diff --git a/current.txt b/current.txt
index 83479f3..fe76327 100644
--- a/current.txt
+++ b/current.txt
@@ -238,15 +238,16 @@
 619600109232ed64b827c8a11beed8070b1827ae464547d7aa146cf0473b4bca android.hardware.cas.native@1.0::IDescrambler
 0a159f81359cd4f71bbe00972ee8403ea79351fb7c0cd48be72ebb3e424dbaef android.hardware.radio@1.0::types
 09342041e17c429fce0034b9096d17849122111436a5f0053e7e59500e1cb89c android.hardware.media.omx@1.0::IOmxStore
-246a56d37d57a47224562c9d077b4a2886ce6242b9311bd98a17325944c280d7 android.hardware.neuralnetworks@1.0::types
 93eb3757ceaf21590fa4cd1d4a7dfe3b3794af5396100a6d25630879352abce9 android.hardware.neuralnetworks@1.0::IDevice
 f66f9a38541bf92001d3adcce678cd7e3da2262124befb460b1c9aea9492813b android.hardware.neuralnetworks@1.0::IExecutionCallback
 953607822954435874f4b81686440a604e2a88cdd2d9164c6293f3d5772510d7 android.hardware.neuralnetworks@1.0::IPreparedModel
 73e03573494ba96f0e711ab7f1956c5b2d54c3da690cd7ecf4d6d0f287447730 android.hardware.neuralnetworks@1.0::IPreparedModelCallback
+246a56d37d57a47224562c9d077b4a2886ce6242b9311bd98a17325944c280d7 android.hardware.neuralnetworks@1.0::types
 f4945e397b5dea41bb64518dfde59be71245d8a125fd1e0acffeb57ac7b08fed android.hardware.thermal@1.1::IThermal
 c8bc853546dd55584611def2a9fa1d99f657e3366c976d2f60fe6b8aa6d2cb87 android.hardware.thermal@1.1::IThermalCallback
 
 # Future changes to HALs
 5804ca86611d72e5481f022b3a0c1b334217f2e4988dad25730c42af2d1f4d1c android.hardware.neuralnetworks@1.0::IDevice
-088b30a9c9ce27bc955b08a03c38c208f8f65b51133053c7656c875479801b99 android.hardware.neuralnetworks@1.0::types
+12e8dca4ab7d8aadd0ef8f1b438021938e2396139e85db2ed65783b08800aa52 android.hardware.neuralnetworks@1.0::IExecutionCallback
+702f9a4cd3b7486a4b04f7155b737757ac2ca4b3548976d5782ad3cae9ff9780 android.hardware.neuralnetworks@1.0::types
 
diff --git a/neuralnetworks/1.0/IExecutionCallback.hal b/neuralnetworks/1.0/IExecutionCallback.hal
index ef0f454..9c06166 100644
--- a/neuralnetworks/1.0/IExecutionCallback.hal
+++ b/neuralnetworks/1.0/IExecutionCallback.hal
@@ -28,7 +28,7 @@
      * ErrorStatus resulting from the execution. If the asynchronous task
      * is not launched, notify must be invoked with the appropriate error.
      *
-     * @return param Error status returned from launching the asynchronous task
+     * @param status Error status returned from launching the asynchronous task
      *               (if the launch fails) or from the asynchronous task itself
      *               (if the launch succeeds). Must be:
      *               - NONE if the asynchronous execution was successful
diff --git a/neuralnetworks/1.0/types.hal b/neuralnetworks/1.0/types.hal
index 12461e9..8c07fcc 100644
--- a/neuralnetworks/1.0/types.hal
+++ b/neuralnetworks/1.0/types.hal
@@ -24,38 +24,40 @@
  * Types prefaced with TENSOR_* must be used for tensor data (i.e., tensors
  * with at least one dimension). Types not prefaced by TENSOR_* represent
  * scalar values and must have no dimensions.
+ *
+ * Although many types are defined, most operators accept just a few
+ * types. Most used are {@link OperandType::TENSOR_FLOAT32},
+ * {@link OperandType::TENSOR_QUANT8_ASYMM},
+ * and {@link OperandType::INT32}.
  */
 enum OperandType : int32_t {
-    /**
-     * The following entries are used to declare scalars.
-     */
+    /** A 32 bit floating point scalar value. */
     FLOAT32             = 0,
+    /** A signed 32 bit integer scalar value. */
     INT32               = 1,
+    /** An unsigned 32 bit integer scalar value. */
     UINT32              = 2,
 
-    /**
-     * The following entries are used to declare tensors.
-     */
+    /** A tensor of 32 bit floating point values. */
     TENSOR_FLOAT32      = 3,
+    /** A tensor of 32 bit integer values. */
     TENSOR_INT32        = 4,
-
-    /**
-     * A tensor of 8 bit integers that represent real numbers.
+    /** A tensor of 8 bit integers that represent real numbers.
      *
      * Attached to this tensor are two numbers that can be used to convert the
      * 8 bit integer to the real value and vice versa. These two numbers are:
-     * - scale: a 32 bit floating point value
-     * - zero_value: a 32 bit integer
+     * - scale: a 32 bit floating point value greater than zero.
+     * - zeroPoint: a 32 bit integer, in range [0, 255].
      *
      * The formula is:
-     * real_value = (integer_value - zero_value) * scale.
+     * real_value = (integer_value - zeroPoint) * scale.
      */
     TENSOR_QUANT8_ASYMM = 5,
 
-    /**
-     * The following entries are OEM specific operand types.
-     */
+    /** OEM specific scalar value. */
     OEM                 = 10000,
+
+    /** A tensor of OEM specific values. */
     TENSOR_OEM_BYTE     = 10001,
 };
 
@@ -66,9 +68,9 @@
  */
 enum OperationType : int32_t {
     /**
-     * Adds two tensors, elment-wise.
+     * Adds two tensors, element-wise.
      *
-     * Takes two input tensors of identical type and compatible dimensions.  The output
+     * Takes two input tensors of identical type and compatible dimensions. The output
      * is the sum of both input tensors, optionally modified by an activation function.
      *
      * Two dimensions are compatible when:
@@ -79,21 +81,25 @@
      * It starts with the trailing dimensions, and works its way forward.
      *
      * Example:
-     *     input1.dimension =    {4, 1, 2}
+     *
+     *     input1.dimension = {4, 1, 2}
      *     input2.dimension = {5, 4, 3, 1}
      *     output.dimension = {5, 4, 3, 2}
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
      * Supported tensor rank: up to 4
      *
      * Inputs:
-     * 0: A tensor.
-     * 1: A tensor of the same type, and compatible dimensions as input0.
-     * 2: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
-     *    Specifies the activation to invoke on the result of each addition.
+     * * 0: A tensor.
+     * * 1: A tensor of the same type, and compatible dimensions as input0.
+     * * 2: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+     *      Specifies the activation to invoke on the result of each addition.
      *
-     * Ouputs:
-     * 0: The sum, a tensor of the same type as input0.
+     * Outputs:
+     * * 0: The sum, a tensor of the same type as input0.
      */
     ADD = 0,
 
@@ -102,29 +108,50 @@
      *
      * The output dimensions are functions of the filter dimensions, stride, and padding.
      *
-     * The values in output Tensor is computed as:
+     * The values in the output tensor are computed as:
+     *
      *     output[batch, row, col, channel] =
      *         sum_{i, j}(input[batch, row + i, col + j, channel]) / sum(1)
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
-     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
-     * Supported tensor rank: 4, with "NHWC" data layout.
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
      *
-     * Inputs:
-     * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
-     * 1: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
-     * 2: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
-     * 3: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
-     * 4: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
-     * 5: An INT32 value, specifying the output stride in the ‘width’ dimension.
-     * 6: An INT32 value, specifying the output stride in the ‘height’ dimension.
-     * 7: An INT32 value, specifying the filter width.
-     * 8: An INT32 value, specifying the filter height.
-     * 9: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
-     *    Specifies the activation to invoke on the result of each addition.
+     * Supported tensor rank: 4, with "NHWC" (i.e., Num_samples, Height, Width, and Channels)
+     * data layout.
      *
-     * Ouputs:
-     * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
+     * Both explicit padding and implicit padding are supported.
+     *
+     * Inputs (explicit padding):
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
+     * * 1: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
+     * * 2: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
+     * * 3: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
+     * * 4: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
+     * * 5: An INT32 value, specifying the stride when walking through input
+     *      in the ‘width’ dimension.
+     * * 6: An INT32 value, specifying the stride when walking through input
+     *      in the ‘height’ dimension.
+     * * 7: An INT32 value, specifying the filter width.
+     * * 8: An INT32 value, specifying the filter height.
+     * * 9: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+     *      Specifies the activation to invoke on the result of each addition.
+     *
+     * Inputs (implicit padding):
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
+     * * 1: An INT32 value, specifying the implicit padding scheme, has to be one of the
+     *      following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
+     * * 2: An INT32 value, specifying the stride when walking through input
+     *      in the ‘width’ dimension.
+     * * 3: An INT32 value, specifying the stride when walking through input
+     *      in the ‘height’ dimension.
+     * * 4: An INT32 value, specifying the filter width.
+     * * 5: An INT32 value, specifying the filter height.
+     * * 6: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+     *      Specifies the activation to invoke on the result of each addition.
+     *
+     * Outputs:
+     * * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
      */
     AVERAGE_POOL_2D = 1,
 
@@ -134,19 +161,21 @@
      * The input tensors must have identical type and the same dimensions except the
      * dimension along the concatenation axis.
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
-     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
      * Supported tensor rank: up to 4
      *
      * Inputs:
-     * 0 ~ n: The list on n input tensors, of shape [D0, D1, ..., Daxis(i), ..., Dm]
-     * n+1: An INT32 value, specifying the concatenation axis.
-     * n+2: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
-     *    Specifies the activation to invoke on the result of each addition.
+     * * 0 ~ n-1: The list of n input tensors, of shape [D0, D1, ..., Daxis(i), ..., Dm].
+     *            For inputs of {@link OperandType::TENSOR_QUANT8_ASYMM} type, all
+     *            input tensors must have the same scale and zeroPoint.
+     * * n: An INT32 value, specifying the concatenation axis.
      *
-     * Ouputs:
-     * 0: The output, a tensor of the same type as the input tensors.
-          The output shape is [D0, D1, ..., sum(Daxis(i)), ..., Dm].
+     * Outputs:
+     * * 0: The output, a tensor of the same type as the input tensors.
+     *      The output shape is [D0, D1, ..., sum(Daxis(i)), ..., Dm].
      */
     CONCATENATION = 2,
 
@@ -158,7 +187,8 @@
      *
      * The output dimensions are functions of the filter dimensions, stride, and padding.
      *
-     * The values in output Tensor is computed as:
+     * The values in the output tensor are computed as:
+     *
      *     output[batch, row, col, channel] =
      *         sum_{i, j} (
      *             input[batch, row + i, col + j, k] *
@@ -166,77 +196,135 @@
      *             bias[channel]
      *         )
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
-     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
      * Supported tensor rank: 4, with "NHWC" data layout.
      *
-     * Inputs:
-     * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
-     * 1: A 4-D tensor, of shape [depth_out, filter_height, filter_width, depth_in],
-     *    specifying the filter.
-     * 2: A 1-D tensor, of shape [depth_out], specifying the bias.
-     *    For input tensor of {@link OperandType::TENSOR_FLOAT32} type, the bias should
-     *    also be of {@link OperandType::TENSOR_FLOAT32}.
-     *    For input tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the bias
-     *    should be of {@link OperandType::TENSOR_INT32}.
-     * 3: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
-     * 4: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
-     * 5: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
-     * 6: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
-     * 7: An INT32 value, specifying the output stride in the ‘width’ dimension.
-     * 8: An INT32 value, specifying the output stride in the ‘height’ dimension.
-     * 9: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
-     *    Specifies the activation to invoke on the result of each addition.
+     * Both explicit padding and implicit padding are supported.
      *
-     * Ouputs:
-     * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth_out].
+     * Inputs (explicit padding):
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
+     * * 1: A 4-D tensor, of shape [depth_out, filter_height, filter_width, depth_in],
+     *      specifying the filter.
+     * * 2: A 1-D tensor, of shape [depth_out], specifying the bias.
+     *      For input tensor of {@link OperandType::TENSOR_FLOAT32} type, the bias should
+     *      also be of {@link OperandType::TENSOR_FLOAT32}.
+     *      For input tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the bias
+     *      should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0 and
+     *      bias_scale == input_scale * filter_scale.
+     * * 3: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
+     * * 4: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
+     * * 5: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
+     * * 6: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
+     * * 7: An INT32 value, specifying the stride when walking through input
+     *      in the ‘width’ dimension.
+     * * 8: An INT32 value, specifying the stride when walking through input
+     *      in the ‘height’ dimension.
+     * * 9: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+     *      Specifies the activation to invoke on the result of each addition.
+     *
+     * Inputs (implicit padding):
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
+     * * 1: A 4-D tensor, of shape [depth_out, filter_height, filter_width, depth_in],
+     *      specifying the filter.
+     * * 2: A 1-D tensor, of shape [depth_out], specifying the bias.
+     *      For input tensor of {@link OperandType::TENSOR_FLOAT32} type, the bias should
+     *      also be of {@link OperandType::TENSOR_FLOAT32}.
+     *      For input tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the bias
+     *      should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0 and
+     *      bias_scale == input_scale * filter_scale.
+     * * 3: An INT32 value, specifying the implicit padding scheme, has to be one of the
+     *      following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
+     * * 4: An INT32 value, specifying the stride when walking through input
+     *      in the ‘width’ dimension.
+     * * 5: An INT32 value, specifying the stride when walking through input
+     *      in the ‘height’ dimension.
+     * * 6: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+     *      Specifies the activation to invoke on the result of each addition.
+     *
+     * 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} type, the following
+     *      condition must be satisfied: output_scale > input_scale * filter_scale.
      */
     CONV_2D = 3,
 
     /**
-     * Performs an depthwise 2-D convolution operation.
+     * Performs a depthwise 2-D convolution operation.
      *
      * Given an input tensor of shape [batches, height, width, depth_in] and a filter
-     * tensor of shape [depth_out, filter_height, filter_width, depth_in] containing
-     * in_channels convolutional filters of depth 1, DEPTHWISE_CONV applies a different
+     * tensor of shape [1, filter_height, filter_width, depth_out] containing
+     * depth_out convolutional filters of depth 1, DEPTHWISE_CONV applies a different
      * filter to each input channel (expanding from 1 channel to channel_multiplier channels
      * for each), then concatenates the results together.
      *
      * The output has depth_out = depth_in * depth_multiplier channels.
      * The output dimensions are functions of the filter dimensions, stride, and padding.
      *
-     * The values in output Tensor is computed as:
+     * The values in the output tensor are computed as:
+     *
      *     output[b, i, j, k * channel_multiplier + q] =
      *         sum_{di, dj} (
      *             input[b, strides[1] * i + di, strides[2] * j + dj, k] *
-     *             filter[di, dj, k, q]
+     *             filter[1, di, dj, k * channel_multiplier + q]
      *         )
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
-     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
      * Supported tensor rank: 4, with "NHWC" data layout.
      *
-     * Inputs:
-     * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
-     * 1: A 4-D tensor, of shape [1, filter_height, filter_width, depth_out],
-     *    specifying the filter.
-     * 2: A 1-D tensor, of shape [depth_out], specifying the bias.
-     *    For input tensor of {@link OperandType::TENSOR_FLOAT32} type, the bias should
-     *    also be of {@link OperandType::TENSOR_FLOAT32}.
-     *    For input tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the bias
-     *    should be of {@link OperandType::TENSOR_INT32}.
-     * 3: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
-     * 4: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
-     * 5: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
-     * 6: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
-     * 7: An INT32 value, specifying the output stride in the ‘width’ dimension.
-     * 8: An INT32 value, specifying the output stride in the ‘height’ dimension.
-     * 9: An INT32 value, specifying the depthwise multiplier.
-     * 10: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
-     *    Specifies the activation to invoke on the result of each addition.
+     * Both explicit padding and implicit padding are supported.
      *
-     * Ouputs:
-     * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth_out].
+     * Inputs (explicit padding):
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
+     * * 1: A 4-D tensor, of shape [1, filter_height, filter_width, depth_out],
+     *      specifying the filter.
+     * * 2: A 1-D tensor, of shape [depth_out], specifying the bias.
+     *      For input tensor of {@link OperandType::TENSOR_FLOAT32} type, the bias should
+     *      also be of {@link OperandType::TENSOR_FLOAT32}.
+     *      For input tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the bias
+     *      should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0 and
+     *      bias_scale == input_scale * filter_scale.
+     * * 3: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
+     * * 4: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
+     * * 5: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
+     * * 6: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
+     * * 7: An INT32 value, specifying the stride when walking through input
+     *      in the ‘width’ dimension.
+     * * 8: An INT32 value, specifying the stride when walking through input
+     *      in the ‘height’ dimension.
+     * * 9: An INT32 value, specifying the depthwise multiplier.
+     * * 10: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+     *       Specifies the activation to invoke on the result of each addition.
+     *
+     * Inputs (implicit padding):
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
+     * * 1: A 4-D tensor, of shape [1, filter_height, filter_width, depth_out],
+     *      specifying the filter.
+     * * 2: A 1-D tensor, of shape [depth_out], specifying the bias.
+     *      For input tensor of {@link OperandType::TENSOR_FLOAT32} type, the bias should
+     *      also be of {@link OperandType::TENSOR_FLOAT32}.
+     *      For input tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the bias
+     *      should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0 and
+     *      bias_scale == input_scale * filter_scale.
+     * * 3: An INT32 value, specifying the implicit padding scheme, has to be one of the
+     *      following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
+     * * 4: An INT32 value, specifying the stride when walking through input
+     *      in the ‘width’ dimension.
+     * * 5: An INT32 value, specifying the stride when walking through input
+     *      in the ‘height’ dimension.
+     * * 6: An INT32 value, specifying the depthwise multiplier.
+     * * 7: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+     *       Specifies the activation to invoke on the result of each addition.
+     *
+     * 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} type, the following
+     *      condition must be satisfied: output_scale > input_scale * filter_scale.
      */
     DEPTHWISE_CONV_2D = 4,
 
@@ -254,18 +342,20 @@
      * input_height * block_size.
      * The depth of the input tensor must be divisible by block_size * block_size
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
-     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
      * Supported tensor rank: 4, with "NHWC" data layout.
      *
      * Inputs:
-     * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
-     * 1: An INT32 value, specifying the block_size. block_size must be >=1 and
-     *    block_size * block_size must be a divisor of the input depth.
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
+     * * 1: An INT32 value, specifying the block_size. block_size must be >=1 and
+     *      block_size * block_size must be a divisor of the input depth.
      *
-     * Ouputs:
-     * 0: The output 4-D tensor, of shape [batch, height*block_size, width*block_size,
-     *    depth/(block_size*block_size)].
+     * Outputs:
+     * * 0: The output 4-D tensor, of shape [batch, height*block_size, width*block_size,
+     *      depth/(block_size*block_size)].
      */
     DEPTH_TO_SPACE = 5,
 
@@ -273,53 +363,69 @@
      * Dequantizes the input tensor.
      *
      * The formula is:
-     *     output = (input - zero_value) * scale.
      *
-     * Supported tensor types: {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *     output = (input - zeroPoint) * scale.
+     *
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
      * Supported tensor rank: up to 4
      *
      * Inputs:
-     * 0: A tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}.
+     * * 0: A tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}.
      *
-     * Ouputs:
-     * 0: The output tensor of same shape as input0, but with type
-          {@link OperandType::TENSOR_FLOAT32}.
+     * Outputs:
+     * * 0: The output tensor of same shape as input0, but with type
+     *      {@link OperandType::TENSOR_FLOAT32}.
      */
     DEQUANTIZE = 6,
 
     /**
-     * Looks up items from a given tensor.
+     * Looks up sub-tensors in the input tensor.
      *
-     * Each item in the output is a raw copy of the corresponding item in
-     * the input “values”. If the the given “lookup” indices are out of bounds,
-     * the op will fail and an error will be reported.
+     * This operator takes for input a tensor of values (Values) and
+     * a one-dimensional tensor of selection indices (Lookups).
+     * The output tensor is the concatenation of sub-tensors of Values as
+     * selected by Lookups.
+     *
+     * Think of Values as being sliced along its first dimension:
+     * The entries in Lookups select which slices are concatenated together
+     * to create the output tensor.
+     *
+     * For example, if Values has shape of [40, 200, 300] and
+     * Lookups has shape of [3], all three values found in Lookups are
+     * expected to be between 0 and 39. The resulting tensor must
+     * have shape of [3, 200, 300].
+     *
+     * If a value in Lookups is out of bounds, the operation must fail
+     * and an error must be reported.
      *
      * Inputs:
-     * * 0: Values. An n-D tensor of any type X (where n >= 2). E.g., if n is 2,
-     *      then the shape would be [lookup_dimension, values_dimension], where
-     *      “lookup_dimension” corresponds to the indexing dimension in the lookup
-     *      table, and “values_dimension” to the contents.
-     * * 1: Lookups. An 1-D tensor of type T, of shape [lookup_size], where
-     *      “lookup_size” is the number of elements to look for, and each entry
-     *      corresponds to the first dimension of the “values” tensor.
+     * * 0: Lookups. A 1-D tensor of {@link OperandType::TENSOR_INT32} type.
+     *      The values are indices into the first dimension of Values.
+     * * 1: Values. An n-D tensor, where n >= 2, from which sub-tensors are
+     *      extracted.
      *
      * Output:
-     * * 0: A n-D tensor of type X and the same rank and shape as the “values”
-     *      tensor, except for the first dimension which has size “lookup_size”.
+     * * 0: A n-D tensor with the same rank and shape as the Values
+     *      tensor, except for the first dimension which has the same size
+     *      as Lookups' only dimension.
      */
     EMBEDDING_LOOKUP = 7,
 
     /**
      * Computes element-wise floor() on the input tensor.
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     *
      * Supported tensor rank: up to 4
      *
      * Inputs:
-     * 0: A tensor.
+     * * 0: A tensor.
      *
-     * Ouputs:
-     * 0: The output, a tensor of the same type and dimensions as input0.
+     * Outputs:
+     * * 0: The output tensor, of the same type and dimensions as the input tensor.
      */
     FLOOR = 8,
 
@@ -328,66 +434,104 @@
      * tensor with each element in the output tensor.
      *
      * This layer implements the operation:
+     *
      *     outputs = activation(inputs * weights’ + bias)
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
-     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
      * Supported tensor rank: up to 4.
      *
      * Inputs:
-     * 0: A tensor, specifying the input. If rank is greater than 2, then it gets flattened to
-     *    a 2-D Tensor. The 2-D Tensor is handled as if dimensions corresponded to shape
-     *    [batch_size, input_size], where “batch_size” corresponds to the batching dimension,
-     *    and “input_size” is the size of the input.
-     * 1: A 2-D tensor, specifying the weights, of shape [num_units, input_size], where “num_units”
-     *    corresponds to the number of output nodes.
-     * 2: A 1-D tensor, of shape [num_units], specifying the bias.
-     *    For input tensor of {@link OperandType::TENSOR_FLOAT32} type, the bias should
-     *    also be of {@link OperandType::TENSOR_FLOAT32}.
-     *    For input tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the bias
-     *    should be of {@link OperandType::TENSOR_INT32}.
-     * 3: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
-     *    Specifies the activation to invoke on the result of each addition.
+     * * 0: A tensor, specifying the input. If rank is greater than 2, then it gets flattened to
+     *      a 2-D Tensor. The 2-D Tensor is handled as if dimensions corresponded to shape
+     *      [batch_size, input_size], where “batch_size” corresponds to the batching dimension,
+     *      and “input_size” is the size of the input.
+     * * 1: A 2-D tensor, specifying the weights, of shape [num_units, input_size], where
+     *      "num_units" corresponds to the number of output nodes.
+     * * 2: A 1-D tensor, of shape [num_units], specifying the bias.
+     *      For input tensor of {@link OperandType::TENSOR_FLOAT32} type, the bias should
+     *      also be of {@link OperandType::TENSOR_FLOAT32}.
+     *      For input tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the bias
+     *      should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0 and
+     *      bias_scale == input_scale * filter_scale.
+     * * 3: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+     *      Specifies the activation to invoke on the result of each addition.
      *
-     * Ouputs:
-     * 0: The output tensor, of shape [batch_size, num_units].
+     * Outputs:
+     * * 0: The output tensor, of shape [batch_size, num_units].
+     *      For output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the following
+     *      condition must be satisfied: output_scale > input_scale * filter_scale.
      */
     FULLY_CONNECTED = 9,
 
     /**
-     * Looks up values of a hash table with given keys.
+     * Looks up sub-tensors in the input tensor using a key-value map.
+     *
+     * This operator takes for input a tensor of values (Values),
+     * a one-dimensional tensor of selection values (Lookups) and
+     * a one-dimensional tensor that maps these values to Values
+     * indexes. The output tensor is the concatenation of sub-tensors of
+     * Values as selected by Lookups via Keys.
+     *
+     * Think of Values as being sliced along its outer-most dimension.
+     * The output is a concatenation of selected slices, with one slice
+     * for each entry of Lookups. The slice selected is the one at the
+     * same index as the Maps entry that matches the value in Lookups.
+     *
+     * For a hit, the corresponding sub-tensor of Values is included
+     * in the Output tensor. For a miss, the corresponding sub-tensor in
+     * Output must have zero values.
+     *
+     * For example, if Values has shape of [40, 200, 300],
+     * Keys should have a shape of [40]. If Lookups tensor has shape
+     * of [3], three slices are being concatenated, so the resulting tensor
+     * must have the shape of [3, 200, 300]. If the first entry in Lookups
+     * has the value 123456, that value must be located in Keys tensor.
+     * If the sixth entry of Keys contains 123456, the sixth slice of Values
+     * must be selected. If no entry in Keys has 123456, a slice of zeroes
+     * must be concatenated.
      *
      * Inputs:
-     * * 0: Lookups. A 1-D int32 tensor with shape [ k ].
-     * * 1: Keys. A 1-D int32 tensor with shape [ n ], *MUST* be sorted in
-     *      ascending order.
-     * * 2: Values. A tensor with shape [ n … ].
+     * * 0: Lookups. A 1-D {@link OperandType::TENSOR_INT32} tensor with shape [ k ].
+     * * 1: Keys. A 1-D {@link OperandType::TENSOR_INT32} tensor with shape [ n ];
+     *      Keys and Values pair represent a map, i.e., the ith element
+     *      in Keys (Keys[i]) is the key to select the ith sub-tensor
+     *      in Values (Values[i]), where 0 <= i <= n-1.
+     *      Keys tensor *MUST* be sorted in ascending order.
+     * * 2: Values. A tensor with shape of [ n, … ]; i.e., the first dimension must be n.
      *
      * Outputs:
      * * 0: Output. A tensor with shape [ k …].
-     * * 1: Hits. A uint8 tensor with shape [ k ] indicates whether the lookup
-     *      hits or not.
+     * * 1: Hits. A boolean tensor with shape [ k ] indicates whether the lookup
+     *      hits (True) or not (False).
+     *      Stored as {@link OperandType::TENSOR_QUANT8_ASYMM} with offset 0 and scale 1.0f.
+     *      A non-zero byte represents True, a hit. A zero indicates otherwise.
      */
     HASHTABLE_LOOKUP = 10,
 
     /**
-     * Applies L2 normalization along a the depth dimension.
+     * Applies L2 normalization along the depth dimension.
      *
-     * The values in output Tensor is computed as:
+     * The values in the output tensor are computed as:
+     *
      *     output[batch, row, col, channel] =
      *         input[batch, row, col, channel] /
      *         sqrt(sum_{c} pow(input[batch, row, col, c], 2))
      *
-     * For x with more dimensions, independently normalizes each 1-D slice along dimension dim.
+     * For input tensor with more dimensions, independently normalizes each 1-D slice along dimension dim.
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
-     * Supported tensor rank: 4, with "NHWC" data layout.
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     *
+     * Supported tensor rank: 4, with "NHWC" data layout (i.e., Num_samples, Height, Width, and Channels).
      *
      * Inputs:
-     * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth].
      *
-     * Ouputs:
-     * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
+     * Outputs:
+     * * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
      */
     L2_NORMALIZATION = 11,
 
