a1e7a8 100644
--- a/neuralnetworks/1.2/types.hal
+++ b/neuralnetworks/1.2/types.hal
@@ -16,10 +16,36 @@
 
 package android.hardware.neuralnetworks@1.2;
 
-import @1.0::Operand;
+import @1.0::DataLocation;
+import @1.0::OperandLifeTime;
+import @1.0::OperandType;
 import @1.0::PerformanceInfo;
 import @1.1::OperationType;
 
+enum OperandType : @1.0::OperandType {
+    /**
+     * An 8 bit boolean scalar value.
+     *
+     * Values of this operand type are either true or false. A zero value
+     * represents false; any other value represents true.
+     */
+    BOOL = 6,
+    /**
+     * A tensor of 16 bit signed integers that represent real numbers.
+     *
+     * Attached to this tensor are two numbers that are used to convert the 16
+     * bit integer to the real value and vice versa. These two numbers are:
+     * - scale: a 32 bit floating point value greater than zero.
+     * - zeroPoint: a 32 bit integer, in range [-32768, 32767].
+     *
+     * The formula is:
+     * realValue = (integerValue - zeroPoint) * scale.
+     */
+    TENSOR_QUANT16_ASYMM = 7,
+    /** A tensor of 16 bit floating point values. */
+    TENSOR_FLOAT16 = 8,
+};
+
 /**
  * Operation types.
  *
@@ -102,6 +128,101 @@
 };
 
 /**
+ * Describes one operand of the model's graph.
+ */
+struct Operand {
+    /**
+     * Data type of the operand.
+     */
+    OperandType type;
+
+    /**
+     * Dimensions of the operand.
+     *
+     * For a scalar operand, dimensions.size() must be 0.
+     *
+     * For a tensor operand, dimensions.size() must be at least 1;
+     * however, any of the dimensions may be unspecified.
+     *
+     * A tensor operand with all dimensions specified has "fully
+     * specified" dimensions. Whenever possible (i.e., whenever the
+     * dimensions are known at model construction time), a tensor
+     * operand should have (but is not required to have) fully
+     * specified dimensions, in order to enable the best possible
+     * performance.
+     *
+     * If a tensor operand's dimensions are not fully specified, the
+     * dimensions of the operand are deduced from the operand
+     * dimensions and values of the operation for which that operand
+     * is an output.
+     *
+     * In the following situations, a tensor operand's dimensions must
+     * be fully specified:
+     *
+     *     . The operand has lifetime CONSTANT_COPY or
+     *       CONSTANT_REFERENCE.
+     *
+     *     . The operand has lifetime MODEL_INPUT or MODEL_OUTPUT. Fully
+     *       specified dimensions must either be present in the
+     *       Operand or they must be provided in the corresponding
+     *       RequestArgument.
+     *       EXCEPTION: If the input or output is optional and omitted
+     *       (by setting the hasNoValue field of the corresponding
+     *       RequestArgument to true) then it need not have fully
+     *       specified dimensions.
+     *
+     * A tensor operand with some number of unspecified dimensions is
+     * represented by setting each unspecified dimension to 0.
+     */
+    vec<uint32_t> dimensions;
+
+    /**
+     * The number of times this operand appears as an operation input.
+     *
+     * (For example, if this operand appears once in one operation's
+     * input list, and three times in another operation's input list,
+     * then numberOfConsumers = 4.)
+     */
+    uint32_t numberOfConsumers;
+
+    /**
+     * Quantized scale of the operand.
+     *
+     * Only applicable if the operand is of type TENSOR_QUANT8_ASYMM or
+     * TENSOR_INT32.
+     */
+    float scale;
+
+    /**
+     * Quantized zero-point offset of the operand.
+     *
+     * Only applicable if the operand is of type TENSOR_QUANT8_ASYMM.
+     */
+    int32_t zeroPoint;
+
+    /**
+     * How the operand is used.
+     */
+    OperandLifeTime lifetime;
+
+    /**
+     * Where to find the data for this operand.
+     * If the lifetime is TEMPORARY_VARIABLE, MODEL_INPUT, MODEL_OUTPUT, or
+     * NO_VALUE:
+     * - All the fields must be 0.
+     * If the lifetime is CONSTANT_COPY:
+     * - location.poolIndex is 0.
+     * - location.offset is the offset in bytes into Model.operandValues.
+     * - location.length is set.
+     * If the lifetime is CONSTANT_REFERENCE:
+     * - location.poolIndex is set.
+     * - location.offset is the offset in bytes into the specified pool.
+     * - location.length is set.
+     */
+    DataLocation location;
+};
+
+/**
  * A Neural Network Model.
  *
  * This includes not only the execution graph, but also constant data such as
diff --git a/neuralnetworks/1.2/vts/functional/BasicTests.cpp b/neuralnetworks/1.2/vts/functional/BasicTests.cpp
index d2dea1d..eb3ebd3 100644
--- a/neuralnetworks/1.2/vts/functional/BasicTests.cpp
+++ b/neuralnetworks/1.2/vts/functional/BasicTests.cpp
@@ -37,6 +37,14 @@
     EXPECT_EQ(DeviceStatus::AVAILABLE, static_cast<DeviceStatus>(status));
 }
 