@@ -396,28 +540,48 @@
      *
      * The output dimensions are functions of the filter dimensions, stride, and padding.
      *
-     * The values in output Tensor is computed as:
+     * The values in the output tensor are computed as:
+     *
      *     output[batch, row, col, channel] =
      *         sqrt(sum_{i, j} pow(input[batch, row + i, col + j, channel], 2) / sum(1))
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     *
      * Supported tensor rank: 4, with "NHWC" data layout.
      *
-     * Inputs:
-     * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
-     * 1: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
-     * 2: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
-     * 3: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
-     * 4: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
-     * 5: An INT32 value, specifying the output stride in the ‘width’ dimension.
-     * 6: An INT32 value, specifying the output stride in the ‘height’ dimension.
-     * 7: An INT32 value, specifying the filter width.
-     * 8: An INT32 value, specifying the filter height.
-     * 9: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
-     *    Specifies the activation to invoke on the result of each addition.
+     * Both explicit padding and implicit padding are supported.
      *
-     * Ouputs:
-     * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
+     * Inputs (explicit padding):
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
+     * * 1: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
+     * * 2: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
+     * * 3: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
+     * * 4: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
+     * * 5: An INT32 value, specifying the stride when walking through input
+     *      in the ‘width’ dimension.
+     * * 6: An INT32 value, specifying the stride when walking through input
+     *      in the ‘height’ dimension.
+     * * 7: An INT32 value, specifying the filter width.
+     * * 8: An INT32 value, specifying the filter height.
+     * * 9: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+     *      Specifies the activation to invoke on the result of each addition.
+     *
+     * Inputs (implicit padding):
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
+     * * 1: An INT32 value, specifying the implicit padding scheme, has to be one of the
+     *      following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
+     * * 2: An INT32 value, specifying the stride when walking through input
+     *      in the ‘width’ dimension.
+     * * 3: An INT32 value, specifying the stride when walking through input
+     *      in the ‘height’ dimension.
+     * * 4: An INT32 value, specifying the filter width.
+     * * 5: An INT32 value, specifying the filter height.
+     * * 6: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+     *      Specifies the activation to invoke on the result of each addition.
+     *
+     * Outputs:
+     * * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
      */
     L2_POOL_2D = 12,
 
@@ -428,41 +592,49 @@
      * dimension), and each vector is normalized independently. Within a given vector,
      * each component is divided by the weighted, squared sum of inputs within depth_radius.
      *
-     * In details:
+     * The output is calculated using this formula:
+     *
      *     sqr_sum[a, b, c, d] =
      *         sum(pow(input[a, b, c, d - depth_radius : d + depth_radius + 1], 2)
      *     output = input / pow((bias + alpha * sqr_sum), beta)
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     *
      * Supported tensor rank: 4, with "NHWC" data layout.
      *
      * Inputs:
-     * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
-     * 1: An INT32 value, specifying the radius of the normalization window.
-     * 2: A FLOAT32 value, specifying the bias, must not be zero.
-     * 3: A FLOAT32 value, specifying the scale factor, alpha.
-     * 4: A FLOAT32 value, specifying the exponent, beta.
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
+     * * 1: An INT32 value, specifying the radius of the normalization window.
+     * * 2: A FLOAT32 value, specifying the bias, must not be zero.
+     * * 3: A FLOAT32 value, specifying the scale factor, alpha.
+     * * 4: A FLOAT32 value, specifying the exponent, beta.
      *
-     * Ouputs:
-     * 0: The output tensor of same shape as input0.
+     * Outputs:
+     * * 0: The output tensor of same shape as input0.
      */
     LOCAL_RESPONSE_NORMALIZATION = 13,
 
     /**
      * Computes sigmoid activation on the input tensor element-wise.
      *
-     * In details:
+     * The output is calculated using this formula:
+     *
      *     output = 1 / (1 + exp(-input))
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
-     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
      * Supported tensor rank: up to 4.
      *
      * Inputs:
-     * 0: A tensor, specifying the input.
+     * * 0: A tensor, specifying the input.
      *
-     * Ouputs:
-     * 0: The output tensor of same shape as input0.
+     * Outputs:
+     * * 0: The output tensor of same shape as input0.
+     *      For {@link OperandType::TENSOR_QUANT8_ASYMM} type,
+     *      the scale must be 1.f / 256 and the zeroPoint must be 0.
      */
     LOGISTIC = 14,
 
@@ -501,102 +673,165 @@
     LSH_PROJECTION = 15,
 
     /**
-     * Long short-term memory unit (LSTM) recurrent network layer.
+     * Performs a single time step in a Long Short-Term Memory (LSTM) layer
      *
-     * The default non-peephole implementation is based on:
-     * http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf
+     * The LSTM operation is described by the following equations.
+     *
+     * \f{eqnarray*}{
+     * i_t =& \sigma(W_{xi}x_t+W_{hi}h_{t-1}+W_{ci}C_{t-1}+b_i) & \\
+     * f_t =& \sigma(W_{xf}x_t+W_{hf}h_{t-1}+W_{cf}C_{t-1}+b_f) & \\
+     * C_t =& clip(f_t \odot C_{t-1} + i_t \odot g(W_{xc}x_t+W_{hc}h_{t-1}+b_c),\ t_{cell})& \\
+     * o_t =& \sigma(W_{xo}x_t+W_{ho}h_{t-1}+W_{co}C_t+b_o)& \\
+     *      & clip(W_{proj}(o_t \odot g(C_t))+b_{proj},\ t_{proj}) & if\ there\ is\ a\ projection; \\
+     * h_t =& & \\
+     *      & o_t \odot g(C_t) & otherwise. \\
+     * \f}
+     * Where:
+     * * \f$x_t\f$ is the input,
+     * * \f$i_t\f$ is the input gate,
+     * * \f$f_t\f$ is the forget gate,
+     * * \f$C_t\f$ is the cell state,
+     * * \f$o_t\f$ is the output,
+     * * \f$h_t\f$ is the output state,
+     * * \f$\sigma\f$ is the logistic sigmoid function,
+     * * \f$g\f$ is the cell input and cell output activation function, usually \f$tahn\f$,
+     * * \f$W_{xi}\f$ is the input-to-input weight matrix,
+     * * \f$W_{hi}\f$ is the recurrent to input weight matrix,
+     * * \f$W_{ci}\f$ is the cell-to-input weight matrix,
+     * * \f$b_i\f$ is the input gate bias,
+     * * \f$W_{xf}\f$ is the input-to-forget weight matrix,
+     * * \f$W_{hf}\f$ is the recurrent-to-forget weight matrix,
+     * * \f$W_{cf}\f$ is the cell-to-forget weight matrix,
+     * * \f$b_f\f$ is the forget gate bias,
+     * * \f$W_{xc}\f$ is the input-to-cell weight matrix,
+     * * \f$W_{hc}\f$ is the recurrent-to-cell weight matrix,
+     * * \f$b_c\f$ is the cell bias,
+     * * \f$W_{xo}\f$ is the input-to-output weight matrix,
+     * * \f$W_{ho}\f$ is the recurrent-to-output weight matrix,
+     * * \f$W_{co}\f$ is the cell-to-output weight matrix,
+     * * \f$b_o\f$ is the output gate bias,
+     * * \f$W_{proj}\f$ is the projection weight matrix,
+     * * \f$b_{proj}\f$ is the projection bias,
+     * * \f$t_{cell}\f$ is the threshold for clipping the cell state, and
+     * * \f$t_{proj}\f$ is the threshold for clipping the projected output.
+     * * \f$\odot\f$ is the <a href="https://en.wikipedia.org/wiki/Hadamard_product_(matrices)">
+     *   Hadamard product</a> that takes two matrices and produces another
+     *   matrix, each element of which is the product of the corresponding
+     *   elements of the input matrices.
+     *
+     * The operation has the following independently optional inputs:
+     * * The input-to-input weights (\f$W_{xi}\f$), recurrent-to-input weights (\f$W_{hi}\f$),
+     *   cell-to-input (\f$W_{ci}\f$) weights, and input gate bias (\f$b_i\f$) either all have values,
+     *   or none of them have values (i.e., all set to null). If they have no
+     *   values, coupling of input and forget gates (CIFG) is used, in which case
+     *   the input gate (\f$i_t\f$) is calculated using the following equation instead.
+     *   \f{eqnarray*}{
+     *   i_t = 1 - f_t
+     *   \f}
+     * * The cell-to-input weights (\f$W_{ci}\f$), cell-to-forget weights (\f$W_{cf}\f$), and cell-to-output
+     *   weights (\f$W_{co}\f$) either all have values or none of them have values.
+     *   If they have values, the peephole optimization is used.
+     * * The projection weights (\f$W_{proj}\f$) is required only for the recurrent projection
+     *   layer, and should otherwise have no value.
+     * * The projection bias (\f$b_{proj}\f$) may (but not required to) have a value if the
+     *   recurrent projection layer exists, and should otherwise have no value.
+     *
+     * References:
+     *
+     * The default non-peephole non-CIFG implementation is based on:
+     * http://www.bioinf.jku.at/publications/older/2604.pdf
      * S. Hochreiter and J. Schmidhuber. "Long Short-Term Memory". Neural
      * Computation, 9(8):1735-1780, 1997.
      *
-     * The peephole implementation is based on:
+     * The peephole implementation and projection layer is based on:
      * https://research.google.com/pubs/archive/43905.pdf
      * Hasim Sak, Andrew Senior, and Francoise Beaufays. "Long short-term memory
      * recurrent neural network architectures for large scale acoustic modeling."
      * INTERSPEECH, 2014.
+     * (However, the concept of peephole optimization was introduced in work
+     * prior to this paper.)
      *
      * The coupling of input and forget gate (CIFG) is based on:
      * http://arxiv.org/pdf/1503.04069.pdf
      * Greff et al. "LSTM: A Search Space Odyssey"
      *
-     * The class has the following independently optional inputs:
-     * * If input gate (if CIFG): “input_to_forget_weights”,
-     *   “recurrent_to_input_weights”, “cell_to_input_weights”, “input_gate_bias”.
-     * * If no peephole connections: “cell_to_input_weights”,
-     *   “cell_to_forget_weights”, “cell_to_output_weights”.
-     * * If no projection layer: “projection_weights” and “projection_bias”.
-     * * If no projection bias: “projection_bias”.
-     *
-     * Supported tensor types:
+     * Supported tensor types (type T):
      * * {@link OperandType::TENSOR_FLOAT32}
      *
      * Inputs:
-     * * 0: Input.
+     * * 0: The input (\f$x_t\f$).
      *      A 2-D tensor of type T, of shape [batch_size, input_size], where
      *      “batch_size” corresponds to the batching dimension, and “input_size”
      *      is the size of the input.
-     * * 1: input_to_input_weights.
+     * * 1: The input-to-input weights (\f$W_{xi}\f$). Optional.
      *      A 2-D tensor of type T, of shape [num_units, input_size], where
      *      “num_units” corresponds to the number of cell units.
-     * * 2: input_to_forget_weights.
+     * * 2: The input-to-forget weights (\f$W_{xf}\f$).
      *      A 2-D tensor of type T, of shape [num_units, input_size].
-     * * 3: input_to_cell_weights.
+     * * 3: The input-to-cell weights (\f$W_{xc}\f$).
      *      A 2-D tensor of type T, of shape [num_units, input_size].
-     * * 4: input_to_output_weights.
+     * * 4: The input-to-output weights (\f$W_{xo}\f$).
      *      A 2-D tensor of type T, of shape [num_units, input_size].
-     * * 5: recurrent_to_input_weights.
+     * * 5: The recurrent-to-input weights (\f$W_{hi}\f$). Optional.
      *      A 2-D tensor of type T, of shape [num_units, output_size], where
      *      “output_size” corresponds to either the number of cell units (i.e.,
      *      “num_units”), or the second dimension of the “projection_weights”, if
      *      defined.
-     * * 6: recurrent_to_forget_weights.
+     * * 6: The recurrent-to-forget weights (\f$W_{hf}\f$).
      *      A 2-D tensor of type T, of shape [num_units, output_size].
-     * * 7: recurrent_to_cell_weights.
+     * * 7: The recurrent-to-cell weights (\f$W_{hc}\f$).
      *      A 2-D tensor of type T, of shape [num_units, output_size].
-     * * 8: recurrent_to_output_weights.
+     * * 8: The recurrent-to-output weights (\f$W_{ho}\f$).
      *      A 2-D tensor of type T, of shape [num_units, output_size].
-     * * 9: cell_to_input_weights.
+     * * 9: The cell-to-input weights (\f$W_{ci}\f$). Optional.
      *      A 1-D tensor of type T, of shape [num_units].
-     * * 10:cell_to_forget_weights.
+     * * 10:The cell-to-forget weights (\f$W_{cf}\f$). Optional.
      *      A 1-D tensor of type T, of shape [num_units].
-     * * 11:cell_to_output_weights.
+     * * 11:The cell-to-output weights (\f$W_{co}\f$). Optional.
      *      A 1-D tensor of type T, of shape [num_units].
-     * * 12:input_gate_bias.
+     * * 12:The input gate bias (\f$b_i\f$). Optional.
      *      A 1-D tensor of type T, of shape [num_units].
-     * * 13:forget_gate_bias.
+     * * 13:The forget gate bias (\f$b_f\f$).
      *      A 1-D tensor of type T, of shape [num_units].
-     * * 14:cell_bias.
+     * * 14:The cell bias (\f$b_c\f$).
      *      A 1-D tensor of type T, of shape [num_units].
-     * * 15:output_gate_bias.
+     * * 15:The output gate bias (\f$b_o\f$).
      *      A 1-D tensor of type T, of shape [num_units].
-     * * 16:projection_weights.
+     * * 16:The projection weights (\f$W_{proj}\f$). Optional.
      *      A 2-D tensor of type T, of shape [output_size, num_units].
-     * * 17:projection_bias.
+     * * 17:The projection bias (\f$b_{proj}\f$). Optional.
      *      A 1-D tensor of type T, of shape [output_size].
-     *
-     * Parameters:
-     * * 18:fused_activation_function.
-     *      An (optional) ActivationFunctionType indicating the activation
-     *      function.
-     *      If “NONE” is specified then it results in a linear activation.
-     * * 19:cell_clip.
-     *      A clipping threshold for the cell state, such that values are bound
+     * * 18:The output state (in) (\f$h_{t-1}\f$).
+     *      A 2-D tensor of type T, of shape [batch_size, output_size].
+     * * 19:The cell state (in) (\f$C_{t-1}\f$).
+     *      A 2-D tensor of type T, of shape [batch_size, num_units].
+     * * 20:The activation function (\f$g\f$).
+     *      A value indicating the activation function:
+     *      <ul>
+     *      <li>0: None;
+     *      <li>1: Relu;
+     *      <li>3: Relu6;
+     *      <li>4: Tanh;
+     *      <li>6: Sigmoid.
+     *      </ul>
+     * * 21:The clipping threshold (\f$t_{cell}\f$) for the cell state, such that values are bound
      *      within [-cell_clip, cell_clip]. If set to 0.0 then clipping is
      *      disabled.
-     * * 20:proj_clip.
-     *      A clipping threshold for the output from the projection layer, such
+     * * 22:The clipping threshold (\f$t_{proj}\f$) for the output from the projection layer, such
      *      that values are bound within [-proj_clip, proj_clip]. If set to 0.0
      *      then clipping is disabled.
      *
      * Outputs:
-     * * 0: scratch_buffer.
-     *      A 3-D tensor of type T, of shape [batch_size, num_cell, 4].
-     * * 1: output_state.
+     * * 0: The scratch buffer.
+     *      A 2-D tensor of type T, of shape [batch_size, num_units * 4] with
+     *      CIFG, or [batch_size, num_units * 3] without CIFG.
+     * * 1: The output state (out) (\f$h_t\f$).
      *      A 2-D tensor of type T, of shape [batch_size, output_size].
-     * * 2: cell_state.
+     * * 2: The cell state (out) (\f$C_t\f$).
      *      A 2-D tensor of type T, of shape [batch_size, num_units].
-     * * 3: output.
+     * * 3: The output (\f$o_t\f$).
      *      A 2-D tensor of type T, of shape [batch_size, output_size]. This is
-     *      effectively the same as the current “output_state” value.
+     *      effectively the same as the current “output state (out)” value.
      */
     LSTM = 16,
 
@@ -605,36 +840,56 @@
      *
      * The output dimensions are functions of the filter dimensions, stride, and padding.
      *
-     * The values in output Tensor is computed as:
+     * The values in the output tensor are computed as:
+     *
      *     output[batch, row, col, channel] =
      *         max_{i, j} (input[batch, row + i, col + j, channel])
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
-     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
      * Supported tensor rank: 4, with "NHWC" data layout.
      *
-     * Inputs:
-     * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
-     * 1: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
-     * 2: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
-     * 3: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
-     * 4: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
-     * 5: An INT32 value, specifying the output stride in the ‘width’ dimension.
-     * 6: An INT32 value, specifying the output stride in the ‘height’ dimension.
-     * 7: An INT32 value, specifying the filter width.
-     * 8: An INT32 value, specifying the filter height.
-     * 9: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
-     *    Specifies the activation to invoke on the result of each addition.
+     * Both explicit padding and implicit padding are supported.
      *
-     * Ouputs:
-     * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
+     * Inputs (explicit padding):
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
+     * * 1: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
+     * * 2: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
+     * * 3: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
+     * * 4: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
+     * * 5: An INT32 value, specifying the stride when walking through input
+     *      in the ‘width’ dimension.
+     * * 6: An INT32 value, specifying the stride when walking through input
+     *      in the ‘height’ dimension.
+     * * 7: An INT32 value, specifying the filter width.
+     * * 8: An INT32 value, specifying the filter height.
+     * * 9: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+     *      Specifies the activation to invoke on the result of each addition.
+     *
+     * Inputs (implicit padding):
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
+     * * 1: An INT32 value, specifying the implicit padding scheme, has to be one of the
+     *      following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
+     * * 2: An INT32 value, specifying the stride when walking through input
+     *      in the ‘width’ dimension.
+     * * 3: An INT32 value, specifying the stride when walking through input
+     *      in the ‘height’ dimension.
+     * * 4: An INT32 value, specifying the filter width.
+     * * 5: An INT32 value, specifying the filter height.
+     * * 6: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+     *      Specifies the activation to invoke on the result of each addition.
+     *
+     * Outputs:
+     * * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
      */
     MAX_POOL_2D = 17,
 
     /**
-     * Multiplies two tensors, elment-wise.
+     * Multiplies two tensors, element-wise.
      *
-     * Takes two input tensors of identical type and compatible dimensions.  The output
+     * Takes two input tensors of identical type and compatible dimensions. The output
      * is the product of both input tensors, optionally modified by an activation function.
      *
      * Two dimensions are compatible when:
@@ -644,71 +899,85 @@
      * The size of the resulting output is the maximum size along each dimension of the
      * input operands. It starts with the trailing dimensions, and works its way forward.
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
      * Supported tensor rank: up to 4
      *
      * Inputs:
-     * 0: A tensor.
-     * 1: A tensor of the same type, and compatible dimensions as input0.
-     * 2: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
-     *    Specifies the activation to invoke on the result of each addition.
+     * * 0: A tensor.
+     * * 1: A tensor of the same type, and compatible dimensions as input0.
+     * * 2: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+     *      Specifies the activation to invoke on the result of each addition.
      *
-     * Ouputs:
-     * 0: The product, a tensor of the same type as input0.
+     * Outputs:
+     * * 0: The product, a tensor of the same type as input0.
+     *      For output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the following
+     *      condition must be satisfied: output_scale > input1_scale * input2_scale.
      */
     MUL = 18,
 
     /**
      * Computes rectified linear activation on the input tensor element-wise.
      *
-     * In details:
+     * The output is calculated using this formula:
+     *
      *     output = max(0, input)
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
-     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
      * Supported tensor rank: up to 4.
      *
      * Inputs:
-     * 0: A tensor, specifying the input.
+     * * 0: A tensor, specifying the input.
      *
-     * Ouputs:
-     * 0: The output tensor of same shape as input0.
+     * Outputs:
+     * * 0: The output tensor of same shape as input0.
      */
     RELU = 19,
 
     /**
      * Computes rectified linear 1 activation on the input tensor element-wise.
      *
-     * In details:
+     * The output is calculated using this formula:
+     *
      *     output = min(1.f, max(-1.f, input))
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
-     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
      * Supported tensor rank: up to 4.
      *
      * Inputs:
-     * 0: A tensor, specifying the input.
+     * * 0: A tensor, specifying the input.
      *
-     * Ouputs:
-     * 0: The output tensor of same shape as input0.
+     * Outputs:
+     * * 0: The output tensor of same shape as input0.
      */
     RELU1 = 20,
 
     /**
      * Computes rectified linear 6 activation on the input tensor element-wise.
      *
-     * In details:
+     * The output is calculated using this formula:
+     *
      *     output = min(6, max(0, input))
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
-     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
      * Supported tensor rank: up to 4.
      *
      * Inputs:
-     * 0: A tensor, specifying the input.
+     * * 0: A tensor, specifying the input.
      *
-     * Ouputs:
-     * 0: The output tensor of same shape as input0.
+     * Outputs:
+     * * 0: The output tensor of same shape as input0.
      */
     RELU6 = 21,
 
@@ -718,36 +987,41 @@
      * Given tensor, this operation returns a tensor that has the same values as tensor,
      * but with a newly specified shape.
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
-     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
      * Supported tensor rank: up to 4.
      *
      * Inputs:
-     * 0: A tensor, specifying the tensor to be reshaped.
-     * 1: A 1-D tensor of type {@link OperandType::TENSOR_INT32}, defining the shape
-     *    of the output tensor. The number of elements implied by shape must be the same
-     *    as the number of elements in the input tensor.
+     * * 0: A tensor, specifying the tensor to be reshaped.
+     * * 1: A 1-D tensor of type {@link OperandType::TENSOR_INT32}, defining the shape
+     *      of the output tensor. The number of elements implied by shape must be the same
+     *      as the number of elements in the input tensor.
      *
-     * Ouputs:
-     * 0: The output tensor, of shape specified by the input shape.
+     * Outputs:
+     * * 0: The output tensor, of shape specified by the input shape.
      */
     RESHAPE = 22,
 
     /**
      * Resizes images to given size using the bilinear interpretation.
      *
-     * Resized images will be distorted if their original aspect ratio is not the same as input.
+     * Resized images must be distorted if their output aspect ratio is not the same as
+     * input aspect ratio.
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     *
      * Supported tensor rank: 4, with "NHWC" data layout.
      *
      * Inputs:
-     * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
-     * 1: An INT32 value, specifying the output width of the output tensor.
-     * 2: An INT32 value, specifying the output height of the output tensor.
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
+     * * 1: An INT32 value, specifying the output height of the output tensor.
+     * * 2: An INT32 value, specifying the output width of the output tensor.
      *
-     * Ouputs:
-     * 0: The output 4-D tensor, of shape [batches, new_height, new_width, depth].
+     * Outputs:
+     * * 0: The output 4-D tensor, of shape [batches, new_height, new_width, depth].
      */
     RESIZE_BILINEAR = 23,
 
@@ -766,7 +1040,7 @@
      * * “activation” is the function passed as the “fused_activation_function”
      *   argument (if not “NONE”).
      *
-     * Supported tensor types:
+     * Supported tensor types (Type T):
      * * {@link OperandType::TENSOR_FLOAT32}
      *
      * Inputs:
@@ -782,21 +1056,18 @@
      *      corresponding to the weights from each unit.
      * * 3: bias.
      *      A 1-D tensor of type T, of shape [num_units].
-     *
-     *    For FLOAT32 input tensor, bias must also be FLOAT32.
-     *    For UINT8 input tensor, bias must be INT32.
-     *
-     * Parameters
-     * * 4: fused_activation_function.
-     *      An (optional) ActivationFunctionType indicating the activation
+     * * 4: hidden state (in).
+     *      A 2-D tensor of type T, of shape [batch_size, num_units].
+     * * 5: fused_activation_function.
+     *      An optional {@link FusedActivationFunc} value indicating the activation
      *      function. If “NONE” is specified then it results in a linear
      *      activation.
      *
-     * * 5: Hidden state.
+     * Outputs:
+     * * 0: hidden state (out).
      *      A 2-D tensor of type T, of shape [batch_size, num_units].
      *
-     * Outputs:
-     * * 0: output.
+     * * 1: output.
      *      A 2-D tensor of type T, of shape [batch_size, num_units]. This is
      *      effectively the same as the current state value.
      */
@@ -806,21 +1077,26 @@
      * Computes the softmax activation on the input tensor element-wise, per batch, by
      * normalizing the input vector so the maximum coefficient is zero.
      *
-     * In details:
+     * The output is calculated using this formula:
+     *
      *     output[batch, i] =
      *         exp((input[batch, i] - max(input[batch, :])) * beta) /
      *         sum_{k}{exp((input[batch, k] - max(input[batch, :])) * beta)}
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
-     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
      * Supported tensor rank: 2 or 4.
      *
      * Inputs:
-     * 0: A 2-D or 4-D tensor, specifying the tensor to be reshaped.
-     * 1: A FLOAT32 value, specifying the scaling factor for the exponent, beta.
+     * * 0: A 2-D or 4-D tensor, specifying the tensor to be reshaped.
+     * * 1: A FLOAT32 value, specifying the positive scaling factor for the exponent, beta.
      *
-     * Ouputs:
-     * 0: The output tensor of same shape as input0.
+     * Outputs:
+     * * 0: The output tensor of same shape as input0.
+     *      For {@link OperandType::TENSOR_QUANT8_ASYMM} type,
+     *      the scale must be 1.f / 256 and the zeroPoint must be 0.
      */
     SOFTMAX = 25,
 
@@ -837,18 +1113,20 @@
      * The depth of the output tensor is input_depth * block_size * block_size.
      * The input tensor's height and width must be divisible by block_size.
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
-     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
      * Supported tensor rank: 4, with "NHWC" data layout.
      *
      * Inputs:
-     * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
-     * 1: An INT32 value, specifying the block_size. block_size must be >=1 and
-     *    block_size must be a divisor of both the input height and width.
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
+     * * 1: An INT32 value, specifying the block_size. block_size must be >=1 and
+     *      block_size must be a divisor of both the input height and width.
      *
-     * Ouputs:
-     * 0: The output 4-D tensor, of shape [batch, height/block_size, width/block_size,
-     *    depth*block_size*block_size].
+     * Outputs:
+     * * 0: The output 4-D tensor, of shape [batch, height/block_size, width/block_size,
+     *      depth*block_size*block_size].
      */
     SPACE_TO_DEPTH = 26,
 
@@ -872,8 +1150,8 @@
      *
      * Specifically, for rank 1, this layer implements the operation:
      *
-     *    memory = push(conv1d(inputs, weights_feature, feature_dim, "VALID"));
-     *    outputs = activation(memory * weights_time + bias);
+     *     memory = push(conv1d(inputs, weights_feature, feature_dim, "PADDING_VALID"));
+     *     outputs = activation(memory * weights_time + bias);
      *
      * Where:
      * * “weights_feature” is a weights matrix that processes the inputs (by
@@ -890,7 +1168,7 @@
      * Each rank adds a dimension to the weights matrices by means of stacking
      * the filters.
      *
-     * Supported tensor types:
+     * Supported tensor types (type T):
      * * {@link OperandType::TENSOR_FLOAT32}
      *
      * Inputs:
@@ -905,20 +1183,17 @@
      *      A 2-D tensor of type T, of shape [num_units, memory_size], where
      *      “memory_size” corresponds to the fixed-size of the memory.
      * * 3: bias.
-     *      A optional 1-D tensor of type T, of shape [num_units].
-     *
-     *    For FLOAT32 input tensor, bias must also be FLOAT32.
-     *    For UINT8 input tensor, bias must be INT32.
-     *
-     * Parameters:
-     * * 4: rank.
+     *      An optional 1-D tensor of type T, of shape [num_units].
+     * * 4: state (in).
+     *      A 2-D tensor of type T, of shape [batch_size, (memory_size - 1) * num_units * rank].
+     * * 5: rank.
      *      The rank of the SVD approximation.
-     * * 5: fused_activation_function.
-     *      An (optional) ActivationFunctionType indicating the activation function.
+     * * 6: fused_activation_function.
+     *      An optional {@link FusedActivationFunc} value indicating the activation function.
      *      If “NONE” is specified then it results in a linear activation.
      *
      * Outputs:
-     * * 0: state.
+     * * 0: state (out).
      *      A 2-D tensor of type T, of shape [batch_size, (memory_size - 1) * num_units * rank].
      * * 1: output.
      *      A 2-D tensor of type T, of shape [batch_size, num_units].
@@ -928,17 +1203,20 @@
     /**
      * Computes hyperbolic tangent of input tensor element-wise.
      *
-     * In details:
+     * The output is calculated using this formula:
+     *
      *     output = tanh(input)
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     *
      * Supported tensor rank: up to 4.
      *
      * Inputs:
-     * 0: A tensor, specifying the input.
+     * * 0: A tensor, specifying the input.
      *
-     * Ouputs:
-     * 0: The output tensor of same shape as input0.
+     * Outputs:
+     * * 0: The output tensor of same shape as input0.
      */
     TANH = 28,
 
@@ -965,8 +1243,8 @@
  */
 enum OperandLifeTime : int32_t {
     /**
-     * The operand is internal to the model.  It's created by an operation
-     * and consumed by other operations.
+     * The operand is internal to the model. It's created by an operation and
+     * consumed by other operations.
      */
     TEMPORARY_VARIABLE,
 
@@ -1110,7 +1388,7 @@
     /**
      * Where to find the data for this operand.
      * If the lifetime is TEMPORARY_VARIABLE, MODEL_INPUT, MODEL_OUTPUT, or NO_VALUE:
-     * - All the fields will be 0.
+     * - 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.
@@ -1218,7 +1496,7 @@
      * Updated dimension information.
      *
      * If dimensions.size() > 0, dimension information was provided along with the
-     * argument.  This can be the case for models that accept inputs of varying size.
+     * argument. This can be the case for models that accept inputs of varying size.
      * This can't change the rank, just the value of the dimensions that were
      * unspecified in the model.
      */
diff --git a/neuralnetworks/1.0/vts/functional/Android.bp b/neuralnetworks/1.0/vts/functional/Android.bp
index 54dd14a..e28113b 100644
--- a/neuralnetworks/1.0/vts/functional/Android.bp
+++ b/neuralnetworks/1.0/vts/functional/Android.bp
@@ -18,7 +18,6 @@
     name: "VtsHalNeuralnetworksTest_utils",
     srcs: [
         "Callbacks.cpp",
-        "Models.cpp",
         "GeneratedTestHarness.cpp",
     ],
     defaults: ["VtsHalTargetTestDefaults"],
@@ -41,14 +40,17 @@
 cc_test {
     name: "VtsHalNeuralnetworksV1_0TargetTest",
     srcs: [
-        "VtsHalNeuralnetworksV1_0.cpp",
-        "VtsHalNeuralnetworksV1_0BasicTest.cpp",
-        "VtsHalNeuralnetworksV1_0GeneratedTest.cpp",
+        "BasicTests.cpp",
+        "GeneratedTests.cpp",
+        "ValidateModel.cpp",
+        "ValidateRequest.cpp",
+        "ValidationTests.cpp",
+        "VtsHalNeuralnetworks.cpp",
     ],
     defaults: ["VtsHalTargetTestDefaults"],
     static_libs: [
-        "android.hardware.neuralnetworks@1.0",
         "android.hardware.neuralnetworks@1.1",
+        "android.hardware.neuralnetworks@1.0",
         "android.hidl.allocator@1.0",
         "android.hidl.memory@1.0",
         "libhidlmemory",
diff --git a/neuralnetworks/1.0/vts/functional/BasicTests.cpp b/neuralnetworks/1.0/vts/functional/BasicTests.cpp
new file mode 100644
index 0000000..945c406
--- /dev/null
+++ b/neuralnetworks/1.0/vts/functional/BasicTests.cpp
@@ -0,0 +1,56 @@
+/*
+ * Copyright (C) 2018 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 "neuralnetworks_hidl_hal_test"
+
+#include "VtsHalNeuralnetworks.h"
+
+namespace android {
+namespace hardware {
+namespace neuralnetworks {
+namespace V1_0 {
+namespace vts {
+namespace functional {
+
+// create device test
+TEST_F(NeuralnetworksHidlTest, CreateDevice) {}
+
+// status test
+TEST_F(NeuralnetworksHidlTest, StatusTest) {
+    Return<DeviceStatus> status = device->getStatus();
+    ASSERT_TRUE(status.isOk());
+    EXPECT_EQ(DeviceStatus::AVAILABLE, static_cast<DeviceStatus>(status));
+}
+
+// initialization
+TEST_F(NeuralnetworksHidlTest, GetCapabilitiesTest) {
+    Return<void> ret =
+        device->getCapabilities([](ErrorStatus status, const Capabilities& capabilities) {
+            EXPECT_EQ(ErrorStatus::NONE, status);
+            EXPECT_LT(0.0f, capabilities.float32Performance.execTime);
+            EXPECT_LT(0.0f, capabilities.float32Performance.powerUsage);
+            EXPECT_LT(0.0f, capabilities.quantized8Performance.execTime);
+            EXPECT_LT(0.0f, capabilities.quantized8Performance.powerUsage);
+        });
+    EXPECT_TRUE(ret.isOk());
+}
+
+}  // namespace functional
+}  // namespace vts
+}  // namespace V1_0
+}  // namespace neuralnetworks
+}  // namespace hardware
+}  // namespace android
diff --git a/neuralnetworks/1.0/vts/functional/Callbacks.h b/neuralnetworks/1.0/vts/functional/Callbacks.h
index 18c3167..570a4fb 100644
--- a/neuralnetworks/1.0/vts/functional/Callbacks.h
+++ b/neuralnetworks/1.0/vts/functional/Callbacks.h
@@ -17,14 +17,6 @@
 namespace V1_0 {
 namespace implementation {
 
-using ::android::hardware::hidl_array;
-using ::android::hardware::hidl_memory;
-using ::android::hardware::hidl_string;
-using ::android::hardware::hidl_vec;
-using ::android::hardware::Return;
-using ::android::hardware::Void;
-using ::android::sp;
-
 /**
  * The CallbackBase class is used internally by the NeuralNetworks runtime to
  * synchronize between different threads. An asynchronous task is launched
diff --git a/neuralnetworks/1.0/vts/functional/GeneratedTestHarness.cpp b/neuralnetworks/1.0/vts/functional/GeneratedTestHarness.cpp
index 8646a4c..ed1fb94 100644
--- a/neuralnetworks/1.0/vts/functional/GeneratedTestHarness.cpp
+++ b/neuralnetworks/1.0/vts/functional/GeneratedTestHarness.cpp
@@ -179,7 +179,7 @@
     }
 }
 
-void Execute(sp<V1_0::IDevice>& device, std::function<V1_0::Model(void)> create_model,
+void Execute(const sp<V1_0::IDevice>& device, std::function<V1_0::Model(void)> create_model,
              std::function<bool(int)> is_ignored,
              const std::vector<MixedTypedExampleType>& examples) {
     V1_0::Model model = create_model();
@@ -223,7 +223,7 @@
     EvaluatePreparedModel(preparedModel, is_ignored, examples);
 }
 
-void Execute(sp<V1_1::IDevice>& device, std::function<V1_1::Model(void)> create_model,
+void Execute(const sp<V1_1::IDevice>& device, std::function<V1_1::Model(void)> create_model,
              std::function<bool(int)> is_ignored,
              const std::vector<MixedTypedExampleType>& examples) {
     V1_1::Model model = create_model();
@@ -242,8 +242,8 @@
     // launch prepare model
     sp<PreparedModelCallback> preparedModelCallback = new PreparedModelCallback();
     ASSERT_NE(nullptr, preparedModelCallback.get());
-    Return<ErrorStatus> prepareLaunchStatus =
-        device->prepareModel_1_1(model, preparedModelCallback);
+    Return<ErrorStatus> prepareLaunchStatus = device->prepareModel_1_1(
+        model, ExecutionPreference::FAST_SINGLE_ANSWER, preparedModelCallback);
     ASSERT_TRUE(prepareLaunchStatus.isOk());
     ASSERT_EQ(ErrorStatus::NONE, static_cast<ErrorStatus>(prepareLaunchStatus));
 
diff --git a/neuralnetworks/1.0/vts/functional/VtsHalNeuralnetworksV1_0GeneratedTest.cpp b/neuralnetworks/1.0/vts/functional/GeneratedTests.cpp
similarity index 61%
rename from neuralnetworks/1.0/vts/functional/VtsHalNeuralnetworksV1_0GeneratedTest.cpp
rename to neuralnetworks/1.0/vts/functional/GeneratedTests.cpp
index b99aef7..2107333 100644
--- a/neuralnetworks/1.0/vts/functional/VtsHalNeuralnetworksV1_0GeneratedTest.cpp
+++ b/neuralnetworks/1.0/vts/functional/GeneratedTests.cpp
@@ -16,47 +16,33 @@
 
 #define LOG_TAG "neuralnetworks_hidl_hal_test"
 
-#include "VtsHalNeuralnetworksV1_0.h"
+#include "VtsHalNeuralnetworks.h"
 
 #include "Callbacks.h"
 #include "TestHarness.h"
+#include "Utils.h"
 
 #include <android-base/logging.h>
 #include <android/hidl/memory/1.0/IMemory.h>
 #include <hidlmemory/mapping.h>
 
-using ::android::hardware::neuralnetworks::V1_0::IDevice;
-using ::android::hardware::neuralnetworks::V1_0::IPreparedModel;
-using ::android::hardware::neuralnetworks::V1_0::Capabilities;
-using ::android::hardware::neuralnetworks::V1_0::DeviceStatus;
-using ::android::hardware::neuralnetworks::V1_0::FusedActivationFunc;
-using ::android::hardware::neuralnetworks::V1_0::Model;
-using ::android::hardware::neuralnetworks::V1_0::OperationType;
-using ::android::hardware::neuralnetworks::V1_0::PerformanceInfo;
-using ::android::hardware::Return;
-using ::android::hardware::Void;
-using ::android::hardware::hidl_memory;
-using ::android::hardware::hidl_string;
-using ::android::hardware::hidl_vec;
-using ::android::hidl::allocator::V1_0::IAllocator;
-using ::android::hidl::memory::V1_0::IMemory;
-using ::android::sp;
-
 namespace android {
 namespace hardware {
 namespace neuralnetworks {
 
 namespace generated_tests {
 using ::generated_tests::MixedTypedExampleType;
-extern void Execute(sp<IDevice>&, std::function<Model(void)>, std::function<bool(int)>,
-                    const std::vector<MixedTypedExampleType>&);
+extern void Execute(const sp<V1_0::IDevice>&, std::function<V1_0::Model(void)>,
+                    std::function<bool(int)>, const std::vector<MixedTypedExampleType>&);
 }  // namespace generated_tests
 
 namespace V1_0 {
 namespace vts {
 namespace functional {
+
 using ::android::hardware::neuralnetworks::V1_0::implementation::ExecutionCallback;
 using ::android::hardware::neuralnetworks::V1_0::implementation::PreparedModelCallback;
+using ::android::nn::allocateSharedMemory;
 
 // Mixed-typed examples
 typedef generated_tests::MixedTypedExampleType MixedTypedExample;
diff --git a/neuralnetworks/1.0/vts/functional/Models.cpp b/neuralnetworks/1.0/vts/functional/Models.cpp
deleted file mode 100644
index 180286a..0000000
--- a/neuralnetworks/1.0/vts/functional/Models.cpp
+++ /dev/null
@@ -1,202 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#define LOG_TAG "neuralnetworks_hidl_hal_test"
-
-#include "Models.h"
-#include "Utils.h"
-
-#include <android-base/logging.h>
-#include <android/hidl/allocator/1.0/IAllocator.h>
-#include <android/hidl/memory/1.0/IMemory.h>
-#include <hidlmemory/mapping.h>
-#include <vector>
-
-using ::android::sp;
-
-namespace android {
-namespace hardware {
-namespace neuralnetworks {
-
-// create a valid model
-V1_1::Model createValidTestModel_1_1() {
-    const std::vector<float> operand2Data = {5.0f, 6.0f, 7.0f, 8.0f};
-    const uint32_t size = operand2Data.size() * sizeof(float);
-
-    const uint32_t operand1 = 0;
-    const uint32_t operand2 = 1;
-    const uint32_t operand3 = 2;
-    const uint32_t operand4 = 3;
-
-    const std::vector<Operand> operands = {
-        {
-            .type = OperandType::TENSOR_FLOAT32,
-            .dimensions = {1, 2, 2, 1},
-            .numberOfConsumers = 1,
-            .scale = 0.0f,
-            .zeroPoint = 0,
-            .lifetime = OperandLifeTime::MODEL_INPUT,
-            .location = {.poolIndex = 0, .offset = 0, .length = 0},
-        },
-        {
-            .type = OperandType::TENSOR_FLOAT32,
-            .dimensions = {1, 2, 2, 1},
-            .numberOfConsumers = 1,
-            .scale = 0.0f,
-            .zeroPoint = 0,
-            .lifetime = OperandLifeTime::CONSTANT_COPY,
-            .location = {.poolIndex = 0, .offset = 0, .length = size},
-        },
-        {
-            .type = OperandType::INT32,
-            .dimensions = {},
-            .numberOfConsumers = 1,
-            .scale = 0.0f,
-            .zeroPoint = 0,
-            .lifetime = OperandLifeTime::CONSTANT_COPY,
-            .location = {.poolIndex = 0, .offset = size, .length = sizeof(int32_t)},
-        },
-        {
-            .type = OperandType::TENSOR_FLOAT32,
-            .dimensions = {1, 2, 2, 1},
-            .numberOfConsumers = 0,
-            .scale = 0.0f,
-            .zeroPoint = 0,
-            .lifetime = OperandLifeTime::MODEL_OUTPUT,
-            .location = {.poolIndex = 0, .offset = 0, .length = 0},
-        },
-    };
-
-    const std::vector<Operation> operations = {{
-        .type = OperationType::ADD, .inputs = {operand1, operand2, operand3}, .outputs = {operand4},
-    }};
-
-    const std::vector<uint32_t> inputIndexes = {operand1};
-    const std::vector<uint32_t> outputIndexes = {operand4};
-    std::vector<uint8_t> operandValues(
-        reinterpret_cast<const uint8_t*>(operand2Data.data()),
-        reinterpret_cast<const uint8_t*>(operand2Data.data()) + size);
-    int32_t activation[1] = {static_cast<int32_t>(FusedActivationFunc::NONE)};
-    operandValues.insert(operandValues.end(), reinterpret_cast<const uint8_t*>(&activation[0]),
-                         reinterpret_cast<const uint8_t*>(&activation[1]));
-
-    const std::vector<hidl_memory> pools = {};
-
-    return {
-        .operands = operands,
-        .operations = operations,
-        .inputIndexes = inputIndexes,
-        .outputIndexes = outputIndexes,
-        .operandValues = operandValues,
-        .pools = pools,
-    };
-}
-
-// create first invalid model
-V1_1::Model createInvalidTestModel1_1_1() {
-    Model model = createValidTestModel_1_1();
-    model.operations[0].type = static_cast<OperationType>(0xDEADBEEF); /* INVALID */
-    return model;
-}
-
-// create second invalid model
-V1_1::Model createInvalidTestModel2_1_1() {
-    Model model = createValidTestModel_1_1();
-    const uint32_t operand1 = 0;
-    const uint32_t operand5 = 4;  // INVALID OPERAND
-    model.inputIndexes = std::vector<uint32_t>({operand1, operand5 /* INVALID OPERAND */});
-    return model;
-}
-
-V1_0::Model createValidTestModel_1_0() {
-    V1_1::Model model = createValidTestModel_1_1();
-    return nn::convertToV1_0(model);
-}
-
-V1_0::Model createInvalidTestModel1_1_0() {
-    V1_1::Model model = createInvalidTestModel1_1_1();
-    return nn::convertToV1_0(model);
-}
-
-V1_0::Model createInvalidTestModel2_1_0() {
-    V1_1::Model model = createInvalidTestModel2_1_1();
-    return nn::convertToV1_0(model);
-}
-
-// create a valid request
-Request createValidTestRequest() {
-    std::vector<float> inputData = {1.0f, 2.0f, 3.0f, 4.0f};
-    std::vector<float> outputData = {-1.0f, -1.0f, -1.0f, -1.0f};
-    const uint32_t INPUT = 0;
-    const uint32_t OUTPUT = 1;
-
-    // prepare inputs
-    uint32_t inputSize = static_cast<uint32_t>(inputData.size() * sizeof(float));
-    uint32_t outputSize = static_cast<uint32_t>(outputData.size() * sizeof(float));
-    std::vector<RequestArgument> inputs = {{
-        .location = {.poolIndex = INPUT, .offset = 0, .length = inputSize}, .dimensions = {},
-    }};
-    std::vector<RequestArgument> outputs = {{
-        .location = {.poolIndex = OUTPUT, .offset = 0, .length = outputSize}, .dimensions = {},
-    }};
-    std::vector<hidl_memory> pools = {nn::allocateSharedMemory(inputSize),
-                                      nn::allocateSharedMemory(outputSize)};
-    if (pools[INPUT].size() == 0 || pools[OUTPUT].size() == 0) {
-        return {};
-    }
-
-    // load data
-    sp<IMemory> inputMemory = mapMemory(pools[INPUT]);
-    sp<IMemory> outputMemory = mapMemory(pools[OUTPUT]);
-    if (inputMemory.get() == nullptr || outputMemory.get() == nullptr) {
-        return {};
-    }
-    float* inputPtr = reinterpret_cast<float*>(static_cast<void*>(inputMemory->getPointer()));
-    float* outputPtr = reinterpret_cast<float*>(static_cast<void*>(outputMemory->getPointer()));
-    if (inputPtr == nullptr || outputPtr == nullptr) {
-        return {};
-    }
-    inputMemory->update();
-    outputMemory->update();
-    std::copy(inputData.begin(), inputData.end(), inputPtr);
-    std::copy(outputData.begin(), outputData.end(), outputPtr);
-    inputMemory->commit();
-    outputMemory->commit();
-
-    return {.inputs = inputs, .outputs = outputs, .pools = pools};
-}
-
-// create first invalid request
-Request createInvalidTestRequest1() {
-    Request request = createValidTestRequest();
-    const uint32_t INVALID = 2;
-    std::vector<float> inputData = {1.0f, 2.0f, 3.0f, 4.0f};
-    uint32_t inputSize = static_cast<uint32_t>(inputData.size() * sizeof(float));
-    request.inputs[0].location = {
-        .poolIndex = INVALID /* INVALID */, .offset = 0, .length = inputSize};
-    return request;
-}
-
-// create second invalid request
-Request createInvalidTestRequest2() {
-    Request request = createValidTestRequest();
-    request.inputs[0].dimensions = std::vector<uint32_t>({1, 2, 3, 4, 5, 6, 7, 8} /* INVALID */);
-    return request;
-}
-
-}  // namespace neuralnetworks
-}  // namespace hardware
-}  // namespace android
diff --git a/neuralnetworks/1.0/vts/functional/Models.h b/neuralnetworks/1.0/vts/functional/Models.h
index 9398235..a1fbe92 100644
--- a/neuralnetworks/1.0/vts/functional/Models.h
+++ b/neuralnetworks/1.0/vts/functional/Models.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (C) 2017 The Android Open Source Project
+ * Copyright (C) 2018 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.
@@ -14,29 +14,187 @@
  * limitations under the License.
  */
 
+#ifndef VTS_HAL_NEURALNETWORKS_V1_0_VTS_FUNCTIONAL_MODELS_H
+#define VTS_HAL_NEURALNETWORKS_V1_0_VTS_FUNCTIONAL_MODELS_H
+
 #define LOG_TAG "neuralnetworks_hidl_hal_test"
 
-#include <android/hardware/neuralnetworks/1.1/types.h>
+#include "TestHarness.h"
+
+#include <android/hardware/neuralnetworks/1.0/types.h>
 
 namespace android {
 namespace hardware {
 namespace neuralnetworks {
+namespace V1_0 {
+namespace vts {
+namespace functional {
 
-// create V1_1 model
-V1_1::Model createValidTestModel_1_1();
-V1_1::Model createInvalidTestModel1_1_1();
-V1_1::Model createInvalidTestModel2_1_1();
+using MixedTypedExample = generated_tests::MixedTypedExampleType;
 
-// create V1_0 model
-V1_0::Model createValidTestModel_1_0();
-V1_0::Model createInvalidTestModel1_1_0();
-V1_0::Model createInvalidTestModel2_1_0();
+#define FOR_EACH_TEST_MODEL(FN)                          \
+    FN(add_broadcast_quant8)                             \
+    FN(add)                                              \
+    FN(add_quant8)                                       \
+    FN(avg_pool_float_1)                                 \
+    FN(avg_pool_float_2)                                 \
+    FN(avg_pool_float_3)                                 \
+    FN(avg_pool_float_4)                                 \
+    FN(avg_pool_float_5)                                 \
+    FN(avg_pool_quant8_1)                                \
+    FN(avg_pool_quant8_2)                                \
+    FN(avg_pool_quant8_3)                                \
+    FN(avg_pool_quant8_4)                                \
+    FN(avg_pool_quant8_5)                                \
+    FN(concat_float_1)                                   \
+    FN(concat_float_2)                                   \
+    FN(concat_float_3)                                   \
+    FN(concat_quant8_1)                                  \
+    FN(concat_quant8_2)                                  \
+    FN(concat_quant8_3)                                  \
+    FN(conv_1_h3_w2_SAME)                                \
+    FN(conv_1_h3_w2_VALID)                               \
+    FN(conv_3_h3_w2_SAME)                                \
+    FN(conv_3_h3_w2_VALID)                               \
+    FN(conv_float_2)                                     \
+    FN(conv_float_channels)                              \
+    FN(conv_float_channels_weights_as_inputs)            \
+    FN(conv_float_large)                                 \
+    FN(conv_float_large_weights_as_inputs)               \
+    FN(conv_float)                                       \
+    FN(conv_float_weights_as_inputs)                     \
+    FN(conv_quant8_2)                                    \
+    FN(conv_quant8_channels)                             \
+    FN(conv_quant8_channels_weights_as_inputs)           \
+    FN(conv_quant8_large)                                \
+    FN(conv_quant8_large_weights_as_inputs)              \
+    FN(conv_quant8)                                      \
+    FN(conv_quant8_overflow)                             \
+    FN(conv_quant8_overflow_weights_as_inputs)           \
+    FN(conv_quant8_weights_as_inputs)                    \
+    FN(depth_to_space_float_1)                           \
+    FN(depth_to_space_float_2)                           \
+    FN(depth_to_space_float_3)                           \
+    FN(depth_to_space_quant8_1)                          \
+    FN(depth_to_space_quant8_2)                          \
+    FN(depthwise_conv2d_float_2)                         \
+    FN(depthwise_conv2d_float_large_2)                   \
+    FN(depthwise_conv2d_float_large_2_weights_as_inputs) \
+    FN(depthwise_conv2d_float_large)                     \
+    FN(depthwise_conv2d_float_large_weights_as_inputs)   \
+    FN(depthwise_conv2d_float)                           \
+    FN(depthwise_conv2d_float_weights_as_inputs)         \
+    FN(depthwise_conv2d_quant8_2)                        \
+    FN(depthwise_conv2d_quant8_large)                    \
+    FN(depthwise_conv2d_quant8_large_weights_as_inputs)  \
+    FN(depthwise_conv2d_quant8)                          \
+    FN(depthwise_conv2d_quant8_weights_as_inputs)        \
+    FN(depthwise_conv)                                   \
+    FN(dequantize)                                       \
+    FN(embedding_lookup)                                 \
+    FN(floor)                                            \
+    FN(fully_connected_float_2)                          \
+    FN(fully_connected_float_large)                      \
+    FN(fully_connected_float_large_weights_as_inputs)    \
+    FN(fully_connected_float)                            \
+    FN(fully_connected_float_weights_as_inputs)          \
+    FN(fully_connected_quant8_2)                         \
+    FN(fully_connected_quant8_large)                     \
+    FN(fully_connected_quant8_large_weights_as_inputs)   \
+    FN(fully_connected_quant8)                           \
+    FN(fully_connected_quant8_weights_as_inputs)         \
+    FN(hashtable_lookup_float)                           \
+    FN(hashtable_lookup_quant8)                          \
+    FN(l2_normalization_2)                               \
+    FN(l2_normalization_large)                           \
+    FN(l2_normalization)                                 \
+    FN(l2_pool_float_2)                                  \
+    FN(l2_pool_float_large)                              \
+    FN(l2_pool_float)                                    \
+    FN(local_response_norm_float_1)                      \
+    FN(local_response_norm_float_2)                      \
+    FN(local_response_norm_float_3)                      \
+    FN(local_response_norm_float_4)                      \
+    FN(logistic_float_1)                                 \
+    FN(logistic_float_2)                                 \
+    FN(logistic_quant8_1)                                \
+    FN(logistic_quant8_2)                                \
+    FN(lsh_projection_2)                                 \
+    FN(lsh_projection)                                   \
+    FN(lsh_projection_weights_as_inputs)                 \
+    FN(lstm2)                                            \
+    FN(lstm2_state2)                                     \
+    FN(lstm2_state)                                      \
+    FN(lstm3)                                            \
+    FN(lstm3_state2)                                     \
+    FN(lstm3_state3)                                     \
+    FN(lstm3_state)                                      \
+    FN(lstm)                                             \
+    FN(lstm_state2)                                      \
+    FN(lstm_state)                                       \
+    FN(max_pool_float_1)                                 \
+    FN(max_pool_float_2)                                 \
+    FN(max_pool_float_3)                                 \
+    FN(max_pool_float_4)                                 \
+    FN(max_pool_quant8_1)                                \
+    FN(max_pool_quant8_2)                                \
+    FN(max_pool_quant8_3)                                \
+    FN(max_pool_quant8_4)                                \
+    FN(mobilenet_224_gender_basic_fixed)                 \
+    FN(mobilenet_quantized)                              \
+    FN(mul_broadcast_quant8)                             \
+    FN(mul)                                              \
+    FN(mul_quant8)                                       \
+    FN(mul_relu)                                         \
+    FN(relu1_float_1)                                    \
+    FN(relu1_float_2)                                    \
+    FN(relu1_quant8_1)                                   \
+    FN(relu1_quant8_2)                                   \
+    FN(relu6_float_1)                                    \
+    FN(relu6_float_2)                                    \
+    FN(relu6_quant8_1)                                   \
+    FN(relu6_quant8_2)                                   \
+    FN(relu_float_1)                                     \
+    FN(relu_float_2)                                     \
+    FN(relu_quant8_1)                                    \
+    FN(relu_quant8_2)                                    \
+    FN(reshape)                                          \
+    FN(reshape_quant8)                                   \
+    FN(reshape_quant8_weights_as_inputs)                 \
+    FN(reshape_weights_as_inputs)                        \
+    FN(resize_bilinear_2)                                \
+    FN(resize_bilinear)                                  \
+    FN(rnn)                                              \
+    FN(rnn_state)                                        \
+    FN(softmax_float_1)                                  \
+    FN(softmax_float_2)                                  \
+    FN(softmax_quant8_1)                                 \
+    FN(softmax_quant8_2)                                 \
+    FN(space_to_depth_float_1)                           \
+    FN(space_to_depth_float_2)                           \
+    FN(space_to_depth_float_3)                           \
+    FN(space_to_depth_quant8_1)                          \
+    FN(space_to_depth_quant8_2)                          \
+    FN(svdf2)                                            \
+    FN(svdf)                                             \
+    FN(svdf_state)                                       \
+    FN(tanh)
 