+// device version test
+TEST_F(NeuralnetworksHidlTest, GetDeviceVersionStringTest) {
+    Return<void> ret = device->getVersionString([](ErrorStatus status, const hidl_string& version) {
+        EXPECT_EQ(ErrorStatus::NONE, status);
+        EXPECT_LT(0, version.size());
+    });
+    EXPECT_TRUE(ret.isOk());
+}
 }  // namespace functional
 }  // namespace vts
 }  // namespace V1_2
diff --git a/neuralnetworks/1.2/vts/functional/ValidateModel.cpp b/neuralnetworks/1.2/vts/functional/ValidateModel.cpp
index 7ec6ff1..b840199 100644
--- a/neuralnetworks/1.2/vts/functional/ValidateModel.cpp
+++ b/neuralnetworks/1.2/vts/functional/ValidateModel.cpp
@@ -26,9 +26,7 @@
 namespace V1_2 {
 
 using V1_0::IPreparedModel;
-using V1_0::Operand;
 using V1_0::OperandLifeTime;
-using V1_0::OperandType;
 using V1_1::ExecutionPreference;
 
 namespace vts {
@@ -131,10 +129,10 @@
 ///////////////////////// VALIDATE MODEL OPERAND TYPE /////////////////////////
 
 static const int32_t invalidOperandTypes[] = {
-    static_cast<int32_t>(OperandType::FLOAT32) - 1,              // lower bound fundamental
-    static_cast<int32_t>(OperandType::TENSOR_QUANT8_ASYMM) + 1,  // upper bound fundamental
-    static_cast<int32_t>(OperandType::OEM) - 1,                  // lower bound OEM
-    static_cast<int32_t>(OperandType::TENSOR_OEM_BYTE) + 1,      // upper bound OEM
+    static_cast<int32_t>(OperandType::FLOAT32) - 1,               // lower bound fundamental
+    static_cast<int32_t>(OperandType::TENSOR_QUANT16_ASYMM) + 1,  // upper bound fundamental
+    static_cast<int32_t>(OperandType::OEM) - 1,                   // lower bound OEM
+    static_cast<int32_t>(OperandType::TENSOR_OEM_BYTE) + 1,       // upper bound OEM
 };
 
 static void mutateOperandTypeTest(const sp<IDevice>& device, const Model& model) {
@@ -157,10 +155,13 @@
         case OperandType::FLOAT32:
         case OperandType::INT32:
         case OperandType::UINT32:
+        case OperandType::BOOL:
             return 1;
+        case OperandType::TENSOR_FLOAT16:
         case OperandType::TENSOR_FLOAT32:
         case OperandType::TENSOR_INT32:
         case OperandType::TENSOR_QUANT8_ASYMM:
+        case OperandType::TENSOR_QUANT16_ASYMM:
             return 0;
         default:
             return 0;
@@ -185,11 +186,14 @@
         case OperandType::FLOAT32:
         case OperandType::INT32:
         case OperandType::UINT32:
+        case OperandType::BOOL:
+        case OperandType::TENSOR_FLOAT16:
         case OperandType::TENSOR_FLOAT32:
             return 1.0f;
         case OperandType::TENSOR_INT32:
             return -1.0f;
         case OperandType::TENSOR_QUANT8_ASYMM:
+        case OperandType::TENSOR_QUANT16_ASYMM:
             return 0.0f;
         default:
             return 0.0f;
@@ -214,10 +218,13 @@
         case OperandType::FLOAT32:
         case OperandType::INT32:
         case OperandType::UINT32:
+        case OperandType::BOOL:
+        case OperandType::TENSOR_FLOAT16:
         case OperandType::TENSOR_FLOAT32:
         case OperandType::TENSOR_INT32:
             return {1};
         case OperandType::TENSOR_QUANT8_ASYMM:
+        case OperandType::TENSOR_QUANT16_ASYMM:
             return {-1, 256};
         default:
             return {};
@@ -253,10 +260,12 @@
         case OperandType::FLOAT32:
         case OperandType::INT32:
         case OperandType::UINT32:
+        case OperandType::BOOL:
             newOperand.dimensions = hidl_vec<uint32_t>();
             newOperand.scale = 0.0f;
             newOperand.zeroPoint = 0;
             break;
+        case OperandType::TENSOR_FLOAT16:
         case OperandType::TENSOR_FLOAT32:
             newOperand.dimensions =
                 operand->dimensions.size() > 0 ? operand->dimensions : hidl_vec<uint32_t>({1});
@@ -269,6 +278,7 @@
             newOperand.zeroPoint = 0;
             break;
         case OperandType::TENSOR_QUANT8_ASYMM:
+        case OperandType::TENSOR_QUANT16_ASYMM:
             newOperand.dimensions =
                 operand->dimensions.size() > 0 ? operand->dimensions : hidl_vec<uint32_t>({1});
             newOperand.scale = operand->scale != 0.0f ? operand->scale : 1.0f;