-// create the request
-V1_0::Request createValidTestRequest();
-V1_0::Request createInvalidTestRequest1();
-V1_0::Request createInvalidTestRequest2();
+#define FORWARD_DECLARE_GENERATED_OBJECTS(function) \
+    namespace function {                            \
+    extern std::vector<MixedTypedExample> examples; \
+    Model createTestModel();                        \
+    }
 
+FOR_EACH_TEST_MODEL(FORWARD_DECLARE_GENERATED_OBJECTS)
+
+#undef FORWARD_DECLARE_GENERATED_OBJECTS
+
+}  // namespace functional
+}  // namespace vts
+}  // namespace V1_0
 }  // namespace neuralnetworks
 }  // namespace hardware
 }  // namespace android
+
+#endif  // VTS_HAL_NEURALNETWORKS_V1_0_VTS_FUNCTIONAL_MODELS_H
diff --git a/neuralnetworks/1.0/vts/functional/ValidateModel.cpp b/neuralnetworks/1.0/vts/functional/ValidateModel.cpp
new file mode 100644
index 0000000..4f0697e
--- /dev/null
+++ b/neuralnetworks/1.0/vts/functional/ValidateModel.cpp
@@ -0,0 +1,506 @@
+/*
+ * Copyright (C) 2018 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 "neuralnetworks_hidl_hal_test"
+
+#include "VtsHalNeuralnetworks.h"
+
+#include "Callbacks.h"
+
+namespace android {
+namespace hardware {
+namespace neuralnetworks {
+namespace V1_0 {
+namespace vts {
+namespace functional {
+
+using ::android::hardware::neuralnetworks::V1_0::implementation::ExecutionCallback;
+using ::android::hardware::neuralnetworks::V1_0::implementation::PreparedModelCallback;
+
+///////////////////////// UTILITY FUNCTIONS /////////////////////////
+
+static void validateGetSupportedOperations(const sp<IDevice>& device, const std::string& message,
+                                           const V1_0::Model& model) {
+    SCOPED_TRACE(message + " [getSupportedOperations]");
+
+    Return<void> ret =
+        device->getSupportedOperations(model, [&](ErrorStatus status, const hidl_vec<bool>&) {
+            EXPECT_EQ(ErrorStatus::INVALID_ARGUMENT, status);
+        });
+    EXPECT_TRUE(ret.isOk());
+}
+
+static void validatePrepareModel(const sp<IDevice>& device, const std::string& message,
+                                 const V1_0::Model& model) {
+    SCOPED_TRACE(message + " [prepareModel]");
+
+    sp<PreparedModelCallback> preparedModelCallback = new PreparedModelCallback();
+    ASSERT_NE(nullptr, preparedModelCallback.get());
+    Return<ErrorStatus> prepareLaunchStatus = device->prepareModel(model, preparedModelCallback);
+    ASSERT_TRUE(prepareLaunchStatus.isOk());
+    ASSERT_EQ(ErrorStatus::INVALID_ARGUMENT, static_cast<ErrorStatus>(prepareLaunchStatus));
+
+    preparedModelCallback->wait();
+    ErrorStatus prepareReturnStatus = preparedModelCallback->getStatus();
+    ASSERT_EQ(ErrorStatus::INVALID_ARGUMENT, prepareReturnStatus);
+    sp<IPreparedModel> preparedModel = preparedModelCallback->getPreparedModel();
+    ASSERT_EQ(nullptr, preparedModel.get());
+}
+
+// Primary validation function. This function will take a valid model, apply a
+// mutation to it to invalidate the model, then pass it to interface calls that
+// use the model. Note that the model here is passed by value, and any mutation
+// to the model does not leave this function.
+static void validate(const sp<IDevice>& device, const std::string& message, V1_0::Model model,
+                     const std::function<void(Model*)>& mutation) {
+    mutation(&model);
+    validateGetSupportedOperations(device, message, model);
+    validatePrepareModel(device, message, model);
+}
+
+// Delete element from hidl_vec. hidl_vec doesn't support a "remove" operation,
+// so this is efficiently accomplished by moving the element to the end and
+// resizing the hidl_vec to one less.
+template <typename Type>
+static void hidl_vec_removeAt(hidl_vec<Type>* vec, uint32_t index) {
+    if (vec) {
+        std::rotate(vec->begin() + index, vec->begin() + index + 1, vec->end());
+        vec->resize(vec->size() - 1);
+    }
+}
+
+template <typename Type>
+static uint32_t hidl_vec_push_back(hidl_vec<Type>* vec, const Type& value) {
+    // assume vec is valid
+    const uint32_t index = vec->size();
+    vec->resize(index + 1);
+    (*vec)[index] = value;
+    return index;
+}
+
+static uint32_t addOperand(Model* model) {
+    return hidl_vec_push_back(&model->operands,
+                              {
+                                  .type = OperandType::INT32,
+                                  .dimensions = {},
+                                  .numberOfConsumers = 0,
+                                  .scale = 0.0f,
+                                  .zeroPoint = 0,
+                                  .lifetime = OperandLifeTime::MODEL_INPUT,
+                                  .location = {.poolIndex = 0, .offset = 0, .length = 0},
+                              });
+}
+
+static uint32_t addOperand(Model* model, OperandLifeTime lifetime) {
+    uint32_t index = addOperand(model);
+    model->operands[index].numberOfConsumers = 1;
+    model->operands[index].lifetime = lifetime;
+    return index;
+}
+
+///////////////////////// 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 void mutateOperandTypeTest(const sp<IDevice>& device, const V1_0::Model& model) {
+    for (size_t operand = 0; operand < model.operands.size(); ++operand) {
+        for (int32_t invalidOperandType : invalidOperandTypes) {
+            const std::string message = "mutateOperandTypeTest: operand " +
+                                        std::to_string(operand) + " set to value " +
+                                        std::to_string(invalidOperandType);
+            validate(device, message, model, [operand, invalidOperandType](Model* model) {
+                model->operands[operand].type = static_cast<OperandType>(invalidOperandType);
+            });
+        }
+    }
+}
+
+///////////////////////// VALIDATE OPERAND RANK /////////////////////////
+
+static uint32_t getInvalidRank(OperandType type) {
+    switch (type) {
+        case OperandType::FLOAT32:
+        case OperandType::INT32:
+        case OperandType::UINT32:
+            return 1;
+        case OperandType::TENSOR_FLOAT32:
+        case OperandType::TENSOR_INT32:
+        case OperandType::TENSOR_QUANT8_ASYMM:
+            return 0;
+        default:
+            return 0;
+    }
+}
+
+static void mutateOperandRankTest(const sp<IDevice>& device, const V1_0::Model& model) {
+    for (size_t operand = 0; operand < model.operands.size(); ++operand) {
+        const uint32_t invalidRank = getInvalidRank(model.operands[operand].type);
+        const std::string message = "mutateOperandRankTest: operand " + std::to_string(operand) +
+                                    " has rank of " + std::to_string(invalidRank);
+        validate(device, message, model, [operand, invalidRank](Model* model) {
+            model->operands[operand].dimensions = std::vector<uint32_t>(invalidRank, 0);
+        });
+    }
+}
+
+///////////////////////// VALIDATE OPERAND SCALE /////////////////////////
+
+static float getInvalidScale(OperandType type) {
+    switch (type) {
+        case OperandType::FLOAT32:
+        case OperandType::INT32:
+        case OperandType::UINT32:
+        case OperandType::TENSOR_FLOAT32:
+            return 1.0f;
+        case OperandType::TENSOR_INT32:
+            return -1.0f;
+        case OperandType::TENSOR_QUANT8_ASYMM:
+            return 0.0f;
+        default:
+            return 0.0f;
+    }
+}
+
+static void mutateOperandScaleTest(const sp<IDevice>& device, const V1_0::Model& model) {
+    for (size_t operand = 0; operand < model.operands.size(); ++operand) {
+        const float invalidScale = getInvalidScale(model.operands[operand].type);
+        const std::string message = "mutateOperandScaleTest: operand " + std::to_string(operand) +
+                                    " has scale of " + std::to_string(invalidScale);
+        validate(device, message, model, [operand, invalidScale](Model* model) {
+            model->operands[operand].scale = invalidScale;
+        });
+    }
+}
+
+///////////////////////// VALIDATE OPERAND ZERO POINT /////////////////////////
+
+static std::vector<int32_t> getInvalidZeroPoints(OperandType type) {
+    switch (type) {
+        case OperandType::FLOAT32:
+        case OperandType::INT32:
+        case OperandType::UINT32:
+        case OperandType::TENSOR_FLOAT32:
+        case OperandType::TENSOR_INT32:
+            return {1};
+        case OperandType::TENSOR_QUANT8_ASYMM:
+            return {-1, 256};
+        default:
+            return {};
+    }
+}
+
+static void mutateOperandZeroPointTest(const sp<IDevice>& device, const V1_0::Model& model) {
+    for (size_t operand = 0; operand < model.operands.size(); ++operand) {
+        const std::vector<int32_t> invalidZeroPoints =
+            getInvalidZeroPoints(model.operands[operand].type);
+        for (int32_t invalidZeroPoint : invalidZeroPoints) {
+            const std::string message = "mutateOperandZeroPointTest: operand " +
+                                        std::to_string(operand) + " has zero point of " +
+                                        std::to_string(invalidZeroPoint);
+            validate(device, message, model, [operand, invalidZeroPoint](Model* model) {
+                model->operands[operand].zeroPoint = invalidZeroPoint;
+            });
+        }
+    }
+}
+
+///////////////////////// VALIDATE EXTRA ??? /////////////////////////
+
+// TODO: Operand::lifetime
+// TODO: Operand::location
+
+///////////////////////// VALIDATE OPERATION OPERAND TYPE /////////////////////////
+
+static void mutateOperand(Operand* operand, OperandType type) {
+    Operand newOperand = *operand;
+    newOperand.type = type;
+    switch (type) {
+        case OperandType::FLOAT32:
+        case OperandType::INT32:
+        case OperandType::UINT32:
+            newOperand.dimensions = hidl_vec<uint32_t>();
+            newOperand.scale = 0.0f;
+            newOperand.zeroPoint = 0;
+            break;
+        case OperandType::TENSOR_FLOAT32:
+            newOperand.dimensions =
+                operand->dimensions.size() > 0 ? operand->dimensions : hidl_vec<uint32_t>({1});
+            newOperand.scale = 0.0f;
+            newOperand.zeroPoint = 0;
+            break;
+        case OperandType::TENSOR_INT32:
+            newOperand.dimensions =
+                operand->dimensions.size() > 0 ? operand->dimensions : hidl_vec<uint32_t>({1});
+            newOperand.zeroPoint = 0;
+            break;
+        case OperandType::TENSOR_QUANT8_ASYMM:
+            newOperand.dimensions =
+                operand->dimensions.size() > 0 ? operand->dimensions : hidl_vec<uint32_t>({1});
+            newOperand.scale = operand->scale != 0.0f ? operand->scale : 1.0f;
+            break;
+        case OperandType::OEM:
+        case OperandType::TENSOR_OEM_BYTE:
+        default:
+            break;
+    }
+    *operand = newOperand;
+}
+
+static bool mutateOperationOperandTypeSkip(size_t operand, const V1_0::Model& model) {
+    // LSH_PROJECTION's second argument is allowed to have any type. This is the
+    // only operation that currently has a type that can be anything independent
+    // from any other type. Changing the operand type to any other type will
+    // result in a valid model for LSH_PROJECTION. If this is the case, skip the
+    // test.
+    for (const Operation& operation : model.operations) {
+        if (operation.type == OperationType::LSH_PROJECTION && operand == operation.inputs[1]) {
+            return true;
+        }
+    }
+    return false;
+}
+
+static void mutateOperationOperandTypeTest(const sp<IDevice>& device, const V1_0::Model& model) {
+    for (size_t operand = 0; operand < model.operands.size(); ++operand) {
+        if (mutateOperationOperandTypeSkip(operand, model)) {
+            continue;
+        }
+        for (OperandType invalidOperandType : hidl_enum_iterator<OperandType>{}) {
+            // Do not test OEM types
+            if (invalidOperandType == model.operands[operand].type ||
+                invalidOperandType == OperandType::OEM ||
+                invalidOperandType == OperandType::TENSOR_OEM_BYTE) {
+                continue;
+            }
+            const std::string message = "mutateOperationOperandTypeTest: operand " +
+                                        std::to_string(operand) + " set to type " +
+                                        toString(invalidOperandType);
+            validate(device, message, model, [operand, invalidOperandType](Model* model) {
+                mutateOperand(&model->operands[operand], invalidOperandType);
+            });
+        }
+    }
+}
+
+///////////////////////// VALIDATE MODEL OPERATION TYPE /////////////////////////
+
+static const int32_t invalidOperationTypes[] = {
+    static_cast<int32_t>(OperationType::ADD) - 1,            // lower bound fundamental
+    static_cast<int32_t>(OperationType::TANH) + 1,           // upper bound fundamental
+    static_cast<int32_t>(OperationType::OEM_OPERATION) - 1,  // lower bound OEM
+    static_cast<int32_t>(OperationType::OEM_OPERATION) + 1,  // upper bound OEM
+};
+
+static void mutateOperationTypeTest(const sp<IDevice>& device, const V1_0::Model& model) {
+    for (size_t operation = 0; operation < model.operations.size(); ++operation) {
+        for (int32_t invalidOperationType : invalidOperationTypes) {
+            const std::string message = "mutateOperationTypeTest: operation " +
+                                        std::to_string(operation) + " set to value " +
+                                        std::to_string(invalidOperationType);
+            validate(device, message, model, [operation, invalidOperationType](Model* model) {
+                model->operations[operation].type =
+                    static_cast<OperationType>(invalidOperationType);
+            });
+        }
+    }
+}
+
+///////////////////////// VALIDATE MODEL OPERATION INPUT OPERAND INDEX /////////////////////////
+
+static void mutateOperationInputOperandIndexTest(const sp<IDevice>& device,
+                                                 const V1_0::Model& model) {
+    for (size_t operation = 0; operation < model.operations.size(); ++operation) {
+        const uint32_t invalidOperand = model.operands.size();
+        for (size_t input = 0; input < model.operations[operation].inputs.size(); ++input) {
+            const std::string message = "mutateOperationInputOperandIndexTest: operation " +
+                                        std::to_string(operation) + " input " +
+                                        std::to_string(input);
+            validate(device, message, model, [operation, input, invalidOperand](Model* model) {
+                model->operations[operation].inputs[input] = invalidOperand;
+            });
+        }
+    }
+}
+
+///////////////////////// VALIDATE MODEL OPERATION OUTPUT OPERAND INDEX /////////////////////////
+
+static void mutateOperationOutputOperandIndexTest(const sp<IDevice>& device,
+                                                  const V1_0::Model& model) {
+    for (size_t operation = 0; operation < model.operations.size(); ++operation) {
+        const uint32_t invalidOperand = model.operands.size();
+        for (size_t output = 0; output < model.operations[operation].outputs.size(); ++output) {
+            const std::string message = "mutateOperationOutputOperandIndexTest: operation " +
+                                        std::to_string(operation) + " output " +
+                                        std::to_string(output);
+            validate(device, message, model, [operation, output, invalidOperand](Model* model) {
+                model->operations[operation].outputs[output] = invalidOperand;
+            });
+        }
+    }
+}
+
+///////////////////////// REMOVE OPERAND FROM EVERYTHING /////////////////////////
+
+static void removeValueAndDecrementGreaterValues(hidl_vec<uint32_t>* vec, uint32_t value) {
+    if (vec) {
+        // remove elements matching "value"
+        auto last = std::remove(vec->begin(), vec->end(), value);
+        vec->resize(std::distance(vec->begin(), last));
+
+        // decrement elements exceeding "value"
+        std::transform(vec->begin(), vec->end(), vec->begin(),
+                       [value](uint32_t v) { return v > value ? v-- : v; });
+    }
+}
+
+static void removeOperand(Model* model, uint32_t index) {
+    hidl_vec_removeAt(&model->operands, index);
+    for (Operation& operation : model->operations) {
+        removeValueAndDecrementGreaterValues(&operation.inputs, index);
+        removeValueAndDecrementGreaterValues(&operation.outputs, index);
+    }
+    removeValueAndDecrementGreaterValues(&model->inputIndexes, index);
+    removeValueAndDecrementGreaterValues(&model->outputIndexes, index);
+}
+
+static void removeOperandTest(const sp<IDevice>& device, const V1_0::Model& model) {
+    for (size_t operand = 0; operand < model.operands.size(); ++operand) {
+        const std::string message = "removeOperandTest: operand " + std::to_string(operand);
+        validate(device, message, model,
+                 [operand](Model* model) { removeOperand(model, operand); });
+    }
+}
+
+///////////////////////// REMOVE OPERATION /////////////////////////
+
+static void removeOperation(Model* model, uint32_t index) {
+    for (uint32_t operand : model->operations[index].inputs) {
+        model->operands[operand].numberOfConsumers--;
+    }
+    hidl_vec_removeAt(&model->operations, index);
+}
+
+static void removeOperationTest(const sp<IDevice>& device, const V1_0::Model& model) {
+    for (size_t operation = 0; operation < model.operations.size(); ++operation) {
+        const std::string message = "removeOperationTest: operation " + std::to_string(operation);
+        validate(device, message, model,
+                 [operation](Model* model) { removeOperation(model, operation); });
+    }
+}
+
+///////////////////////// REMOVE OPERATION INPUT /////////////////////////
+
+static void removeOperationInputTest(const sp<IDevice>& device, const V1_0::Model& model) {
+    for (size_t operation = 0; operation < model.operations.size(); ++operation) {
+        for (size_t input = 0; input < model.operations[operation].inputs.size(); ++input) {
+            const V1_0::Operation& op = model.operations[operation];
+            // CONCATENATION has at least 2 inputs, with the last element being
+            // INT32. Skip this test if removing one of CONCATENATION's
+            // inputs still produces a valid model.
+            if (op.type == V1_0::OperationType::CONCATENATION && op.inputs.size() > 2 &&
+                input != op.inputs.size() - 1) {
+                continue;
+            }
+            const std::string message = "removeOperationInputTest: operation " +
+                                        std::to_string(operation) + ", input " +
+                                        std::to_string(input);
+            validate(device, message, model, [operation, input](Model* model) {
+                uint32_t operand = model->operations[operation].inputs[input];
+                model->operands[operand].numberOfConsumers--;
+                hidl_vec_removeAt(&model->operations[operation].inputs, input);
+            });
+        }
+    }
+}
+
+///////////////////////// REMOVE OPERATION OUTPUT /////////////////////////
+
+static void removeOperationOutputTest(const sp<IDevice>& device, const V1_0::Model& model) {
+    for (size_t operation = 0; operation < model.operations.size(); ++operation) {
+        for (size_t output = 0; output < model.operations[operation].outputs.size(); ++output) {
+            const std::string message = "removeOperationOutputTest: operation " +
+                                        std::to_string(operation) + ", output " +
+                                        std::to_string(output);
+            validate(device, message, model, [operation, output](Model* model) {
+                hidl_vec_removeAt(&model->operations[operation].outputs, output);
+            });
+        }
+    }
+}
+
+///////////////////////// MODEL VALIDATION /////////////////////////
+
+// TODO: remove model input
+// TODO: remove model output
+// TODO: add unused operation
+
+///////////////////////// ADD OPERATION INPUT /////////////////////////
+
+static void addOperationInputTest(const sp<IDevice>& device, const V1_0::Model& model) {
+    for (size_t operation = 0; operation < model.operations.size(); ++operation) {
+        const std::string message = "addOperationInputTest: operation " + std::to_string(operation);
+        validate(device, message, model, [operation](Model* model) {
+            uint32_t index = addOperand(model, OperandLifeTime::MODEL_INPUT);
+            hidl_vec_push_back(&model->operations[operation].inputs, index);
+            hidl_vec_push_back(&model->inputIndexes, index);
+        });
+    }
+}
+
+///////////////////////// ADD OPERATION OUTPUT /////////////////////////
+
+static void addOperationOutputTest(const sp<IDevice>& device, const V1_0::Model& model) {
+    for (size_t operation = 0; operation < model.operations.size(); ++operation) {
+        const std::string message =
+            "addOperationOutputTest: operation " + std::to_string(operation);
+        validate(device, message, model, [operation](Model* model) {
+            uint32_t index = addOperand(model, OperandLifeTime::MODEL_OUTPUT);
+            hidl_vec_push_back(&model->operations[operation].outputs, index);
+            hidl_vec_push_back(&model->outputIndexes, index);
+        });
+    }
+}
+
+////////////////////////// ENTRY POINT //////////////////////////////
+
+void ValidationTest::validateModel(const V1_0::Model& model) {
+    mutateOperandTypeTest(device, model);
+    mutateOperandRankTest(device, model);
+    mutateOperandScaleTest(device, model);
+    mutateOperandZeroPointTest(device, model);
+    mutateOperationOperandTypeTest(device, model);
+    mutateOperationTypeTest(device, model);
+    mutateOperationInputOperandIndexTest(device, model);
+    mutateOperationOutputOperandIndexTest(device, model);
+    removeOperandTest(device, model);
+    removeOperationTest(device, model);
+    removeOperationInputTest(device, model);
+    removeOperationOutputTest(device, model);
+    addOperationInputTest(device, model);
+    addOperationOutputTest(device, model);
+}
+
+}  // namespace functional
+}  // namespace vts
+}  // namespace V1_0
+}  // namespace neuralnetworks
+}  // namespace hardware
+}  // namespace android
diff --git a/neuralnetworks/1.0/vts/functional/ValidateRequest.cpp b/neuralnetworks/1.0/vts/functional/ValidateRequest.cpp
new file mode 100644
index 0000000..08f2613
--- /dev/null
+++ b/neuralnetworks/1.0/vts/functional/ValidateRequest.cpp
@@ -0,0 +1,261 @@
+/*
+ * Copyright (C) 2018 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 "neuralnetworks_hidl_hal_test"
+
+#include "VtsHalNeuralnetworks.h"
+
+#include "Callbacks.h"
+#include "TestHarness.h"
+#include "Utils.h"
+
+#include <android-base/logging.h>
+#include <android/hidl/memory/1.0/IMemory.h>
+#include <hidlmemory/mapping.h>
+
+namespace android {
+namespace hardware {
+namespace neuralnetworks {
+namespace V1_0 {
+namespace vts {
+namespace functional {
+
+using ::android::hardware::neuralnetworks::V1_0::implementation::ExecutionCallback;
+using ::android::hardware::neuralnetworks::V1_0::implementation::PreparedModelCallback;
+using ::android::hidl::memory::V1_0::IMemory;
+using generated_tests::MixedTyped;
+using generated_tests::MixedTypedExampleType;
+using generated_tests::for_all;
+
+///////////////////////// UTILITY FUNCTIONS /////////////////////////
+
+static void createPreparedModel(const sp<IDevice>& device, const V1_0::Model& model,
+                                sp<IPreparedModel>* preparedModel) {
+    ASSERT_NE(nullptr, preparedModel);
+
+    // see if service can handle model
+    bool fullySupportsModel = false;
+    Return<void> supportedOpsLaunchStatus = device->getSupportedOperations(
+        model, [&fullySupportsModel](ErrorStatus status, const hidl_vec<bool>& supported) {
+            ASSERT_EQ(ErrorStatus::NONE, status);
+            ASSERT_NE(0ul, supported.size());
+            fullySupportsModel =
+                std::all_of(supported.begin(), supported.end(), [](bool valid) { return valid; });
+        });
+    ASSERT_TRUE(supportedOpsLaunchStatus.isOk());
+
+    // launch prepare model
+    sp<PreparedModelCallback> preparedModelCallback = new PreparedModelCallback();
+    ASSERT_NE(nullptr, preparedModelCallback.get());
+    Return<ErrorStatus> prepareLaunchStatus = device->prepareModel(model, preparedModelCallback);
+    ASSERT_TRUE(prepareLaunchStatus.isOk());
+    ASSERT_EQ(ErrorStatus::NONE, static_cast<ErrorStatus>(prepareLaunchStatus));
+
+    // retrieve prepared model
+    preparedModelCallback->wait();
+    ErrorStatus prepareReturnStatus = preparedModelCallback->getStatus();
+    *preparedModel = preparedModelCallback->getPreparedModel();
+
+    // The getSupportedOperations call returns a list of operations that are
+    // guaranteed not to fail if prepareModel is called, and
+    // 'fullySupportsModel' is true i.f.f. the entire model is guaranteed.
+    // If a driver has any doubt that it can prepare an operation, it must
+    // return false. So here, if a driver isn't sure if it can support an
+    // operation, but reports that it successfully prepared the model, the test
+    // can continue.
+    if (!fullySupportsModel && prepareReturnStatus != ErrorStatus::NONE) {
+        ASSERT_EQ(nullptr, preparedModel->get());
+        LOG(INFO) << "NN VTS: Unable to test Request validation because vendor service cannot "
+                     "prepare model that it does not support.";
+        std::cout << "[          ]   Unable to test Request validation because vendor service "
+                     "cannot prepare model that it does not support."
+                  << std::endl;
+        return;
+    }
+    ASSERT_EQ(ErrorStatus::NONE, prepareReturnStatus);
+    ASSERT_NE(nullptr, preparedModel->get());
+}
+
+// Primary validation function. This function will take a valid request, apply a
+// mutation to it to invalidate the request, then pass it to interface calls
+// that use the request. Note that the request here is passed by value, and any
+// mutation to the request does not leave this function.
+static void validate(const sp<IPreparedModel>& preparedModel, const std::string& message,
+                     Request request, const std::function<void(Request*)>& mutation) {
+    mutation(&request);
+    SCOPED_TRACE(message + " [execute]");
+
+    sp<ExecutionCallback> executionCallback = new ExecutionCallback();
+    ASSERT_NE(nullptr, executionCallback.get());
+    Return<ErrorStatus> executeLaunchStatus = preparedModel->execute(request, executionCallback);
+    ASSERT_TRUE(executeLaunchStatus.isOk());
+    ASSERT_EQ(ErrorStatus::INVALID_ARGUMENT, static_cast<ErrorStatus>(executeLaunchStatus));
+
+    executionCallback->wait();
+    ErrorStatus executionReturnStatus = executionCallback->getStatus();
+    ASSERT_EQ(ErrorStatus::INVALID_ARGUMENT, executionReturnStatus);
+}
+
+// Delete element from hidl_vec. hidl_vec doesn't support a "remove" operation,
+// so this is efficiently accomplished by moving the element to the end and
+// resizing the hidl_vec to one less.
+template <typename Type>
+static void hidl_vec_removeAt(hidl_vec<Type>* vec, uint32_t index) {
+    if (vec) {
+        std::rotate(vec->begin() + index, vec->begin() + index + 1, vec->end());
+        vec->resize(vec->size() - 1);
+    }
+}
+
+template <typename Type>
+static uint32_t hidl_vec_push_back(hidl_vec<Type>* vec, const Type& value) {
+    // assume vec is valid
+    const uint32_t index = vec->size();
+    vec->resize(index + 1);
+    (*vec)[index] = value;
+    return index;
+}
+
+///////////////////////// REMOVE INPUT ////////////////////////////////////
+
+static void removeInputTest(const sp<IPreparedModel>& preparedModel, const Request& request) {
+    for (size_t input = 0; input < request.inputs.size(); ++input) {
+        const std::string message = "removeInput: removed input " + std::to_string(input);
+        validate(preparedModel, message, request,
+                 [input](Request* request) { hidl_vec_removeAt(&request->inputs, input); });
+    }
+}
+
+///////////////////////// REMOVE OUTPUT ////////////////////////////////////
+
+static void removeOutputTest(const sp<IPreparedModel>& preparedModel, const Request& request) {
+    for (size_t output = 0; output < request.outputs.size(); ++output) {
+        const std::string message = "removeOutput: removed Output " + std::to_string(output);
+        validate(preparedModel, message, request,
+                 [output](Request* request) { hidl_vec_removeAt(&request->outputs, output); });
+    }
+}
+
+///////////////////////////// ENTRY POINT //////////////////////////////////
+
+std::vector<Request> createRequests(const std::vector<MixedTypedExampleType>& examples) {
+    const uint32_t INPUT = 0;
+    const uint32_t OUTPUT = 1;
+
+    std::vector<Request> requests;
+
+    for (auto& example : examples) {
+        const MixedTyped& inputs = example.first;
+        const MixedTyped& outputs = example.second;
+
+        std::vector<RequestArgument> inputs_info, outputs_info;
+        uint32_t inputSize = 0, outputSize = 0;
+
+        // This function only partially specifies the metadata (vector of RequestArguments).
+        // The contents are copied over below.
+        for_all(inputs, [&inputs_info, &inputSize](int index, auto, auto s) {
+            if (inputs_info.size() <= static_cast<size_t>(index)) inputs_info.resize(index + 1);
+            RequestArgument arg = {
+                .location = {.poolIndex = INPUT, .offset = 0, .length = static_cast<uint32_t>(s)},
+                .dimensions = {},
+            };
+            RequestArgument arg_empty = {
+                .hasNoValue = true,
+            };
+            inputs_info[index] = s ? arg : arg_empty;
+            inputSize += s;
+        });
+        // Compute offset for inputs 1 and so on
+        {
+            size_t offset = 0;
+            for (auto& i : inputs_info) {
+                if (!i.hasNoValue) i.location.offset = offset;
+                offset += i.location.length;
+            }
+        }
+
+        // Go through all outputs, initialize RequestArgument descriptors
+        for_all(outputs, [&outputs_info, &outputSize](int index, auto, auto s) {
+            if (outputs_info.size() <= static_cast<size_t>(index)) outputs_info.resize(index + 1);
+            RequestArgument arg = {
+                .location = {.poolIndex = OUTPUT, .offset = 0, .length = static_cast<uint32_t>(s)},
+                .dimensions = {},
+            };
+            outputs_info[index] = arg;
+            outputSize += s;
+        });
+        // Compute offset for outputs 1 and so on
+        {
+            size_t offset = 0;
+            for (auto& i : outputs_info) {
+                i.location.offset = offset;
+                offset += i.location.length;
+            }
+        }
+        std::vector<hidl_memory> pools = {nn::allocateSharedMemory(inputSize),
+                                          nn::allocateSharedMemory(outputSize)};
+        if (pools[INPUT].size() == 0 || pools[OUTPUT].size() == 0) {
+            return {};
+        }
+
+        // map pool
+        sp<IMemory> inputMemory = mapMemory(pools[INPUT]);
+        if (inputMemory == nullptr) {
+            return {};
+        }
+        char* inputPtr = reinterpret_cast<char*>(static_cast<void*>(inputMemory->getPointer()));
+        if (inputPtr == nullptr) {
+            return {};
+        }
+
+        // initialize pool
+        inputMemory->update();
+        for_all(inputs, [&inputs_info, inputPtr](int index, auto p, auto s) {
+            char* begin = (char*)p;
+            char* end = begin + s;
+            // TODO: handle more than one input
+            std::copy(begin, end, inputPtr + inputs_info[index].location.offset);
+        });
+        inputMemory->commit();
+
+        requests.push_back({.inputs = inputs_info, .outputs = outputs_info, .pools = pools});
+    }
+
+    return requests;
+}
+
+void ValidationTest::validateRequests(const V1_0::Model& model,
+                                      const std::vector<Request>& requests) {
+    // create IPreparedModel
+    sp<IPreparedModel> preparedModel;
+    ASSERT_NO_FATAL_FAILURE(createPreparedModel(device, model, &preparedModel));
+    if (preparedModel == nullptr) {
+        return;
+    }
+
+    // validate each request
+    for (const Request& request : requests) {
+        removeInputTest(preparedModel, request);
+        removeOutputTest(preparedModel, request);
+    }
+}
+
+}  // namespace functional
+}  // namespace vts
+}  // namespace V1_0
+}  // namespace neuralnetworks
+}  // namespace hardware
+}  // namespace android
diff --git a/neuralnetworks/1.0/vts/functional/ValidationTests.cpp b/neuralnetworks/1.0/vts/functional/ValidationTests.cpp
new file mode 100644
index 0000000..98fc1c5
--- /dev/null
+++ b/neuralnetworks/1.0/vts/functional/ValidationTests.cpp
@@ -0,0 +1,50 @@
+/*
+ * Copyright (C) 2018 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 "neuralnetworks_hidl_hal_test"
+
+#include "Models.h"
+#include "VtsHalNeuralnetworks.h"
+
+namespace android {
+namespace hardware {
+namespace neuralnetworks {
+namespace V1_0 {
+namespace vts {
+namespace functional {
+
+// forward declarations
+std::vector<Request> createRequests(const std::vector<MixedTypedExample>& examples);
+
+// generate validation tests
+#define VTS_CURRENT_TEST_CASE(TestName)                                           \
+    TEST_F(ValidationTest, TestName) {                                            \
+        const Model model = TestName::createTestModel();                          \
+        const std::vector<Request> requests = createRequests(TestName::examples); \
+        validateModel(model);                                                     \
+        validateRequests(model, requests);                                        \
+    }
+
+FOR_EACH_TEST_MODEL(VTS_CURRENT_TEST_CASE)
+
+#undef VTS_CURRENT_TEST_CASE
+
+}  // namespace functional
+}  // namespace vts
+}  // namespace V1_0
+}  // namespace neuralnetworks
+}  // namespace hardware
+}  // namespace android
diff --git a/neuralnetworks/1.0/vts/functional/VtsHalNeuralnetworksV1_0.cpp b/neuralnetworks/1.0/vts/functional/VtsHalNeuralnetworks.cpp
similarity index 64%
rename from neuralnetworks/1.0/vts/functional/VtsHalNeuralnetworksV1_0.cpp
rename to neuralnetworks/1.0/vts/functional/VtsHalNeuralnetworks.cpp
index b14fb2c..1ff3b66 100644
--- a/neuralnetworks/1.0/vts/functional/VtsHalNeuralnetworksV1_0.cpp
+++ b/neuralnetworks/1.0/vts/functional/VtsHalNeuralnetworks.cpp
@@ -16,15 +16,7 @@
 
 #define LOG_TAG "neuralnetworks_hidl_hal_test"
 
-#include "VtsHalNeuralnetworksV1_0.h"
-#include "Utils.h"
-
-#include <android-base/logging.h>
-
-using ::android::hardware::hidl_memory;
-using ::android::hidl::allocator::V1_0::IAllocator;
-using ::android::hidl::memory::V1_0::IMemory;
-using ::android::sp;
+#include "VtsHalNeuralnetworks.h"
 
 namespace android {
 namespace hardware {
@@ -33,11 +25,6 @@
 namespace vts {
 namespace functional {
 
-// allocator helper
-hidl_memory allocateSharedMemory(int64_t size) {
-    return nn::allocateSharedMemory(size);
-}
-
 // A class for test environment setup
 NeuralnetworksHidlEnvironment::NeuralnetworksHidlEnvironment() {}
 
@@ -51,23 +38,49 @@
 }
 
 void NeuralnetworksHidlEnvironment::registerTestServices() {
-    registerTestService<V1_0::IDevice>();
+    registerTestService<IDevice>();
 }
 
 // The main test class for NEURALNETWORK HIDL HAL.
+NeuralnetworksHidlTest::NeuralnetworksHidlTest() {}
+
 NeuralnetworksHidlTest::~NeuralnetworksHidlTest() {}
 
 void NeuralnetworksHidlTest::SetUp() {
-    device = ::testing::VtsHalHidlTargetTestBase::getService<V1_0::IDevice>(
+    ::testing::VtsHalHidlTargetTestBase::SetUp();
+    device = ::testing::VtsHalHidlTargetTestBase::getService<IDevice>(
         NeuralnetworksHidlEnvironment::getInstance());
     ASSERT_NE(nullptr, device.get());
 }
 
-void NeuralnetworksHidlTest::TearDown() {}
+void NeuralnetworksHidlTest::TearDown() {
+    device = nullptr;
+    ::testing::VtsHalHidlTargetTestBase::TearDown();
+}
 
 }  // namespace functional
 }  // namespace vts
+
+::std::ostream& operator<<(::std::ostream& os, ErrorStatus errorStatus) {
+    return os << toString(errorStatus);
+}
+
+::std::ostream& operator<<(::std::ostream& os, DeviceStatus deviceStatus) {
+    return os << toString(deviceStatus);
+}
+
 }  // namespace V1_0
 }  // namespace neuralnetworks
 }  // namespace hardware
 }  // namespace android
+
+using android::hardware::neuralnetworks::V1_0::vts::functional::NeuralnetworksHidlEnvironment;
+
+int main(int argc, char** argv) {
+    ::testing::AddGlobalTestEnvironment(NeuralnetworksHidlEnvironment::getInstance());
+    ::testing::InitGoogleTest(&argc, argv);
+    NeuralnetworksHidlEnvironment::getInstance()->init(&argc, argv);
+
+    int status = RUN_ALL_TESTS();
+    return status;
+}
diff --git a/neuralnetworks/1.0/vts/functional/VtsHalNeuralnetworksV1_0.h b/neuralnetworks/1.0/vts/functional/VtsHalNeuralnetworks.h
similarity index 60%
rename from neuralnetworks/1.0/vts/functional/VtsHalNeuralnetworksV1_0.h
rename to neuralnetworks/1.0/vts/functional/VtsHalNeuralnetworks.h
index fbb1607..e79129b 100644
--- a/neuralnetworks/1.0/vts/functional/VtsHalNeuralnetworksV1_0.h
+++ b/neuralnetworks/1.0/vts/functional/VtsHalNeuralnetworks.h
@@ -18,16 +18,15 @@
 #define VTS_HAL_NEURALNETWORKS_V1_0_TARGET_TESTS_H
 
 #include <android/hardware/neuralnetworks/1.0/IDevice.h>
-#include <android/hardware/neuralnetworks/1.0/IExecutionCallback.h>
-#include <android/hardware/neuralnetworks/1.0/IPreparedModel.h>
-#include <android/hardware/neuralnetworks/1.0/IPreparedModelCallback.h>
 #include <android/hardware/neuralnetworks/1.0/types.h>
-#include <android/hidl/allocator/1.0/IAllocator.h>
 
 #include <VtsHalHidlTargetTestBase.h>
 #include <VtsHalHidlTargetTestEnvBase.h>
+
+#include <android-base/macros.h>
 #include <gtest/gtest.h>
-#include <string>
+#include <iostream>
+#include <vector>
 
 namespace android {
 namespace hardware {
@@ -36,47 +35,47 @@
 namespace vts {
 namespace functional {
 
-hidl_memory allocateSharedMemory(int64_t size);
-
 // A class for test environment setup
 class NeuralnetworksHidlEnvironment : public ::testing::VtsHalHidlTargetTestEnvBase {
+    DISALLOW_COPY_AND_ASSIGN(NeuralnetworksHidlEnvironment);
     NeuralnetworksHidlEnvironment();
-    NeuralnetworksHidlEnvironment(const NeuralnetworksHidlEnvironment&) = delete;
-    NeuralnetworksHidlEnvironment(NeuralnetworksHidlEnvironment&&) = delete;
-    NeuralnetworksHidlEnvironment& operator=(const NeuralnetworksHidlEnvironment&) = delete;
-    NeuralnetworksHidlEnvironment& operator=(NeuralnetworksHidlEnvironment&&) = delete;
+    ~NeuralnetworksHidlEnvironment() override;
 
    public:
-    ~NeuralnetworksHidlEnvironment() override;
     static NeuralnetworksHidlEnvironment* getInstance();
     void registerTestServices() override;
 };
 
 // The main test class for NEURALNETWORKS HIDL HAL.
 class NeuralnetworksHidlTest : public ::testing::VtsHalHidlTargetTestBase {
+    DISALLOW_COPY_AND_ASSIGN(NeuralnetworksHidlTest);
+
    public:
+    NeuralnetworksHidlTest();
     ~NeuralnetworksHidlTest() override;
     void SetUp() override;
     void TearDown() override;
 
-    sp<V1_0::IDevice> device;
+   protected:
+    sp<IDevice> device;
 };
+
+// Tag for the validation tests
+class ValidationTest : public NeuralnetworksHidlTest {
+   protected:
+    void validateModel(const Model& model);
+    void validateRequests(const Model& model, const std::vector<Request>& request);
+};
+
+// Tag for the generated tests
+class GeneratedTest : public NeuralnetworksHidlTest {};
+
 }  // namespace functional
 }  // namespace vts
 
 // pretty-print values for error messages
-
-template <typename CharT, typename Traits>
-::std::basic_ostream<CharT, Traits>& operator<<(::std::basic_ostream<CharT, Traits>& os,
-                                                V1_0::ErrorStatus errorStatus) {
-    return os << toString(errorStatus);
-}
-
-template <typename CharT, typename Traits>
-::std::basic_ostream<CharT, Traits>& operator<<(::std::basic_ostream<CharT, Traits>& os,
-                                                V1_0::DeviceStatus deviceStatus) {
-    return os << toString(deviceStatus);
-}
+::std::ostream& operator<<(::std::ostream& os, ErrorStatus errorStatus);
+::std::ostream& operator<<(::std::ostream& os, DeviceStatus deviceStatus);
 
 }  // namespace V1_0
 }  // namespace neuralnetworks
diff --git a/neuralnetworks/1.0/vts/functional/VtsHalNeuralnetworksV1_0BasicTest.cpp b/neuralnetworks/1.0/vts/functional/VtsHalNeuralnetworksV1_0BasicTest.cpp
deleted file mode 100644
index 59e5b80..0000000
--- a/neuralnetworks/1.0/vts/functional/VtsHalNeuralnetworksV1_0BasicTest.cpp
+++ /dev/null
@@ -1,293 +0,0 @@
-/*
- * Copyright (C) 2018 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 "neuralnetworks_hidl_hal_test"
-
-#include "VtsHalNeuralnetworksV1_0.h"
-
-#include "Callbacks.h"
-#include "Models.h"
-#include "TestHarness.h"
-
-#include <android-base/logging.h>
-#include <android/hidl/memory/1.0/IMemory.h>
-#include <hidlmemory/mapping.h>
-
-using ::android::hardware::neuralnetworks::V1_0::IDevice;
-using ::android::hardware::neuralnetworks::V1_0::IPreparedModel;
-using ::android::hardware::neuralnetworks::V1_0::Capabilities;
-using ::android::hardware::neuralnetworks::V1_0::DeviceStatus;
-using ::android::hardware::neuralnetworks::V1_0::FusedActivationFunc;
-using ::android::hardware::neuralnetworks::V1_0::Model;
-using ::android::hardware::neuralnetworks::V1_0::OperationType;
-using ::android::hardware::neuralnetworks::V1_0::PerformanceInfo;
-using ::android::hardware::Return;
-using ::android::hardware::Void;
-using ::android::hardware::hidl_memory;
-using ::android::hardware::hidl_string;
-using ::android::hardware::hidl_vec;
-using ::android::hidl::allocator::V1_0::IAllocator;
-using ::android::hidl::memory::V1_0::IMemory;
-using ::android::sp;
-
-namespace android {
-namespace hardware {
-namespace neuralnetworks {
-namespace V1_0 {
-namespace vts {
-namespace functional {
-using ::android::hardware::neuralnetworks::V1_0::implementation::ExecutionCallback;
-using ::android::hardware::neuralnetworks::V1_0::implementation::PreparedModelCallback;
-
-static void doPrepareModelShortcut(const sp<IDevice>& device, sp<IPreparedModel>* preparedModel) {
-    ASSERT_NE(nullptr, preparedModel);
-    Model model = createValidTestModel_1_0();
-
-    // see if service can handle model
-    bool fullySupportsModel = false;
-    Return<void> supportedOpsLaunchStatus = device->getSupportedOperations(
-        model, [&fullySupportsModel](ErrorStatus status, const hidl_vec<bool>& supported) {
-            ASSERT_EQ(ErrorStatus::NONE, status);
-            ASSERT_NE(0ul, supported.size());
-            fullySupportsModel =
-                std::all_of(supported.begin(), supported.end(), [](bool valid) { return valid; });
-        });
-    ASSERT_TRUE(supportedOpsLaunchStatus.isOk());
-
-    // launch prepare model
-    sp<PreparedModelCallback> preparedModelCallback = new PreparedModelCallback();
-    ASSERT_NE(nullptr, preparedModelCallback.get());
-    Return<ErrorStatus> prepareLaunchStatus = device->prepareModel(model, preparedModelCallback);
-    ASSERT_TRUE(prepareLaunchStatus.isOk());
-    ASSERT_EQ(ErrorStatus::NONE, static_cast<ErrorStatus>(prepareLaunchStatus));
-
-    // retrieve prepared model
-    preparedModelCallback->wait();
-    ErrorStatus prepareReturnStatus = preparedModelCallback->getStatus();
-    *preparedModel = preparedModelCallback->getPreparedModel();
-
-    // The getSupportedOperations call returns a list of operations that are
-    // guaranteed not to fail if prepareModel is called, and
-    // 'fullySupportsModel' is true i.f.f. the entire model is guaranteed.
-    // If a driver has any doubt that it can prepare an operation, it must
-    // return false. So here, if a driver isn't sure if it can support an
-    // operation, but reports that it successfully prepared the model, the test
-    // can continue.
-    if (!fullySupportsModel && prepareReturnStatus != ErrorStatus::NONE) {
-        ASSERT_EQ(nullptr, preparedModel->get());
-        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;
-    }
-    ASSERT_EQ(ErrorStatus::NONE, prepareReturnStatus);
-    ASSERT_NE(nullptr, preparedModel->get());
-}
-
-// create device test
-TEST_F(NeuralnetworksHidlTest, CreateDevice) {}
-
-// status test
-TEST_F(NeuralnetworksHidlTest, StatusTest) {
-    Return<DeviceStatus> status = device->getStatus();
-    ASSERT_TRUE(status.isOk());
-    EXPECT_EQ(DeviceStatus::AVAILABLE, static_cast<DeviceStatus>(status));
-}
-
-// initialization
-TEST_F(NeuralnetworksHidlTest, GetCapabilitiesTest) {
-    Return<void> ret =
-        device->getCapabilities([](ErrorStatus status, const Capabilities& capabilities) {
-            EXPECT_EQ(ErrorStatus::NONE, status);
-            EXPECT_LT(0.0f, capabilities.float32Performance.execTime);
-            EXPECT_LT(0.0f, capabilities.float32Performance.powerUsage);
-            EXPECT_LT(0.0f, capabilities.quantized8Performance.execTime);
-            EXPECT_LT(0.0f, capabilities.quantized8Performance.powerUsage);
-        });
-    EXPECT_TRUE(ret.isOk());
-}
-
-// supported operations positive test
-TEST_F(NeuralnetworksHidlTest, SupportedOperationsPositiveTest) {
-    Model model = createValidTestModel_1_0();
-    Return<void> ret = device->getSupportedOperations(
-        model, [&](ErrorStatus status, const hidl_vec<bool>& supported) {
-            EXPECT_EQ(ErrorStatus::NONE, status);
-            EXPECT_EQ(model.operations.size(), supported.size());
-        });
-    EXPECT_TRUE(ret.isOk());
-}
-
-// supported operations negative test 1
-TEST_F(NeuralnetworksHidlTest, SupportedOperationsNegativeTest1) {
-    Model model = createInvalidTestModel1_1_0();
-    Return<void> ret = device->getSupportedOperations(
-        model, [&](ErrorStatus status, const hidl_vec<bool>& supported) {
-            EXPECT_EQ(ErrorStatus::INVALID_ARGUMENT, status);
-            (void)supported;
-        });
-    EXPECT_TRUE(ret.isOk());
-}
-
-// supported operations negative test 2
-TEST_F(NeuralnetworksHidlTest, SupportedOperationsNegativeTest2) {
-    Model model = createInvalidTestModel2_1_0();
-    Return<void> ret = device->getSupportedOperations(
-        model, [&](ErrorStatus status, const hidl_vec<bool>& supported) {
-            EXPECT_EQ(ErrorStatus::INVALID_ARGUMENT, status);
-            (void)supported;
-        });
-    EXPECT_TRUE(ret.isOk());
-}
-
-// prepare simple model positive test
-TEST_F(NeuralnetworksHidlTest, SimplePrepareModelPositiveTest) {
-    sp<IPreparedModel> preparedModel;
-    doPrepareModelShortcut(device, &preparedModel);
-}
-
-// prepare simple model negative test 1
-TEST_F(NeuralnetworksHidlTest, SimplePrepareModelNegativeTest1) {
-    Model model = createInvalidTestModel1_1_0();
-    sp<PreparedModelCallback> preparedModelCallback = new PreparedModelCallback();
-    ASSERT_NE(nullptr, preparedModelCallback.get());
-    Return<ErrorStatus> prepareLaunchStatus = device->prepareModel(model, preparedModelCallback);
-    ASSERT_TRUE(prepareLaunchStatus.isOk());
-    EXPECT_EQ(ErrorStatus::INVALID_ARGUMENT, static_cast<ErrorStatus>(prepareLaunchStatus));
-
-    preparedModelCallback->wait();
-    ErrorStatus prepareReturnStatus = preparedModelCallback->getStatus();
-    EXPECT_EQ(ErrorStatus::INVALID_ARGUMENT, prepareReturnStatus);
-    sp<IPreparedModel> preparedModel = preparedModelCallback->getPreparedModel();
-    EXPECT_EQ(nullptr, preparedModel.get());
-}
-
-// prepare simple model negative test 2
-TEST_F(NeuralnetworksHidlTest, SimplePrepareModelNegativeTest2) {
-    Model model = createInvalidTestModel2_1_0();
-    sp<PreparedModelCallback> preparedModelCallback = new PreparedModelCallback();
-    ASSERT_NE(nullptr, preparedModelCallback.get());
-    Return<ErrorStatus> prepareLaunchStatus = device->prepareModel(model, preparedModelCallback);
-    ASSERT_TRUE(prepareLaunchStatus.isOk());
-    EXPECT_EQ(ErrorStatus::INVALID_ARGUMENT, static_cast<ErrorStatus>(prepareLaunchStatus));
-
-    preparedModelCallback->wait();
-    ErrorStatus prepareReturnStatus = preparedModelCallback->getStatus();
-    EXPECT_EQ(ErrorStatus::INVALID_ARGUMENT, prepareReturnStatus);
-    sp<IPreparedModel> preparedModel = preparedModelCallback->getPreparedModel();
-    EXPECT_EQ(nullptr, preparedModel.get());
-}
-
-// execute simple graph positive test
-TEST_F(NeuralnetworksHidlTest, SimpleExecuteGraphPositiveTest) {
-    std::vector<float> outputData = {-1.0f, -1.0f, -1.0f, -1.0f};
-    std::vector<float> expectedData = {6.0f, 8.0f, 10.0f, 12.0f};
-    const uint32_t OUTPUT = 1;
-
-    sp<IPreparedModel> preparedModel;
-    ASSERT_NO_FATAL_FAILURE(doPrepareModelShortcut(device, &preparedModel));
-    if (preparedModel == nullptr) {
-        return;
-    }
-    Request request = createValidTestRequest();
-
-    auto postWork = [&] {
-        sp<IMemory> outputMemory = mapMemory(request.pools[OUTPUT]);
-        if (outputMemory == nullptr) {
-            return false;
-        }
-        float* outputPtr = reinterpret_cast<float*>(static_cast<void*>(outputMemory->getPointer()));
-        if (outputPtr == nullptr) {
-            return false;
-        }
-        outputMemory->read();
-        std::copy(outputPtr, outputPtr + outputData.size(), outputData.begin());
-        outputMemory->commit();
-        return true;
-    };
-
-    sp<ExecutionCallback> executionCallback = new ExecutionCallback();
-    ASSERT_NE(nullptr, executionCallback.get());
-    executionCallback->on_finish(postWork);
-    Return<ErrorStatus> executeLaunchStatus = preparedModel->execute(request, executionCallback);
-    ASSERT_TRUE(executeLaunchStatus.isOk());
-    EXPECT_EQ(ErrorStatus::NONE, static_cast<ErrorStatus>(executeLaunchStatus));
-
-    executionCallback->wait();
-    ErrorStatus executionReturnStatus = executionCallback->getStatus();
-    EXPECT_EQ(ErrorStatus::NONE, executionReturnStatus);
-    EXPECT_EQ(expectedData, outputData);
-}
-
-// execute simple graph negative test 1
-TEST_F(NeuralnetworksHidlTest, SimpleExecuteGraphNegativeTest1) {
-    sp<IPreparedModel> preparedModel;
-    ASSERT_NO_FATAL_FAILURE(doPrepareModelShortcut(device, &preparedModel));
-    if (preparedModel == nullptr) {
-        return;
-    }
-    Request request = createInvalidTestRequest1();
-
-    sp<ExecutionCallback> executionCallback = new ExecutionCallback();
-    ASSERT_NE(nullptr, executionCallback.get());
-    Return<ErrorStatus> executeLaunchStatus = preparedModel->execute(request, executionCallback);
-    ASSERT_TRUE(executeLaunchStatus.isOk());
-    EXPECT_EQ(ErrorStatus::INVALID_ARGUMENT, static_cast<ErrorStatus>(executeLaunchStatus));
-
-    executionCallback->wait();
-    ErrorStatus executionReturnStatus = executionCallback->getStatus();
-    EXPECT_EQ(ErrorStatus::INVALID_ARGUMENT, executionReturnStatus);
-}
-
-// execute simple graph negative test 2
-TEST_F(NeuralnetworksHidlTest, SimpleExecuteGraphNegativeTest2) {
-    sp<IPreparedModel> preparedModel;
-    ASSERT_NO_FATAL_FAILURE(doPrepareModelShortcut(device, &preparedModel));
-    if (preparedModel == nullptr) {
-        return;
-    }
-    Request request = createInvalidTestRequest2();
-
-    sp<ExecutionCallback> executionCallback = new ExecutionCallback();
-    ASSERT_NE(nullptr, executionCallback.get());
-    Return<ErrorStatus> executeLaunchStatus = preparedModel->execute(request, executionCallback);
-    ASSERT_TRUE(executeLaunchStatus.isOk());
-    EXPECT_EQ(ErrorStatus::INVALID_ARGUMENT, static_cast<ErrorStatus>(executeLaunchStatus));
-
-    executionCallback->wait();
-    ErrorStatus executionReturnStatus = executionCallback->getStatus();
-    EXPECT_EQ(ErrorStatus::INVALID_ARGUMENT, executionReturnStatus);
-}
-
-}  // namespace functional
-}  // namespace vts
-}  // namespace V1_0
-}  // namespace neuralnetworks
-}  // namespace hardware
-}  // namespace android
-
-using android::hardware::neuralnetworks::V1_0::vts::functional::NeuralnetworksHidlEnvironment;
-
-int main(int argc, char** argv) {
-    ::testing::AddGlobalTestEnvironment(NeuralnetworksHidlEnvironment::getInstance());
-    ::testing::InitGoogleTest(&argc, argv);
-    NeuralnetworksHidlEnvironment::getInstance()->init(&argc, argv);
-
-    int status = RUN_ALL_TESTS();
-    return status;
-}
diff --git a/neuralnetworks/1.1/IDevice.hal b/neuralnetworks/1.1/IDevice.hal
index d2c4843..1335bde 100644
--- a/neuralnetworks/1.1/IDevice.hal
+++ b/neuralnetworks/1.1/IDevice.hal
@@ -102,6 +102,8 @@
      * Multiple threads can call prepareModel on the same model concurrently.
      *
      * @param model The model to be prepared for execution.
+     * @param preference Indicates the intended execution behavior of a prepared
+     *                   model.
      * @param 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
@@ -115,6 +117,7 @@
      *                - INVALID_ARGUMENT if one of the input arguments is
      *                  invalid
      */
-    prepareModel_1_1(Model model, IPreparedModelCallback callback)
+    prepareModel_1_1(Model model, ExecutionPreference preference,
+                     IPreparedModelCallback callback)
           generates (ErrorStatus status);
 };
diff --git a/neuralnetworks/1.1/types.hal b/neuralnetworks/1.1/types.hal
index 1d470d6..8290fbb 100644
--- a/neuralnetworks/1.1/types.hal
+++ b/neuralnetworks/1.1/types.hal
@@ -27,25 +27,24 @@
  */
 enum OperationType : @1.0::OperationType {
     /**
-     * BatchToSpace for N-D tensors.
+     * BatchToSpace for N-dimensional tensors.
      *
-     * This operation reshapes the "batch" dimension 0 into M + 1 dimensions of shape
+     * This operation reshapes the batch dimension (dimension 0) into M + 1 dimensions of shape
      * block_shape + [batch], interleaves these blocks back into the grid defined by the
      * spatial dimensions [1, ..., M], to obtain a result with the same rank as the input.
-     * The spatial dimensions of this intermediate result are then optionally cropped
-     * according to the amount to crop to produce the output.
+     *
      * This is the reverse of SpaceToBatch.
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
-     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
-     * Supported tensor rank: up to 4
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
+     * Supported tensor rank: 4
      *
      * Inputs:
-     * 0: An n-D tensor, specifying the input.
+     * 0: An n-D tensor, specifying the tensor to be reshaped
      * 1: A 1-D Tensor of type TENSOR_INT32, the block sizes for each spatial dimension of the
      *    input tensor. All values must be >= 1.
-     * 2: A 1-D Tensor of type TENSOR_INT32, the amount to crop for each spatial diemension of the
-     *    input tensor. All values must be >= 0.
      *
      * Outputs:
      * 0: A tensor of the same type as input0.
@@ -53,9 +52,9 @@
     BATCH_TO_SPACE_ND = 29,
 
     /**
-     * Divides the second tensor from the first tensor, element-wise.
+     * Element-wise division of two tensors.
      *
-     * Takes two input tensors of identical OperandType and compatible dimensions. The output
+     * Takes two input tensors of identical type and compatible dimensions. The output
      * is the result of dividing the first input tensor by the second, optionally
      * modified by an activation function.
      *
@@ -71,7 +70,9 @@
      *     input2.dimension = {5, 4, 3, 1}
      *     output.dimension = {5, 4, 3, 2}
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     *
      * Supported tensor rank: up to 4
      *
      * Inputs:
@@ -88,15 +89,17 @@
     /**
      * Computes the mean of elements across dimensions of a tensor.
      *
-     * Reduces input tensor along the dimensions given in axis. Unless keep_dims is true,
-     * the rank of the tensor is reduced by 1 for each entry in axis. If keep_dims is
-     * true, the reduced dimensions are retained with length 1.
+     * Reduces the input tensor along the given dimensions to reduce. Unless keep_dims
+     * is true, the rank of the tensor is reduced by 1 for each entry in axis.
+     * If keep_dims is true, the reduced dimensions are retained with length 1.
      *
-     * If axis has no entries, all dimensions are reduced, and a tensor with a single
-     * element is returned.
+     * If dimensions to reduce have no entries, all dimensions are reduced, and a tensor with
+     * a single element is returned.
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
-     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
      * Supported tensor rank: up to 4
      *
      * Inputs:
@@ -115,14 +118,18 @@
      *
      * This operation pads a tensor according to the specified paddings.
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
-     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
      * Supported tensor rank: up to 4
      *
      * Inputs:
-     * 0: An n-D tensor, specifying the input.
-     * 1: A 2-D Tensor of type TENSOR_INT32. The paddings, before and after for each spatial dimension
-     *    of the input tensor.
+     * 0: An n-D tensor, specifying the tensor to be padded.
+     * 1: A 2-D Tensor of type TENSOR_INT32, the paddings for each spatial dimension of the
+     *    input tensor. The shape of the tensor must be {rank(input0), 2}.
+     *    padding[i, 0] specifies the number of element to be padded in the front of dimension i.
+     *    padding[i, 1] specifies the number of element to be padded after the end of dimension i.
      *
      * Outputs:
      * 0: A tensor of the same type as input0.
@@ -130,7 +137,7 @@
     PAD = 32,
 
     /**
-     * SpaceToBatch for N-D tensors.
+     * SpaceToBatch for N-Dimensional tensors.
      *
      * This operation divides "spatial" dimensions [1, ..., M] of the input into a grid of blocks
      * of shape block_shape, and interleaves these blocks with the "batch" dimension (0) such that
@@ -139,16 +146,20 @@
      * batch position. Prior to division into blocks, the spatial dimensions of the input are
      * optionally zero padded according to paddings.
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
-     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
-     * Supported tensor rank: up to 4
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
+     * Supported tensor rank: 4
      *
      * Inputs:
      * 0: An n-D tensor, specifying the input.
      * 1: A 1-D Tensor of type TENSOR_INT32, the block sizes for each spatial dimension of the
      *    input tensor. All values must be >= 1.
      * 2: A 2-D Tensor of type TENSOR_INT32, the paddings for each spatial diemension of the
-     *    input tensor. All values must be >= 0.
+     *    input tensor. All values must be >= 0. The shape of the tensor must be {rank(input0), 2}.
+     *    padding[i, 0] specifies the number of element to be padded in the front of dimension i.
+     *    padding[i, 1] specifies the number of element to be padded after the end of dimension i.
      *
      * Outputs:
      * 0: A tensor of the same type as input0.
@@ -160,17 +171,20 @@
      *
      * Given a tensor input, this operation returns a tensor of the same type with all
      * dimensions of size 1 removed. If you don't want to remove all size 1 dimensions,
-     * you can remove specific size 1 dimensions by specifying axis.
+     * you can remove specific size 1 dimensions by specifying the axes (input1).
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
-     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
      * Supported tensor rank: up to 4
      *
      * Inputs:
-     * 0: An n-D tensor, specifying the input.
-     * 1: An 1-D Tensor of type TENSOR_INT32. The dimensions to squeeze. If None (the default),
-     *    squeezes all dimensions. If specified, only squeezes the dimensions listed. The dimension
-     *    index starts at 0. It is an error to squeeze a dimension that is not 1.
+     * 0: An n-D tensor, the tensor to be squeezed.
+     * 1: An optional 1-D tensor of type TENSOR_INT32. The dimensions to squeeze. If specified
+     *    only squeezes the dimensions listed. Otherwise, squeezes all dimensions.
+     *    The dimension index starts at 0. An error must be reported if squeezing a dimension that
+     *    is not 1.
      *
      * Outputs:
      * 0: A tensor of the same type as input0. Contains the same data as input, but has one or more
@@ -181,23 +195,25 @@
     /**
      * Extracts a strided slice of a tensor.
      *
-     * This op extracts a slice of size (end-begin)/stride from the given input tensor.
-    *  Starting at the location specified by begin the slice continues by adding
+     * Roughly speaking, this op extracts a slice of size (end - begin) / stride from the given
+     * input tensor. Starting at the location specified by begin the slice continues by adding
      * stride to the index until all dimensions are not less than end. Note that a stride can
      * be negative, which causes a reverse slice.
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
-     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
      * Supported tensor rank: up to 4
      *
      * Inputs:
-     * 0: An n-D tensor, specifying the input.
+     * 0: An n-D tensor, specifying the tensor to be sliced.
      * 1: A 1-D Tensor of type TENSOR_INT32, the starts of the dimensions of the input
-     *    tensor to be sliced.
+     *    tensor to be sliced. The length must be of rank(input0).
      * 2: A 1-D Tensor of type TENSOR_INT32, the ends of the dimensions of the input
-     *    tensor to be sliced.
+     *    tensor to be sliced. The length must be of rank(input0).
      * 3: A 1-D Tensor of type TENSOR_INT32, the strides of the dimensions of the input
-     *    tensor to be sliced.
+     *    tensor to be sliced. The length must be of rank(input0).
      *
      * Outputs:
      * 0: A tensor of the same type as input0.
@@ -205,7 +221,7 @@
     STRIDED_SLICE = 35,
 
     /**
-     * Subtracts the second tensor from the first tensor, element-wise.
+     * Element-wise subtraction of two tensors.
      *
      * Takes two input tensors of identical type and compatible dimensions. The output
      * is the result of subtracting the second input tensor from the first one, optionally
@@ -223,7 +239,9 @@
      *     input2.dimension = {5, 4, 3, 1}
      *     output.dimension = {5, 4, 3, 2}
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     *
      * Supported tensor rank: up to 4
      *
      * Inputs:
@@ -240,18 +258,20 @@
     /**
      * Transposes the input tensor, permuting the dimensions according to the perm tensor.
      *
-     * The returned tensor's dimension i must correspond to the input dimension perm[i].
+     * The returned tensor's dimension i corresponds to the input dimension perm[i].
      * If perm is not given, it is set to (n-1...0), where n is the rank of the input tensor.
      * Hence by default, this operation performs a regular matrix transpose on 2-D input Tensors.
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
-     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
      * Supported tensor rank: up to 4
      *
      * Inputs:
-     * 0: An n-D tensor, specifying the input.
-     * 1: A 1-D Tensor of type TENSOR_INT32, the permutation of the dimensions of the input
-     *    tensor.
+     * 0: An n-D tensor, specifying the tensor to be transposed.
+     * 1: An optional 1-D Tensor of type TENSOR_INT32, the permutation of the dimensions of the
+     *    input tensor.
      *
      * Outputs:
      * 0: A tensor of the same type as input0.
@@ -362,3 +382,24 @@
      */
     bool relaxComputationFloat32toFloat16;
 };
+
+/**
+ * Execution preferences.
+ */
+enum ExecutionPreference : int32_t {
+    /**
+     * Prefer executing in a way that minimizes battery drain.
+     * This is desirable for compilations that will be executed often.
+     */
+    LOW_POWER = 0,
+    /**
+     * Prefer returning a single answer as fast as possible, even if this causes
+     * more power consumption.
+     */
+    FAST_SINGLE_ANSWER = 1,
+    /**
+     * Prefer maximizing the throughput of successive frames, for example when
+     * processing successive frames coming from the camera.
+     */
+    SUSTAINED_SPEED = 2,
+};
diff --git a/neuralnetworks/1.1/vts/functional/Android.bp b/neuralnetworks/1.1/vts/functional/Android.bp
index 623b441..f755c20 100644
--- a/neuralnetworks/1.1/vts/functional/Android.bp
+++ b/neuralnetworks/1.1/vts/functional/Android.bp
@@ -17,9 +17,12 @@
 cc_test {
     name: "VtsHalNeuralnetworksV1_1TargetTest",
     srcs: [
-        "VtsHalNeuralnetworksV1_1.cpp",
-        "VtsHalNeuralnetworksV1_1BasicTest.cpp",
-        "VtsHalNeuralnetworksV1_1GeneratedTest.cpp",
+        "BasicTests.cpp",
+        "GeneratedTests.cpp",
+        "ValidateModel.cpp",
+        "ValidateRequest.cpp",
+        "ValidationTests.cpp",
+        "VtsHalNeuralnetworks.cpp",
     ],
     defaults: ["VtsHalTargetTestDefaults"],
     static_libs: [
@@ -36,4 +39,13 @@
         "libneuralnetworks_generated_test_harness_headers",
         "libneuralnetworks_generated_tests",
     ],
+    // Bug: http://b/74200014 - Disable arm32 asan since it triggers internal
+    // error in ld.gold.
+    arch: {
+        arm: {
+            sanitize: {
+                never: true,
+            },
+        },
+    },
 }
diff --git a/neuralnetworks/1.1/vts/functional/BasicTests.cpp b/neuralnetworks/1.1/vts/functional/BasicTests.cpp
new file mode 100644
index 0000000..ed59a2d
--- /dev/null
+++ b/neuralnetworks/1.1/vts/functional/BasicTests.cpp
@@ -0,0 +1,58 @@
+/*
+ * Copyright (C) 2018 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 "neuralnetworks_hidl_hal_test"
+
+#include "VtsHalNeuralnetworks.h"
+
+namespace android {
+namespace hardware {
+namespace neuralnetworks {
+namespace V1_1 {
+namespace vts {
+namespace functional {
+
+// create device test
+TEST_F(NeuralnetworksHidlTest, CreateDevice) {}
+
+// status test
+TEST_F(NeuralnetworksHidlTest, StatusTest) {
+    Return<DeviceStatus> status = device->getStatus();
+    ASSERT_TRUE(status.isOk());
+    EXPECT_EQ(DeviceStatus::AVAILABLE, static_cast<DeviceStatus>(status));
+}
+
+// initialization
+TEST_F(NeuralnetworksHidlTest, GetCapabilitiesTest) {
+    Return<void> ret =
+        device->getCapabilities_1_1([](ErrorStatus status, const Capabilities& capabilities) {
+            EXPECT_EQ(ErrorStatus::NONE, status);
+            EXPECT_LT(0.0f, capabilities.float32Performance.execTime);
+            EXPECT_LT(0.0f, capabilities.float32Performance.powerUsage);
+            EXPECT_LT(0.0f, capabilities.quantized8Performance.execTime);
+            EXPECT_LT(0.0f, capabilities.quantized8Performance.powerUsage);
+            EXPECT_LT(0.0f, capabilities.relaxedFloat32toFloat16Performance.execTime);
+            EXPECT_LT(0.0f, capabilities.relaxedFloat32toFloat16Performance.powerUsage);
+        });
+    EXPECT_TRUE(ret.isOk());
+}
+
+}  // namespace functional
+}  // namespace vts
+}  // namespace V1_1
+}  // namespace neuralnetworks
+}  // namespace hardware
+}  // namespace android
diff --git a/neuralnetworks/1.1/vts/functional/GeneratedTests.cpp b/neuralnetworks/1.1/vts/functional/GeneratedTests.cpp
new file mode 100644
index 0000000..1f1cc7a
--- /dev/null
+++ b/neuralnetworks/1.1/vts/functional/GeneratedTests.cpp
@@ -0,0 +1,59 @@
+/*
+ * Copyright (C) 2018 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 "neuralnetworks_hidl_hal_test"
+
+#include "VtsHalNeuralnetworks.h"
+
+#include "Callbacks.h"
+#include "TestHarness.h"
+#include "Utils.h"
+
+#include <android-base/logging.h>
+#include <android/hidl/memory/1.0/IMemory.h>
+#include <hidlmemory/mapping.h>
+
+namespace android {
+namespace hardware {
+namespace neuralnetworks {
+
+namespace generated_tests {
+using ::generated_tests::MixedTypedExampleType;
+extern void Execute(const sp<V1_1::IDevice>&, std::function<V1_1::Model(void)>,
+                    std::function<bool(int)>, const std::vector<MixedTypedExampleType>&);
+}  // namespace generated_tests
+
+namespace V1_1 {
+namespace vts {
+namespace functional {
+
+using ::android::hardware::neuralnetworks::V1_0::implementation::ExecutionCallback;
+using ::android::hardware::neuralnetworks::V1_0::implementation::PreparedModelCallback;
+using ::android::nn::allocateSharedMemory;
+
+// Mixed-typed examples
+typedef generated_tests::MixedTypedExampleType MixedTypedExample;
+
+// in frameworks/ml/nn/runtime/tests/generated/
+#include "all_generated_V1_0_vts_tests.cpp"
+#include "all_generated_V1_1_vts_tests.cpp"
+
+}  // namespace functional
+}  // namespace vts
+}  // namespace V1_1
+}  // namespace neuralnetworks
+}  // namespace hardware
+}  // namespace android
diff --git a/neuralnetworks/1.1/vts/functional/Models.h b/neuralnetworks/1.1/vts/functional/Models.h
new file mode 100644
index 0000000..c3cadb5
--- /dev/null
+++ b/neuralnetworks/1.1/vts/functional/Models.h
@@ -0,0 +1,323 @@
+/*
+ * Copyright (C) 2018 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef VTS_HAL_NEURALNETWORKS_V1_1_VTS_FUNCTIONAL_MODELS_H
+#define VTS_HAL_NEURALNETWORKS_V1_1_VTS_FUNCTIONAL_MODELS_H
+
+#define LOG_TAG "neuralnetworks_hidl_hal_test"
+
+#include "TestHarness.h"
+
+#include <android/hardware/neuralnetworks/1.0/types.h>
+#include <android/hardware/neuralnetworks/1.1/types.h>
+
+namespace android {
+namespace hardware {
+namespace neuralnetworks {
+namespace V1_1 {
+namespace vts {
+namespace functional {
+
+using MixedTypedExample = generated_tests::MixedTypedExampleType;
+
+#define FOR_EACH_TEST_MODEL(FN)                                \
+    FN(add)                                                    \
+    FN(add_broadcast_quant8)                                   \
+    FN(add_quant8)                                             \
+    FN(add_relaxed)                                            \
+    FN(avg_pool_float_1)                                       \
+    FN(avg_pool_float_1_relaxed)                               \
+    FN(avg_pool_float_2)                                       \
+    FN(avg_pool_float_2_relaxed)                               \
+    FN(avg_pool_float_3)                                       \
+    FN(avg_pool_float_3_relaxed)                               \
+    FN(avg_pool_float_4)                                       \
+    FN(avg_pool_float_4_relaxed)                               \
+    FN(avg_pool_float_5)                                       \
+    FN(avg_pool_quant8_1)                                      \
+    FN(avg_pool_quant8_2)                                      \
+    FN(avg_pool_quant8_3)                                      \
+    FN(avg_pool_quant8_4)                                      \
+    FN(avg_pool_quant8_5)                                      \
+    FN(batch_to_space)                                         \
+    FN(batch_to_space_float_1)                                 \
+    FN(batch_to_space_quant8_1)                                \
+    FN(concat_float_1)                                         \
+    FN(concat_float_1_relaxed)                                 \
+    FN(concat_float_2)                                         \
+    FN(concat_float_2_relaxed)                                 \
+    FN(concat_float_3)                                         \
+    FN(concat_float_3_relaxed)                                 \
+    FN(concat_quant8_1)                                        \
+    FN(concat_quant8_2)                                        \
+    FN(concat_quant8_3)                                        \
+    FN(conv_1_h3_w2_SAME)                                      \
+    FN(conv_1_h3_w2_SAME_relaxed)                              \
+    FN(conv_1_h3_w2_VALID)                                     \
+    FN(conv_1_h3_w2_VALID_relaxed)                             \
+    FN(conv_3_h3_w2_SAME)                                      \
+    FN(conv_3_h3_w2_SAME_relaxed)                              \
+    FN(conv_3_h3_w2_VALID)                                     \
+    FN(conv_3_h3_w2_VALID_relaxed)                             \
+    FN(conv_float)                                             \
+    FN(conv_float_2)                                           \
+    FN(conv_float_channels)                                    \
+    FN(conv_float_channels_relaxed)                            \
+    FN(conv_float_channels_weights_as_inputs)                  \
+    FN(conv_float_channels_weights_as_inputs_relaxed)          \
+    FN(conv_float_large)                                       \
+    FN(conv_float_large_relaxed)                               \
+    FN(conv_float_large_weights_as_inputs)                     \
+    FN(conv_float_large_weights_as_inputs_relaxed)             \
+    FN(conv_float_relaxed)                                     \
+    FN(conv_float_weights_as_inputs)                           \
+    FN(conv_float_weights_as_inputs_relaxed)                   \
+    FN(conv_quant8)                                            \
+    FN(conv_quant8_2)                                          \
+    FN(conv_quant8_channels)                                   \
+    FN(conv_quant8_channels_weights_as_inputs)                 \
+    FN(conv_quant8_large)                                      \
+    FN(conv_quant8_large_weights_as_inputs)                    \
+    FN(conv_quant8_overflow)                                   \
+    FN(conv_quant8_overflow_weights_as_inputs)                 \
+    FN(conv_quant8_weights_as_inputs)                          \
+    FN(depth_to_space_float_1)                                 \
+    FN(depth_to_space_float_1_relaxed)                         \
+    FN(depth_to_space_float_2)                                 \
+    FN(depth_to_space_float_2_relaxed)                         \
+    FN(depth_to_space_float_3)                                 \
+    FN(depth_to_space_float_3_relaxed)                         \
+    FN(depth_to_space_quant8_1)                                \
+    FN(depth_to_space_quant8_2)                                \
+    FN(depthwise_conv)                                         \
+    FN(depthwise_conv2d_float)                                 \
+    FN(depthwise_conv2d_float_2)                               \
+    FN(depthwise_conv2d_float_large)                           \
+    FN(depthwise_conv2d_float_large_2)                         \
+    FN(depthwise_conv2d_float_large_2_weights_as_inputs)       \
+    FN(depthwise_conv2d_float_large_relaxed)                   \
+    FN(depthwise_conv2d_float_large_weights_as_inputs)         \
+    FN(depthwise_conv2d_float_large_weights_as_inputs_relaxed) \
+    FN(depthwise_conv2d_float_weights_as_inputs)               \
+    FN(depthwise_conv2d_quant8)                                \
+    FN(depthwise_conv2d_quant8_2)                              \
+    FN(depthwise_conv2d_quant8_large)                          \
+    FN(depthwise_conv2d_quant8_large_weights_as_inputs)        \
+    FN(depthwise_conv2d_quant8_weights_as_inputs)              \
+    FN(depthwise_conv_relaxed)                                 \
+    FN(dequantize)                                             \
+    FN(div)                                                    \
+    FN(embedding_lookup)                                       \
+    FN(embedding_lookup_relaxed)                               \
+    FN(floor)                                                  \
+    FN(floor_relaxed)                                          \
+    FN(fully_connected_float)                                  \
+    FN(fully_connected_float_2)                                \
+    FN(fully_connected_float_large)                            \
+    FN(fully_connected_float_large_weights_as_inputs)          \
+    FN(fully_connected_float_relaxed)                          \
+    FN(fully_connected_float_weights_as_inputs)                \
+    FN(fully_connected_float_weights_as_inputs_relaxed)        \
+    FN(fully_connected_quant8)                                 \
+    FN(fully_connected_quant8_2)                               \
+    FN(fully_connected_quant8_large)                           \
+    FN(fully_connected_quant8_large_weights_as_inputs)         \
+    FN(fully_connected_quant8_weights_as_inputs)               \
+    FN(hashtable_lookup_float)                                 \
+    FN(hashtable_lookup_float_relaxed)                         \
+    FN(hashtable_lookup_quant8)                                \
+    FN(l2_normalization)                                       \
+    FN(l2_normalization_2)                                     \
+    FN(l2_normalization_large)                                 \
+    FN(l2_normalization_large_relaxed)                         \
+    FN(l2_normalization_relaxed)                               \
+    FN(l2_pool_float)                                          \
+    FN(l2_pool_float_2)                                        \
+    FN(l2_pool_float_large)                                    \
+    FN(l2_pool_float_relaxed)                                  \
+    FN(local_response_norm_float_1)                            \
+    FN(local_response_norm_float_1_relaxed)                    \
+    FN(local_response_norm_float_2)                            \
+    FN(local_response_norm_float_2_relaxed)                    \
+    FN(local_response_norm_float_3)                            \
+    FN(local_response_norm_float_3_relaxed)                    \
+    FN(local_response_norm_float_4)                            \
+    FN(local_response_norm_float_4_relaxed)                    \
+    FN(logistic_float_1)                                       \
+    FN(logistic_float_1_relaxed)                               \
+    FN(logistic_float_2)                                       \
+    FN(logistic_float_2_relaxed)                               \
+    FN(logistic_quant8_1)                                      \
+    FN(logistic_quant8_2)                                      \
+    FN(lsh_projection)                                         \
+    FN(lsh_projection_2)                                       \
+    FN(lsh_projection_2_relaxed)                               \
+    FN(lsh_projection_relaxed)                                 \
+    FN(lsh_projection_weights_as_inputs)                       \
+    FN(lsh_projection_weights_as_inputs_relaxed)               \
+    FN(lstm)                                                   \
+    FN(lstm2)                                                  \
+    FN(lstm2_relaxed)                                          \
+    FN(lstm2_state)                                            \
+    FN(lstm2_state2)                                           \
+    FN(lstm2_state2_relaxed)                                   \
+    FN(lstm2_state_relaxed)                                    \
+    FN(lstm3)                                                  \
+    FN(lstm3_relaxed)                                          \
+    FN(lstm3_state)                                            \
+    FN(lstm3_state2)                                           \
+    FN(lstm3_state2_relaxed)                                   \
+    FN(lstm3_state3)                                           \
+    FN(lstm3_state3_relaxed)                                   \
+    FN(lstm3_state_relaxed)                                    \
+    FN(lstm_relaxed)                                           \
+    FN(lstm_state)                                             \
+    FN(lstm_state2)                                            \
+    FN(lstm_state2_relaxed)                                    \
+    FN(lstm_state_relaxed)                                     \
+    FN(max_pool_float_1)                                       \
+    FN(max_pool_float_1_relaxed)                               \
+    FN(max_pool_float_2)                                       \
+    FN(max_pool_float_2_relaxed)                               \
+    FN(max_pool_float_3)                                       \
+    FN(max_pool_float_3_relaxed)                               \
+    FN(max_pool_float_4)                                       \
+    FN(max_pool_quant8_1)                                      \
+    FN(max_pool_quant8_2)                                      \
+    FN(max_pool_quant8_3)                                      \
+    FN(max_pool_quant8_4)                                      \
+    FN(mean)                                                   \
+    FN(mean_float_1)                                           \
+    FN(mean_float_2)                                           \
+    FN(mean_quant8_1)                                          \
+    FN(mean_quant8_2)                                          \
+    FN(mobilenet_224_gender_basic_fixed)                       \
+    FN(mobilenet_224_gender_basic_fixed_relaxed)               \
+    FN(mobilenet_quantized)                                    \
+    FN(mul)                                                    \
+    FN(mul_broadcast_quant8)                                   \
+    FN(mul_quant8)                                             \
+    FN(mul_relaxed)                                            \
+    FN(mul_relu)                                               \
+    FN(mul_relu_relaxed)                                       \
+    FN(pad)                                                    \
+    FN(pad_float_1)                                            \
+    FN(relu1_float_1)                                          \
+    FN(relu1_float_1_relaxed)                                  \
+    FN(relu1_float_2)                                          \
+    FN(relu1_float_2_relaxed)                                  \
+    FN(relu1_quant8_1)                                         \
+    FN(relu1_quant8_2)                                         \
+    FN(relu6_float_1)                                          \
+    FN(relu6_float_1_relaxed)                                  \
+    FN(relu6_float_2)                                          \
+    FN(relu6_float_2_relaxed)                                  \
+    FN(relu6_quant8_1)                                         \
+    FN(relu6_quant8_2)                                         \
+    FN(relu_float_1)                                           \
+    FN(relu_float_1_relaxed)                                   \
+    FN(relu_float_2)                                           \
+    FN(relu_quant8_1)                                          \
+    FN(relu_quant8_2)                                          \
+    FN(reshape)                                                \
+    FN(reshape_quant8)                                         \
+    FN(reshape_quant8_weights_as_inputs)                       \
+    FN(reshape_relaxed)                                        \
+    FN(reshape_weights_as_inputs)                              \
+    FN(reshape_weights_as_inputs_relaxed)                      \
+    FN(resize_bilinear)                                        \
+    FN(resize_bilinear_2)                                      \
+    FN(resize_bilinear_relaxed)                                \
+    FN(rnn)                                                    \
+    FN(rnn_relaxed)                                            \
+    FN(rnn_state)                                              \
+    FN(rnn_state_relaxed)                                      \
+    FN(softmax_float_1)                                        \
+    FN(softmax_float_1_relaxed)                                \
+    FN(softmax_float_2)                                        \
+    FN(softmax_float_2_relaxed)                                \
+    FN(softmax_quant8_1)                                       \
+    FN(softmax_quant8_2)                                       \
+    FN(space_to_batch)                                         \
+    FN(space_to_batch_float_1)                                 \
+    FN(space_to_batch_float_2)                                 \
+    FN(space_to_batch_float_3)                                 \
+    FN(space_to_batch_quant8_1)                                \
+    FN(space_to_batch_quant8_2)                                \
+    FN(space_to_batch_quant8_3)                                \
+    FN(space_to_depth_float_1)                                 \
+    FN(space_to_depth_float_1_relaxed)                         \
+    FN(space_to_depth_float_2)                                 \
+    FN(space_to_depth_float_2_relaxed)                         \
+    FN(space_to_depth_float_3)                                 \
+    FN(space_to_depth_float_3_relaxed)                         \
+    FN(space_to_depth_quant8_1)                                \
+    FN(space_to_depth_quant8_2)                                \
+    FN(squeeze)                                                \
+    FN(squeeze_float_1)                                        \
+    FN(squeeze_quant8_1)                                       \
+    FN(strided_slice)                                          \
+    FN(strided_slice_float_1)                                  \
+    FN(strided_slice_float_10)                                 \
+    FN(strided_slice_float_2)                                  \
+    FN(strided_slice_float_3)                                  \
+    FN(strided_slice_float_4)                                  \
+    FN(strided_slice_float_5)                                  \
+    FN(strided_slice_float_6)                                  \
+    FN(strided_slice_float_7)                                  \
+    FN(strided_slice_float_8)                                  \
+    FN(strided_slice_float_9)                                  \
+    FN(strided_slice_qaunt8_10)                                \
+    FN(strided_slice_quant8_1)                                 \
+    FN(strided_slice_quant8_2)                                 \
+    FN(strided_slice_quant8_3)                                 \
+    FN(strided_slice_quant8_4)                                 \
+    FN(strided_slice_quant8_5)                                 \
+    FN(strided_slice_quant8_6)                                 \
+    FN(strided_slice_quant8_7)                                 \
+    FN(strided_slice_quant8_8)                                 \
+    FN(strided_slice_quant8_9)                                 \
+    FN(sub)                                                    \
+    FN(svdf)                                                   \
+    FN(svdf2)                                                  \
+    FN(svdf2_relaxed)                                          \
+    FN(svdf_relaxed)                                           \
+    FN(svdf_state)                                             \
+    FN(svdf_state_relaxed)                                     \
+    FN(tanh)                                                   \
+    FN(tanh_relaxed)                                           \
+    FN(transpose)                                              \
+    FN(transpose_float_1)                                      \
+    FN(transpose_quant8_1)
+
+#define FORWARD_DECLARE_GENERATED_OBJECTS(function) \
+    namespace function {                            \
+    extern std::vector<MixedTypedExample> examples; \
+    Model createTestModel();                        \
+    }
+
+FOR_EACH_TEST_MODEL(FORWARD_DECLARE_GENERATED_OBJECTS)
+
+#undef FORWARD_DECLARE_GENERATED_OBJECTS
+
+}  // namespace functional
+}  // namespace vts
+}  // namespace V1_1
+}  // namespace neuralnetworks
+}  // namespace hardware
+}  // namespace android
+
+#endif  // VTS_HAL_NEURALNETWORKS_V1_1_VTS_FUNCTIONAL_MODELS_H
diff --git a/neuralnetworks/1.1/vts/functional/ValidateModel.cpp b/neuralnetworks/1.1/vts/functional/ValidateModel.cpp
new file mode 100644
index 0000000..3aa55f8
--- /dev/null
+++ b/neuralnetworks/1.1/vts/functional/ValidateModel.cpp
@@ -0,0 +1,539 @@
+/*
+ * Copyright (C) 2018 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 "neuralnetworks_hidl_hal_test"
+
+#include "VtsHalNeuralnetworks.h"
+
+#include "Callbacks.h"
+
+namespace android {
+namespace hardware {
+namespace neuralnetworks {
+namespace V1_1 {
+
+using V1_0::IPreparedModel;
+using V1_0::Operand;
+using V1_0::OperandLifeTime;
+using V1_0::OperandType;
+
+namespace vts {
+namespace functional {
+
+using ::android::hardware::neuralnetworks::V1_0::implementation::ExecutionCallback;
+using ::android::hardware::neuralnetworks::V1_0::implementation::PreparedModelCallback;
+
+///////////////////////// UTILITY FUNCTIONS /////////////////////////
+
+static void validateGetSupportedOperations(const sp<IDevice>& device, const std::string& message,
+                                           const V1_1::Model& model) {
+    SCOPED_TRACE(message + " [getSupportedOperations_1_1]");
+
+    Return<void> ret =
+        device->getSupportedOperations_1_1(model, [&](ErrorStatus status, const hidl_vec<bool>&) {
+            EXPECT_EQ(ErrorStatus::INVALID_ARGUMENT, status);
+        });
+    EXPECT_TRUE(ret.isOk());
+}
+
+static void validatePrepareModel(const sp<IDevice>& device, const std::string& message,
+                                 const V1_1::Model& model, ExecutionPreference preference) {
+    SCOPED_TRACE(message + " [prepareModel_1_1]");
+
+    sp<PreparedModelCallback> preparedModelCallback = new PreparedModelCallback();
+    ASSERT_NE(nullptr, preparedModelCallback.get());
+    Return<ErrorStatus> prepareLaunchStatus =
+        device->prepareModel_1_1(model, preference, preparedModelCallback);
+    ASSERT_TRUE(prepareLaunchStatus.isOk());
+    ASSERT_EQ(ErrorStatus::INVALID_ARGUMENT, static_cast<ErrorStatus>(prepareLaunchStatus));
+
+    preparedModelCallback->wait();
+    ErrorStatus prepareReturnStatus = preparedModelCallback->getStatus();
+    ASSERT_EQ(ErrorStatus::INVALID_ARGUMENT, prepareReturnStatus);
+    sp<IPreparedModel> preparedModel = preparedModelCallback->getPreparedModel();
+    ASSERT_EQ(nullptr, preparedModel.get());
+}
+
+static bool validExecutionPreference(ExecutionPreference preference) {
+    return preference == ExecutionPreference::LOW_POWER ||
+           preference == ExecutionPreference::FAST_SINGLE_ANSWER ||
+           preference == ExecutionPreference::SUSTAINED_SPEED;
+}
+
+// Primary validation function. This function will take a valid model, apply a
+// mutation to it to invalidate the model, then pass it to interface calls that
+// use the model. Note that the model here is passed by value, and any mutation
+// to the model does not leave this function.
+static void validate(const sp<IDevice>& device, const std::string& message, V1_1::Model model,
+                     const std::function<void(Model*)>& mutation,
+                     ExecutionPreference preference = ExecutionPreference::FAST_SINGLE_ANSWER) {
+    mutation(&model);
+    if (validExecutionPreference(preference)) {
+        validateGetSupportedOperations(device, message, model);
+    }
+    validatePrepareModel(device, message, model, preference);
+}
+
+// Delete element from hidl_vec. hidl_vec doesn't support a "remove" operation,
+// so this is efficiently accomplished by moving the element to the end and
+// resizing the hidl_vec to one less.
+template <typename Type>
+static void hidl_vec_removeAt(hidl_vec<Type>* vec, uint32_t index) {
+    if (vec) {
+        std::rotate(vec->begin() + index, vec->begin() + index + 1, vec->end());
+        vec->resize(vec->size() - 1);
+    }
+}
+
+template <typename Type>
+static uint32_t hidl_vec_push_back(hidl_vec<Type>* vec, const Type& value) {
+    // assume vec is valid
+    const uint32_t index = vec->size();
+    vec->resize(index + 1);
+    (*vec)[index] = value;
+    return index;
+}
+
+static uint32_t addOperand(Model* model) {
+    return hidl_vec_push_back(&model->operands,
+                              {
+                                  .type = OperandType::INT32,
+                                  .dimensions = {},
+                                  .numberOfConsumers = 0,
+                                  .scale = 0.0f,
+                                  .zeroPoint = 0,
+                                  .lifetime = OperandLifeTime::MODEL_INPUT,
+                                  .location = {.poolIndex = 0, .offset = 0, .length = 0},
+                              });
+}
+
+static uint32_t addOperand(Model* model, OperandLifeTime lifetime) {
+    uint32_t index = addOperand(model);
+    model->operands[index].numberOfConsumers = 1;
+    model->operands[index].lifetime = lifetime;
+    return index;
+}
+
+///////////////////////// 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 void mutateOperandTypeTest(const sp<IDevice>& device, const V1_1::Model& model) {
+    for (size_t operand = 0; operand < model.operands.size(); ++operand) {
+        for (int32_t invalidOperandType : invalidOperandTypes) {
+            const std::string message = "mutateOperandTypeTest: operand " +
+                                        std::to_string(operand) + " set to value " +
+                                        std::to_string(invalidOperandType);
+            validate(device, message, model, [operand, invalidOperandType](Model* model) {
+                model->operands[operand].type = static_cast<OperandType>(invalidOperandType);
+            });
+        }
+    }
+}
+
+///////////////////////// VALIDATE OPERAND RANK /////////////////////////
+
+static uint32_t getInvalidRank(OperandType type) {
+    switch (type) {
+        case OperandType::FLOAT32:
+        case OperandType::INT32:
+        case OperandType::UINT32:
+            return 1;
+        case OperandType::TENSOR_FLOAT32:
+        case OperandType::TENSOR_INT32:
+        case OperandType::TENSOR_QUANT8_ASYMM:
+            return 0;
+        default:
+            return 0;
+    }
+}
+
+static void mutateOperandRankTest(const sp<IDevice>& device, const V1_1::Model& model) {
+    for (size_t operand = 0; operand < model.operands.size(); ++operand) {
+        const uint32_t invalidRank = getInvalidRank(model.operands[operand].type);
+        const std::string message = "mutateOperandRankTest: operand " + std::to_string(operand) +
+                                    " has rank of " + std::to_string(invalidRank);
+        validate(device, message, model, [operand, invalidRank](Model* model) {
+            model->operands[operand].dimensions = std::vector<uint32_t>(invalidRank, 0);
+        });
+    }
+}
+
+///////////////////////// VALIDATE OPERAND SCALE /////////////////////////
+
+static float getInvalidScale(OperandType type) {
+    switch (type) {
+        case OperandType::FLOAT32:
+        case OperandType::INT32:
+        case OperandType::UINT32:
+        case OperandType::TENSOR_FLOAT32:
+            return 1.0f;
+        case OperandType::TENSOR_INT32:
+            return -1.0f;
+        case OperandType::TENSOR_QUANT8_ASYMM:
+            return 0.0f;
+        default:
+            return 0.0f;
+    }
+}
+
+static void mutateOperandScaleTest(const sp<IDevice>& device, const V1_1::Model& model) {
+    for (size_t operand = 0; operand < model.operands.size(); ++operand) {
+        const float invalidScale = getInvalidScale(model.operands[operand].type);
+        const std::string message = "mutateOperandScaleTest: operand " + std::to_string(operand) +
+                                    " has scale of " + std::to_string(invalidScale);
+        validate(device, message, model, [operand, invalidScale](Model* model) {
+            model->operands[operand].scale = invalidScale;
+        });
+    }
+}
+
+///////////////////////// VALIDATE OPERAND ZERO POINT /////////////////////////
+
+static std::vector<int32_t> getInvalidZeroPoints(OperandType type) {
+    switch (type) {
+        case OperandType::FLOAT32:
+        case OperandType::INT32:
+        case OperandType::UINT32:
+        case OperandType::TENSOR_FLOAT32:
+        case OperandType::TENSOR_INT32:
+            return {1};
+        case OperandType::TENSOR_QUANT8_ASYMM:
+            return {-1, 256};
+        default:
+            return {};
+    }
+}
+
+static void mutateOperandZeroPointTest(const sp<IDevice>& device, const V1_1::Model& model) {
+    for (size_t operand = 0; operand < model.operands.size(); ++operand) {
+        const std::vector<int32_t> invalidZeroPoints =
+            getInvalidZeroPoints(model.operands[operand].type);
+        for (int32_t invalidZeroPoint : invalidZeroPoints) {
+            const std::string message = "mutateOperandZeroPointTest: operand " +
+                                        std::to_string(operand) + " has zero point of " +
+                                        std::to_string(invalidZeroPoint);
+            validate(device, message, model, [operand, invalidZeroPoint](Model* model) {
+                model->operands[operand].zeroPoint = invalidZeroPoint;
+            });
+        }
+    }
+}
+
+///////////////////////// VALIDATE EXTRA ??? /////////////////////////
+
+// TODO: Operand::lifetime
+// TODO: Operand::location
+
+///////////////////////// VALIDATE OPERATION OPERAND TYPE /////////////////////////
+
+static void mutateOperand(Operand* operand, OperandType type) {
+    Operand newOperand = *operand;
+    newOperand.type = type;
+    switch (type) {
+        case OperandType::FLOAT32:
+        case OperandType::INT32:
+        case OperandType::UINT32:
+            newOperand.dimensions = hidl_vec<uint32_t>();
+            newOperand.scale = 0.0f;
+            newOperand.zeroPoint = 0;
+            break;
+        case OperandType::TENSOR_FLOAT32:
+            newOperand.dimensions =
+                operand->dimensions.size() > 0 ? operand->dimensions : hidl_vec<uint32_t>({1});
+            newOperand.scale = 0.0f;
+            newOperand.zeroPoint = 0;
+            break;
+        case OperandType::TENSOR_INT32:
+            newOperand.dimensions =
+                operand->dimensions.size() > 0 ? operand->dimensions : hidl_vec<uint32_t>({1});
+            newOperand.zeroPoint = 0;
+            break;
+        case OperandType::TENSOR_QUANT8_ASYMM:
+            newOperand.dimensions =
+                operand->dimensions.size() > 0 ? operand->dimensions : hidl_vec<uint32_t>({1});
+            newOperand.scale = operand->scale != 0.0f ? operand->scale : 1.0f;
+            break;
+        case OperandType::OEM:
+        case OperandType::TENSOR_OEM_BYTE:
+        default:
+            break;
+    }
+    *operand = newOperand;
+}
+
+static bool mutateOperationOperandTypeSkip(size_t operand, const V1_1::Model& model) {
+    // LSH_PROJECTION's second argument is allowed to have any type. This is the
+    // only operation that currently has a type that can be anything independent
+    // from any other type. Changing the operand type to any other type will
+    // result in a valid model for LSH_PROJECTION. If this is the case, skip the
+    // test.
+    for (const Operation& operation : model.operations) {
+        if (operation.type == OperationType::LSH_PROJECTION && operand == operation.inputs[1]) {
+            return true;
+        }
+    }
+    return false;
+}
+
+static void mutateOperationOperandTypeTest(const sp<IDevice>& device, const V1_1::Model& model) {
+    for (size_t operand = 0; operand < model.operands.size(); ++operand) {
+        if (mutateOperationOperandTypeSkip(operand, model)) {
+            continue;
+        }
+        for (OperandType invalidOperandType : hidl_enum_iterator<OperandType>{}) {
+            // Do not test OEM types
+            if (invalidOperandType == model.operands[operand].type ||
+                invalidOperandType == OperandType::OEM ||
+                invalidOperandType == OperandType::TENSOR_OEM_BYTE) {
+                continue;
+            }
+            const std::string message = "mutateOperationOperandTypeTest: operand " +
+                                        std::to_string(operand) + " set to type " +
+                                        toString(invalidOperandType);
+            validate(device, message, model, [operand, invalidOperandType](Model* model) {
+                mutateOperand(&model->operands[operand], invalidOperandType);
+            });
+        }
+    }
+}
+
+///////////////////////// VALIDATE MODEL OPERATION TYPE /////////////////////////
+
+static const int32_t invalidOperationTypes[] = {
+    static_cast<int32_t>(OperationType::ADD) - 1,            // lower bound fundamental
+    static_cast<int32_t>(OperationType::TRANSPOSE) + 1,      // upper bound fundamental
+    static_cast<int32_t>(OperationType::OEM_OPERATION) - 1,  // lower bound OEM
+    static_cast<int32_t>(OperationType::OEM_OPERATION) + 1,  // upper bound OEM
+};
+
+static void mutateOperationTypeTest(const sp<IDevice>& device, const V1_1::Model& model) {
+    for (size_t operation = 0; operation < model.operations.size(); ++operation) {
+        for (int32_t invalidOperationType : invalidOperationTypes) {
+            const std::string message = "mutateOperationTypeTest: operation " +
+                                        std::to_string(operation) + " set to value " +
+                                        std::to_string(invalidOperationType);
+            validate(device, message, model, [operation, invalidOperationType](Model* model) {
+                model->operations[operation].type =
+                    static_cast<OperationType>(invalidOperationType);
+            });
+        }
+    }
+}
+
+///////////////////////// VALIDATE MODEL OPERATION INPUT OPERAND INDEX /////////////////////////
+
+static void mutateOperationInputOperandIndexTest(const sp<IDevice>& device,
+                                                 const V1_1::Model& model) {
+    for (size_t operation = 0; operation < model.operations.size(); ++operation) {
+        const uint32_t invalidOperand = model.operands.size();
+        for (size_t input = 0; input < model.operations[operation].inputs.size(); ++input) {
+            const std::string message = "mutateOperationInputOperandIndexTest: operation " +
+                                        std::to_string(operation) + " input " +
+                                        std::to_string(input);
+            validate(device, message, model, [operation, input, invalidOperand](Model* model) {
+                model->operations[operation].inputs[input] = invalidOperand;
+            });
+        }
+    }
+}
+
+///////////////////////// VALIDATE MODEL OPERATION OUTPUT OPERAND INDEX /////////////////////////
+
+static void mutateOperationOutputOperandIndexTest(const sp<IDevice>& device,
+                                                  const V1_1::Model& model) {
+    for (size_t operation = 0; operation < model.operations.size(); ++operation) {
+        const uint32_t invalidOperand = model.operands.size();
+        for (size_t output = 0; output < model.operations[operation].outputs.size(); ++output) {
+            const std::string message = "mutateOperationOutputOperandIndexTest: operation " +
+                                        std::to_string(operation) + " output " +
+                                        std::to_string(output);
+            validate(device, message, model, [operation, output, invalidOperand](Model* model) {
+                model->operations[operation].outputs[output] = invalidOperand;
+            });
+        }
+    }
+}
+
+///////////////////////// REMOVE OPERAND FROM EVERYTHING /////////////////////////
+
+static void removeValueAndDecrementGreaterValues(hidl_vec<uint32_t>* vec, uint32_t value) {
+    if (vec) {
+        // remove elements matching "value"
+        auto last = std::remove(vec->begin(), vec->end(), value);
+        vec->resize(std::distance(vec->begin(), last));
+
+        // decrement elements exceeding "value"
+        std::transform(vec->begin(), vec->end(), vec->begin(),
+                       [value](uint32_t v) { return v > value ? v-- : v; });
+    }
+}
+
+static void removeOperand(Model* model, uint32_t index) {
+    hidl_vec_removeAt(&model->operands, index);
+    for (Operation& operation : model->operations) {
+        removeValueAndDecrementGreaterValues(&operation.inputs, index);
+        removeValueAndDecrementGreaterValues(&operation.outputs, index);
+    }
+    removeValueAndDecrementGreaterValues(&model->inputIndexes, index);
+    removeValueAndDecrementGreaterValues(&model->outputIndexes, index);
+}
+
+static void removeOperandTest(const sp<IDevice>& device, const V1_1::Model& model) {
+    for (size_t operand = 0; operand < model.operands.size(); ++operand) {
+        const std::string message = "removeOperandTest: operand " + std::to_string(operand);
+        validate(device, message, model,
+                 [operand](Model* model) { removeOperand(model, operand); });
+    }
+}
+
+///////////////////////// REMOVE OPERATION /////////////////////////
+
+static void removeOperation(Model* model, uint32_t index) {
+    for (uint32_t operand : model->operations[index].inputs) {
+        model->operands[operand].numberOfConsumers--;
+    }
+    hidl_vec_removeAt(&model->operations, index);
+}
+
+static void removeOperationTest(const sp<IDevice>& device, const V1_1::Model& model) {
+    for (size_t operation = 0; operation < model.operations.size(); ++operation) {
+        const std::string message = "removeOperationTest: operation " + std::to_string(operation);
+        validate(device, message, model,
+                 [operation](Model* model) { removeOperation(model, operation); });
+    }
+}
+
+///////////////////////// REMOVE OPERATION INPUT /////////////////////////
+
+static void removeOperationInputTest(const sp<IDevice>& device, const V1_1::Model& model) {
+    for (size_t operation = 0; operation < model.operations.size(); ++operation) {
+        for (size_t input = 0; input < model.operations[operation].inputs.size(); ++input) {
+            const V1_1::Operation& op = model.operations[operation];
+            // CONCATENATION has at least 2 inputs, with the last element being
+            // INT32. Skip this test if removing one of CONCATENATION's
+            // inputs still produces a valid model.
+            if (op.type == V1_1::OperationType::CONCATENATION && op.inputs.size() > 2 &&
+                input != op.inputs.size() - 1) {
+                continue;
+            }
+            const std::string message = "removeOperationInputTest: operation " +
+                                        std::to_string(operation) + ", input " +
+                                        std::to_string(input);
+            validate(device, message, model, [operation, input](Model* model) {
+                uint32_t operand = model->operations[operation].inputs[input];
+                model->operands[operand].numberOfConsumers--;
+                hidl_vec_removeAt(&model->operations[operation].inputs, input);
+            });
+        }
+    }
+}
+
+///////////////////////// REMOVE OPERATION OUTPUT /////////////////////////
+
+static void removeOperationOutputTest(const sp<IDevice>& device, const V1_1::Model& model) {
+    for (size_t operation = 0; operation < model.operations.size(); ++operation) {
+        for (size_t output = 0; output < model.operations[operation].outputs.size(); ++output) {
+            const std::string message = "removeOperationOutputTest: operation " +
+                                        std::to_string(operation) + ", output " +
+                                        std::to_string(output);
+            validate(device, message, model, [operation, output](Model* model) {
+                hidl_vec_removeAt(&model->operations[operation].outputs, output);
+            });
+        }
+    }
+}
+
+///////////////////////// MODEL VALIDATION /////////////////////////
+
+// TODO: remove model input
+// TODO: remove model output
+// TODO: add unused operation
+
+///////////////////////// ADD OPERATION INPUT /////////////////////////
+
+static void addOperationInputTest(const sp<IDevice>& device, const V1_1::Model& model) {
+    for (size_t operation = 0; operation < model.operations.size(); ++operation) {
+        const std::string message = "addOperationInputTest: operation " + std::to_string(operation);
+        validate(device, message, model, [operation](Model* model) {
+            uint32_t index = addOperand(model, OperandLifeTime::MODEL_INPUT);
+            hidl_vec_push_back(&model->operations[operation].inputs, index);
+            hidl_vec_push_back(&model->inputIndexes, index);
+        });
+    }
+}
+
+///////////////////////// ADD OPERATION OUTPUT /////////////////////////
+
+static void addOperationOutputTest(const sp<IDevice>& device, const V1_1::Model& model) {
+    for (size_t operation = 0; operation < model.operations.size(); ++operation) {
+        const std::string message =
+            "addOperationOutputTest: operation " + std::to_string(operation);
+        validate(device, message, model, [operation](Model* model) {
+            uint32_t index = addOperand(model, OperandLifeTime::MODEL_OUTPUT);
+            hidl_vec_push_back(&model->operations[operation].outputs, index);
+            hidl_vec_push_back(&model->outputIndexes, index);
+        });
+    }
+}
+
+///////////////////////// VALIDATE EXECUTION PREFERENCE /////////////////////////
+
+static const int32_t invalidExecutionPreferences[] = {
+    static_cast<int32_t>(ExecutionPreference::LOW_POWER) - 1,        // lower bound
+    static_cast<int32_t>(ExecutionPreference::SUSTAINED_SPEED) + 1,  // upper bound
+};
+
+static void mutateExecutionPreferenceTest(const sp<IDevice>& device, const V1_1::Model& model) {
+    for (int32_t preference : invalidExecutionPreferences) {
+        const std::string message =
+            "mutateExecutionPreferenceTest: preference " + std::to_string(preference);
+        validate(device, message, model, [](Model*) {},
+                 static_cast<ExecutionPreference>(preference));
+    }
+}
+
+////////////////////////// ENTRY POINT //////////////////////////////
+
+void ValidationTest::validateModel(const V1_1::Model& model) {
+    mutateOperandTypeTest(device, model);
+    mutateOperandRankTest(device, model);
+    mutateOperandScaleTest(device, model);
+    mutateOperandZeroPointTest(device, model);
+    mutateOperationOperandTypeTest(device, model);
+    mutateOperationTypeTest(device, model);
+    mutateOperationInputOperandIndexTest(device, model);
+    mutateOperationOutputOperandIndexTest(device, model);
+    removeOperandTest(device, model);
+    removeOperationTest(device, model);
+    removeOperationInputTest(device, model);
+    removeOperationOutputTest(device, model);
+    addOperationInputTest(device, model);
+    addOperationOutputTest(device, model);
+    mutateExecutionPreferenceTest(device, model);
+}
+
+}  // namespace functional
+}  // namespace vts
+}  // namespace V1_1
+}  // namespace neuralnetworks
+}  // namespace hardware
+}  // namespace android
diff --git a/neuralnetworks/1.1/vts/functional/ValidateRequest.cpp b/neuralnetworks/1.1/vts/functional/ValidateRequest.cpp
new file mode 100644
index 0000000..b42f561
--- /dev/null
+++ b/neuralnetworks/1.1/vts/functional/ValidateRequest.cpp
@@ -0,0 +1,262 @@
+/*
+ * Copyright (C) 2018 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 "neuralnetworks_hidl_hal_test"
+
+#include "VtsHalNeuralnetworks.h"
+
+#include "Callbacks.h"
+#include "TestHarness.h"
+#include "Utils.h"
+
+#include <android-base/logging.h>
+#include <android/hidl/memory/1.0/IMemory.h>
+#include <hidlmemory/mapping.h>
+
+namespace android {
+namespace hardware {
+namespace neuralnetworks {
+namespace V1_1 {
+namespace vts {
+namespace functional {
+
+using ::android::hardware::neuralnetworks::V1_0::implementation::ExecutionCallback;
+using ::android::hardware::neuralnetworks::V1_0::implementation::PreparedModelCallback;
+using ::android::hidl::memory::V1_0::IMemory;
+using generated_tests::MixedTyped;
+using generated_tests::MixedTypedExampleType;
+using generated_tests::for_all;
+
+///////////////////////// UTILITY FUNCTIONS /////////////////////////
+
+static void createPreparedModel(const sp<IDevice>& device, const V1_1::Model& model,
+                                sp<IPreparedModel>* preparedModel) {
+    ASSERT_NE(nullptr, preparedModel);
+
+    // see if service can handle model
+    bool fullySupportsModel = false;
+    Return<void> supportedOpsLaunchStatus = device->getSupportedOperations_1_1(
+        model, [&fullySupportsModel](ErrorStatus status, const hidl_vec<bool>& supported) {
+            ASSERT_EQ(ErrorStatus::NONE, status);
+            ASSERT_NE(0ul, supported.size());
+            fullySupportsModel =
+                std::all_of(supported.begin(), supported.end(), [](bool valid) { return valid; });
+        });
+    ASSERT_TRUE(supportedOpsLaunchStatus.isOk());
+
+    // launch prepare model
+    sp<PreparedModelCallback> preparedModelCallback = new PreparedModelCallback();
+    ASSERT_NE(nullptr, preparedModelCallback.get());
+    Return<ErrorStatus> prepareLaunchStatus = device->prepareModel_1_1(
+        model, ExecutionPreference::FAST_SINGLE_ANSWER, preparedModelCallback);
+    ASSERT_TRUE(prepareLaunchStatus.isOk());
+    ASSERT_EQ(ErrorStatus::NONE, static_cast<ErrorStatus>(prepareLaunchStatus));
+
+    // retrieve prepared model
+    preparedModelCallback->wait();
+    ErrorStatus prepareReturnStatus = preparedModelCallback->getStatus();
+    *preparedModel = preparedModelCallback->getPreparedModel();
+
+    // The getSupportedOperations_1_1 call returns a list of operations that are
+    // guaranteed not to fail if prepareModel_1_1 is called, and
+    // 'fullySupportsModel' is true i.f.f. the entire model is guaranteed.
+    // If a driver has any doubt that it can prepare an operation, it must
+    // return false. So here, if a driver isn't sure if it can support an
+    // operation, but reports that it successfully prepared the model, the test
+    // can continue.
+    if (!fullySupportsModel && prepareReturnStatus != ErrorStatus::NONE) {
+        ASSERT_EQ(nullptr, preparedModel->get());
+        LOG(INFO) << "NN VTS: Unable to test Request validation because vendor service cannot "
+                     "prepare model that it does not support.";
+        std::cout << "[          ]   Unable to test Request validation because vendor service "
+                     "cannot prepare model that it does not support."
+                  << std::endl;
+        return;
+    }
+    ASSERT_EQ(ErrorStatus::NONE, prepareReturnStatus);
+    ASSERT_NE(nullptr, preparedModel->get());
+}
+
+// Primary validation function. This function will take a valid request, apply a
+// mutation to it to invalidate the request, then pass it to interface calls
+// that use the request. Note that the request here is passed by value, and any
+// mutation to the request does not leave this function.
+static void validate(const sp<IPreparedModel>& preparedModel, const std::string& message,
+                     Request request, const std::function<void(Request*)>& mutation) {
+    mutation(&request);
+    SCOPED_TRACE(message + " [execute]");
+
+    sp<ExecutionCallback> executionCallback = new ExecutionCallback();
+    ASSERT_NE(nullptr, executionCallback.get());
+    Return<ErrorStatus> executeLaunchStatus = preparedModel->execute(request, executionCallback);
+    ASSERT_TRUE(executeLaunchStatus.isOk());
+    ASSERT_EQ(ErrorStatus::INVALID_ARGUMENT, static_cast<ErrorStatus>(executeLaunchStatus));
+
+    executionCallback->wait();
+    ErrorStatus executionReturnStatus = executionCallback->getStatus();
+    ASSERT_EQ(ErrorStatus::INVALID_ARGUMENT, executionReturnStatus);
+}
+
+// Delete element from hidl_vec. hidl_vec doesn't support a "remove" operation,
+// so this is efficiently accomplished by moving the element to the end and
+// resizing the hidl_vec to one less.
+template <typename Type>
+static void hidl_vec_removeAt(hidl_vec<Type>* vec, uint32_t index) {
+    if (vec) {
+        std::rotate(vec->begin() + index, vec->begin() + index + 1, vec->end());
+        vec->resize(vec->size() - 1);
+    }
+}
+
+template <typename Type>
+static uint32_t hidl_vec_push_back(hidl_vec<Type>* vec, const Type& value) {
+    // assume vec is valid
+    const uint32_t index = vec->size();
+    vec->resize(index + 1);
+    (*vec)[index] = value;
+    return index;
+}
+
+///////////////////////// REMOVE INPUT ////////////////////////////////////
+
+static void removeInputTest(const sp<IPreparedModel>& preparedModel, const Request& request) {
+    for (size_t input = 0; input < request.inputs.size(); ++input) {
+        const std::string message = "removeInput: removed input " + std::to_string(input);
+        validate(preparedModel, message, request,
+                 [input](Request* request) { hidl_vec_removeAt(&request->inputs, input); });
+    }
+}
+
+///////////////////////// REMOVE OUTPUT ////////////////////////////////////
+
+static void removeOutputTest(const sp<IPreparedModel>& preparedModel, const Request& request) {
+    for (size_t output = 0; output < request.outputs.size(); ++output) {
+        const std::string message = "removeOutput: removed Output " + std::to_string(output);
+        validate(preparedModel, message, request,
+                 [output](Request* request) { hidl_vec_removeAt(&request->outputs, output); });
+    }
+}
+
+///////////////////////////// ENTRY POINT //////////////////////////////////
+
+std::vector<Request> createRequests(const std::vector<MixedTypedExampleType>& examples) {
+    const uint32_t INPUT = 0;
+    const uint32_t OUTPUT = 1;
+
+    std::vector<Request> requests;
+
+    for (auto& example : examples) {
+        const MixedTyped& inputs = example.first;
+        const MixedTyped& outputs = example.second;
+
+        std::vector<RequestArgument> inputs_info, outputs_info;
+        uint32_t inputSize = 0, outputSize = 0;
+
+        // This function only partially specifies the metadata (vector of RequestArguments).
+        // The contents are copied over below.
+        for_all(inputs, [&inputs_info, &inputSize](int index, auto, auto s) {
+            if (inputs_info.size() <= static_cast<size_t>(index)) inputs_info.resize(index + 1);
+            RequestArgument arg = {
+                .location = {.poolIndex = INPUT, .offset = 0, .length = static_cast<uint32_t>(s)},
+                .dimensions = {},
+            };
+            RequestArgument arg_empty = {
+                .hasNoValue = true,
+            };
+            inputs_info[index] = s ? arg : arg_empty;
+            inputSize += s;
+        });
+        // Compute offset for inputs 1 and so on
+        {
+            size_t offset = 0;
+            for (auto& i : inputs_info) {
+                if (!i.hasNoValue) i.location.offset = offset;
+                offset += i.location.length;
+            }
+        }
+
+        // Go through all outputs, initialize RequestArgument descriptors
+        for_all(outputs, [&outputs_info, &outputSize](int index, auto, auto s) {
+            if (outputs_info.size() <= static_cast<size_t>(index)) outputs_info.resize(index + 1);
+            RequestArgument arg = {
+                .location = {.poolIndex = OUTPUT, .offset = 0, .length = static_cast<uint32_t>(s)},
+                .dimensions = {},
+            };
+            outputs_info[index] = arg;
+            outputSize += s;
+        });
+        // Compute offset for outputs 1 and so on
+        {
+            size_t offset = 0;
+            for (auto& i : outputs_info) {
+                i.location.offset = offset;
+                offset += i.location.length;
+            }
+        }
+        std::vector<hidl_memory> pools = {nn::allocateSharedMemory(inputSize),
+                                          nn::allocateSharedMemory(outputSize)};
+        if (pools[INPUT].size() == 0 || pools[OUTPUT].size() == 0) {
+            return {};
+        }
+
+        // map pool
+        sp<IMemory> inputMemory = mapMemory(pools[INPUT]);
+        if (inputMemory == nullptr) {
+            return {};
+        }
+        char* inputPtr = reinterpret_cast<char*>(static_cast<void*>(inputMemory->getPointer()));
+        if (inputPtr == nullptr) {
+            return {};
+        }
+
+        // initialize pool
+        inputMemory->update();
+        for_all(inputs, [&inputs_info, inputPtr](int index, auto p, auto s) {
+            char* begin = (char*)p;
+            char* end = begin + s;
+            // TODO: handle more than one input
+            std::copy(begin, end, inputPtr + inputs_info[index].location.offset);
+        });
+        inputMemory->commit();
+
+        requests.push_back({.inputs = inputs_info, .outputs = outputs_info, .pools = pools});
+    }
+
+    return requests;
+}
+
+void ValidationTest::validateRequests(const V1_1::Model& model,
+                                      const std::vector<Request>& requests) {
+    // create IPreparedModel
+    sp<IPreparedModel> preparedModel;
+    ASSERT_NO_FATAL_FAILURE(createPreparedModel(device, model, &preparedModel));
+    if (preparedModel == nullptr) {
+        return;
+    }
+
+    // validate each request
+    for (const Request& request : requests) {
+        removeInputTest(preparedModel, request);
+        removeOutputTest(preparedModel, request);
+    }
+}
+
+}  // namespace functional
+}  // namespace vts
+}  // namespace V1_1
+}  // namespace neuralnetworks
+}  // namespace hardware
+}  // namespace android
diff --git a/neuralnetworks/1.1/vts/functional/ValidationTests.cpp b/neuralnetworks/1.1/vts/functional/ValidationTests.cpp
new file mode 100644
index 0000000..1c35ba8
--- /dev/null
+++ b/neuralnetworks/1.1/vts/functional/ValidationTests.cpp
@@ -0,0 +1,50 @@
+/*
+ * Copyright (C) 2018 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 "neuralnetworks_hidl_hal_test"
+
+#include "Models.h"
+#include "VtsHalNeuralnetworks.h"
+
+namespace android {
+namespace hardware {
+namespace neuralnetworks {
+namespace V1_1 {
+namespace vts {
+namespace functional {
+
+// forward declarations
+std::vector<Request> createRequests(const std::vector<MixedTypedExample>& examples);
+
+// generate validation tests
+#define VTS_CURRENT_TEST_CASE(TestName)                                           \
+    TEST_F(ValidationTest, TestName) {                                            \
+        const Model model = TestName::createTestModel();                          \
+        const std::vector<Request> requests = createRequests(TestName::examples); \
+        validateModel(model);                                                     \
+        validateRequests(model, requests);                                        \
+    }
+
+FOR_EACH_TEST_MODEL(VTS_CURRENT_TEST_CASE)
+
+#undef VTS_CURRENT_TEST_CASE
+
+}  // namespace functional
+}  // namespace vts
+}  // namespace V1_1
+}  // namespace neuralnetworks
+}  // namespace hardware
+}  // namespace android
diff --git a/neuralnetworks/1.1/vts/functional/VtsHalNeuralnetworksV1_1.cpp b/neuralnetworks/1.1/vts/functional/VtsHalNeuralnetworks.cpp
similarity index 64%
rename from neuralnetworks/1.1/vts/functional/VtsHalNeuralnetworksV1_1.cpp
rename to neuralnetworks/1.1/vts/functional/VtsHalNeuralnetworks.cpp
index b1d3be7..62381e6 100644
--- a/neuralnetworks/1.1/vts/functional/VtsHalNeuralnetworksV1_1.cpp
+++ b/neuralnetworks/1.1/vts/functional/VtsHalNeuralnetworks.cpp
@@ -16,16 +16,7 @@
 
 #define LOG_TAG "neuralnetworks_hidl_hal_test"
 
-#include "VtsHalNeuralnetworksV1_1.h"
-#include "Utils.h"
-
-#include <android-base/logging.h>
-#include <hidlmemory/mapping.h>
-
-using ::android::hardware::hidl_memory;
-using ::android::hidl::allocator::V1_0::IAllocator;
-using ::android::hidl::memory::V1_0::IMemory;
-using ::android::sp;
+#include "VtsHalNeuralnetworks.h"
 
 namespace android {
 namespace hardware {
@@ -34,11 +25,6 @@
 namespace vts {
 namespace functional {
 
-// allocator helper
-hidl_memory allocateSharedMemory(int64_t size) {
-    return nn::allocateSharedMemory(size);
-}
-
 // A class for test environment setup
 NeuralnetworksHidlEnvironment::NeuralnetworksHidlEnvironment() {}
 
@@ -52,23 +38,49 @@
 }
 
 void NeuralnetworksHidlEnvironment::registerTestServices() {
-    registerTestService<V1_1::IDevice>();
+    registerTestService<IDevice>();
 }
 
 // The main test class for NEURALNETWORK HIDL HAL.
+NeuralnetworksHidlTest::NeuralnetworksHidlTest() {}
+
 NeuralnetworksHidlTest::~NeuralnetworksHidlTest() {}
 
 void NeuralnetworksHidlTest::SetUp() {
-    device = ::testing::VtsHalHidlTargetTestBase::getService<V1_1::IDevice>(
+    ::testing::VtsHalHidlTargetTestBase::SetUp();
+    device = ::testing::VtsHalHidlTargetTestBase::getService<IDevice>(
         NeuralnetworksHidlEnvironment::getInstance());
     ASSERT_NE(nullptr, device.get());
 }
 
-void NeuralnetworksHidlTest::TearDown() {}
+void NeuralnetworksHidlTest::TearDown() {
+    device = nullptr;
+    ::testing::VtsHalHidlTargetTestBase::TearDown();
+}
 
 }  // namespace functional
 }  // namespace vts
+
+::std::ostream& operator<<(::std::ostream& os, ErrorStatus errorStatus) {
+    return os << toString(errorStatus);
+}
+
+::std::ostream& operator<<(::std::ostream& os, DeviceStatus deviceStatus) {
+    return os << toString(deviceStatus);
+}
+
 }  // namespace V1_1
 }  // namespace neuralnetworks
 }  // namespace hardware
 }  // namespace android
+
+using android::hardware::neuralnetworks::V1_1::vts::functional::NeuralnetworksHidlEnvironment;
+
+int main(int argc, char** argv) {
+    ::testing::AddGlobalTestEnvironment(NeuralnetworksHidlEnvironment::getInstance());
+    ::testing::InitGoogleTest(&argc, argv);
+    NeuralnetworksHidlEnvironment::getInstance()->init(&argc, argv);
+
+    int status = RUN_ALL_TESTS();
+    return status;
+}
diff --git a/neuralnetworks/1.1/vts/functional/VtsHalNeuralnetworksV1_1.h b/neuralnetworks/1.1/vts/functional/VtsHalNeuralnetworks.h
similarity index 60%
rename from neuralnetworks/1.1/vts/functional/VtsHalNeuralnetworksV1_1.h
rename to neuralnetworks/1.1/vts/functional/VtsHalNeuralnetworks.h
index 426246c..0050e52 100644
--- a/neuralnetworks/1.1/vts/functional/VtsHalNeuralnetworksV1_1.h
+++ b/neuralnetworks/1.1/vts/functional/VtsHalNeuralnetworks.h
@@ -17,65 +17,71 @@
 #ifndef VTS_HAL_NEURALNETWORKS_V1_1_H
 #define VTS_HAL_NEURALNETWORKS_V1_1_H
 
-#include <android/hardware/neuralnetworks/1.0/IExecutionCallback.h>
-#include <android/hardware/neuralnetworks/1.0/IPreparedModel.h>
-#include <android/hardware/neuralnetworks/1.0/IPreparedModelCallback.h>
+#include <android/hardware/neuralnetworks/1.0/types.h>
 #include <android/hardware/neuralnetworks/1.1/IDevice.h>
 #include <android/hardware/neuralnetworks/1.1/types.h>
-#include <android/hidl/allocator/1.0/IAllocator.h>
 
 #include <VtsHalHidlTargetTestBase.h>
 #include <VtsHalHidlTargetTestEnvBase.h>
+
+#include <android-base/macros.h>
 #include <gtest/gtest.h>
-#include <string>
+#include <iostream>
+#include <vector>
 
 namespace android {
 namespace hardware {
 namespace neuralnetworks {
 namespace V1_1 {
+
+using V1_0::Request;
+using V1_0::DeviceStatus;
+using V1_0::ErrorStatus;
+
 namespace vts {
 namespace functional {
-hidl_memory allocateSharedMemory(int64_t size);
 
 // A class for test environment setup
 class NeuralnetworksHidlEnvironment : public ::testing::VtsHalHidlTargetTestEnvBase {
+    DISALLOW_COPY_AND_ASSIGN(NeuralnetworksHidlEnvironment);
     NeuralnetworksHidlEnvironment();
-    NeuralnetworksHidlEnvironment(const NeuralnetworksHidlEnvironment&) = delete;
-    NeuralnetworksHidlEnvironment(NeuralnetworksHidlEnvironment&&) = delete;
-    NeuralnetworksHidlEnvironment& operator=(const NeuralnetworksHidlEnvironment&) = delete;
-    NeuralnetworksHidlEnvironment& operator=(NeuralnetworksHidlEnvironment&&) = delete;
+    ~NeuralnetworksHidlEnvironment() override;
 
    public:
-    ~NeuralnetworksHidlEnvironment() override;
     static NeuralnetworksHidlEnvironment* getInstance();
     void registerTestServices() override;
 };
 
 // The main test class for NEURALNETWORKS HIDL HAL.
 class NeuralnetworksHidlTest : public ::testing::VtsHalHidlTargetTestBase {
+    DISALLOW_COPY_AND_ASSIGN(NeuralnetworksHidlTest);
+
    public:
+    NeuralnetworksHidlTest();
     ~NeuralnetworksHidlTest() override;
     void SetUp() override;
     void TearDown() override;
 
-    sp<V1_1::IDevice> device;
+   protected:
+    sp<IDevice> device;
 };
+
+// Tag for the validation tests
+class ValidationTest : public NeuralnetworksHidlTest {
+   protected:
+    void validateModel(const Model& model);
+    void validateRequests(const Model& model, const std::vector<Request>& request);
+};
+
+// Tag for the generated tests
+class GeneratedTest : public NeuralnetworksHidlTest {};
+
 }  // namespace functional
 }  // namespace vts
 
 // pretty-print values for error messages
-
-template <typename CharT, typename Traits>
-::std::basic_ostream<CharT, Traits>& operator<<(::std::basic_ostream<CharT, Traits>& os,
-                                                V1_0::ErrorStatus errorStatus) {
-    return os << toString(errorStatus);
-}
-
-template <typename CharT, typename Traits>
-::std::basic_ostream<CharT, Traits>& operator<<(::std::basic_ostream<CharT, Traits>& os,
-                                                V1_0::DeviceStatus deviceStatus) {
-    return os << toString(deviceStatus);
-}
+::std::ostream& operator<<(::std::ostream& os, ErrorStatus errorStatus);
+::std::ostream& operator<<(::std::ostream& os, DeviceStatus deviceStatus);
 
 }  // namespace V1_1
 }  // namespace neuralnetworks
diff --git a/neuralnetworks/1.1/vts/functional/VtsHalNeuralnetworksV1_1BasicTest.cpp b/neuralnetworks/1.1/vts/functional/VtsHalNeuralnetworksV1_1BasicTest.cpp
deleted file mode 100644
index 17f6744..0000000
--- a/neuralnetworks/1.1/vts/functional/VtsHalNeuralnetworksV1_1BasicTest.cpp
+++ /dev/null
@@ -1,305 +0,0 @@
-/*
- * Copyright (C) 2018 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 "neuralnetworks_hidl_hal_test"
-
-#include "VtsHalNeuralnetworksV1_1.h"
-
-#include "Callbacks.h"
-#include "Models.h"
-#include "TestHarness.h"
-
-#include <android-base/logging.h>
-#include <android/hardware/neuralnetworks/1.1/IDevice.h>
-#include <android/hardware/neuralnetworks/1.1/types.h>
-#include <android/hidl/memory/1.0/IMemory.h>
-#include <hidlmemory/mapping.h>
-
-using ::android::hardware::neuralnetworks::V1_0::IPreparedModel;
-using ::android::hardware::neuralnetworks::V1_0::DeviceStatus;
-using ::android::hardware::neuralnetworks::V1_0::ErrorStatus;
-using ::android::hardware::neuralnetworks::V1_0::FusedActivationFunc;
-using ::android::hardware::neuralnetworks::V1_0::Operand;
-using ::android::hardware::neuralnetworks::V1_0::OperandLifeTime;
-using ::android::hardware::neuralnetworks::V1_0::OperandType;
-using ::android::hardware::neuralnetworks::V1_0::Request;
-using ::android::hardware::neuralnetworks::V1_1::Capabilities;
-using ::android::hardware::neuralnetworks::V1_1::IDevice;
-using ::android::hardware::neuralnetworks::V1_1::Model;
-using ::android::hardware::neuralnetworks::V1_1::Operation;
-using ::android::hardware::neuralnetworks::V1_1::OperationType;
-using ::android::hardware::Return;
-using ::android::hardware::Void;
-using ::android::hardware::hidl_memory;
-using ::android::hardware::hidl_string;
-using ::android::hardware::hidl_vec;
-using ::android::hidl::allocator::V1_0::IAllocator;
-using ::android::hidl::memory::V1_0::IMemory;
-using ::android::sp;
-
-namespace android {
-namespace hardware {
-namespace neuralnetworks {
-namespace V1_1 {
-namespace vts {
-namespace functional {
-using ::android::hardware::neuralnetworks::V1_0::implementation::ExecutionCallback;
-using ::android::hardware::neuralnetworks::V1_0::implementation::PreparedModelCallback;
-
-static void doPrepareModelShortcut(const sp<IDevice>& device, sp<IPreparedModel>* preparedModel) {
-    ASSERT_NE(nullptr, preparedModel);
-    Model model = createValidTestModel_1_1();
-
-    // see if service can handle model
-    bool fullySupportsModel = false;
-    Return<void> supportedOpsLaunchStatus = device->getSupportedOperations_1_1(
-        model, [&fullySupportsModel](ErrorStatus status, const hidl_vec<bool>& supported) {
-            ASSERT_EQ(ErrorStatus::NONE, status);
-            ASSERT_NE(0ul, supported.size());
-            fullySupportsModel =
-                std::all_of(supported.begin(), supported.end(), [](bool valid) { return valid; });
-        });
-    ASSERT_TRUE(supportedOpsLaunchStatus.isOk());
-
-    // launch prepare model
-    sp<PreparedModelCallback> preparedModelCallback = new PreparedModelCallback();
-    ASSERT_NE(nullptr, preparedModelCallback.get());
-    Return<ErrorStatus> prepareLaunchStatus =
-        device->prepareModel_1_1(model, preparedModelCallback);
-    ASSERT_TRUE(prepareLaunchStatus.isOk());
-    ASSERT_EQ(ErrorStatus::NONE, static_cast<ErrorStatus>(prepareLaunchStatus));
-
-    // retrieve prepared model
-    preparedModelCallback->wait();
-    ErrorStatus prepareReturnStatus = preparedModelCallback->getStatus();
-    *preparedModel = preparedModelCallback->getPreparedModel();
-
-    // The getSupportedOperations call returns a list of operations that are
-    // guaranteed not to fail if prepareModel is called, and
-    // 'fullySupportsModel' is true i.f.f. the entire model is guaranteed.
-    // If a driver has any doubt that it can prepare an operation, it must
-    // return false. So here, if a driver isn't sure if it can support an
-    // operation, but reports that it successfully prepared the model, the test
-    // can continue.
-    if (!fullySupportsModel && prepareReturnStatus != ErrorStatus::NONE) {
-        ASSERT_EQ(nullptr, preparedModel->get());
-        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;
-    }
-    ASSERT_EQ(ErrorStatus::NONE, prepareReturnStatus);
-    ASSERT_NE(nullptr, preparedModel->get());
-}
-
-// create device test
-TEST_F(NeuralnetworksHidlTest, CreateDevice) {}
-
-// status test
-TEST_F(NeuralnetworksHidlTest, StatusTest) {
-    Return<DeviceStatus> status = device->getStatus();
-    ASSERT_TRUE(status.isOk());
-    EXPECT_EQ(DeviceStatus::AVAILABLE, static_cast<DeviceStatus>(status));
-}
-
-// initialization
-TEST_F(NeuralnetworksHidlTest, GetCapabilitiesTest) {
-    Return<void> ret =
-        device->getCapabilities_1_1([](ErrorStatus status, const Capabilities& capabilities) {
-            EXPECT_EQ(ErrorStatus::NONE, status);
-            EXPECT_LT(0.0f, capabilities.float32Performance.execTime);
-            EXPECT_LT(0.0f, capabilities.float32Performance.powerUsage);
-            EXPECT_LT(0.0f, capabilities.quantized8Performance.execTime);
-            EXPECT_LT(0.0f, capabilities.quantized8Performance.powerUsage);
-            EXPECT_LT(0.0f, capabilities.relaxedFloat32toFloat16Performance.execTime);
-            EXPECT_LT(0.0f, capabilities.relaxedFloat32toFloat16Performance.powerUsage);
-        });
-    EXPECT_TRUE(ret.isOk());
-}
-
-// supported operations positive test
-TEST_F(NeuralnetworksHidlTest, SupportedOperationsPositiveTest) {
-    Model model = createValidTestModel_1_1();
-    Return<void> ret = device->getSupportedOperations_1_1(
-        model, [&](ErrorStatus status, const hidl_vec<bool>& supported) {
-            EXPECT_EQ(ErrorStatus::NONE, status);
-            EXPECT_EQ(model.operations.size(), supported.size());
-        });
-    EXPECT_TRUE(ret.isOk());
-}
-
-// supported operations negative test 1
-TEST_F(NeuralnetworksHidlTest, SupportedOperationsNegativeTest1) {
-    Model model = createInvalidTestModel1_1_1();
-    Return<void> ret = device->getSupportedOperations_1_1(
-        model, [&](ErrorStatus status, const hidl_vec<bool>& supported) {
-            EXPECT_EQ(ErrorStatus::INVALID_ARGUMENT, status);
-            (void)supported;
-        });
-    EXPECT_TRUE(ret.isOk());
-}
-
-// supported operations negative test 2
-TEST_F(NeuralnetworksHidlTest, SupportedOperationsNegativeTest2) {
-    Model model = createInvalidTestModel2_1_1();
-    Return<void> ret = device->getSupportedOperations_1_1(
-        model, [&](ErrorStatus status, const hidl_vec<bool>& supported) {
-            EXPECT_EQ(ErrorStatus::INVALID_ARGUMENT, status);
-            (void)supported;
-        });
-    EXPECT_TRUE(ret.isOk());
-}
-
-// prepare simple model positive test
-TEST_F(NeuralnetworksHidlTest, SimplePrepareModelPositiveTest) {
-    sp<IPreparedModel> preparedModel;
-    doPrepareModelShortcut(device, &preparedModel);
-}
-
-// prepare simple model negative test 1
-TEST_F(NeuralnetworksHidlTest, SimplePrepareModelNegativeTest1) {
-    Model model = createInvalidTestModel1_1_1();
-    sp<PreparedModelCallback> preparedModelCallback = new PreparedModelCallback();
-    ASSERT_NE(nullptr, preparedModelCallback.get());
-    Return<ErrorStatus> prepareLaunchStatus =
-        device->prepareModel_1_1(model, preparedModelCallback);
-    ASSERT_TRUE(prepareLaunchStatus.isOk());
-    EXPECT_EQ(ErrorStatus::INVALID_ARGUMENT, static_cast<ErrorStatus>(prepareLaunchStatus));
-
-    preparedModelCallback->wait();
-    ErrorStatus prepareReturnStatus = preparedModelCallback->getStatus();
-    EXPECT_EQ(ErrorStatus::INVALID_ARGUMENT, prepareReturnStatus);
-    sp<IPreparedModel> preparedModel = preparedModelCallback->getPreparedModel();
-    EXPECT_EQ(nullptr, preparedModel.get());
-}
-
-// prepare simple model negative test 2
-TEST_F(NeuralnetworksHidlTest, SimplePrepareModelNegativeTest2) {
-    Model model = createInvalidTestModel2_1_1();
-    sp<PreparedModelCallback> preparedModelCallback = new PreparedModelCallback();
-    ASSERT_NE(nullptr, preparedModelCallback.get());
-    Return<ErrorStatus> prepareLaunchStatus =
-        device->prepareModel_1_1(model, preparedModelCallback);
-    ASSERT_TRUE(prepareLaunchStatus.isOk());
-    EXPECT_EQ(ErrorStatus::INVALID_ARGUMENT, static_cast<ErrorStatus>(prepareLaunchStatus));
-
-    preparedModelCallback->wait();
-    ErrorStatus prepareReturnStatus = preparedModelCallback->getStatus();
-    EXPECT_EQ(ErrorStatus::INVALID_ARGUMENT, prepareReturnStatus);
-    sp<IPreparedModel> preparedModel = preparedModelCallback->getPreparedModel();
-    EXPECT_EQ(nullptr, preparedModel.get());
-}
-
-// execute simple graph positive test
-TEST_F(NeuralnetworksHidlTest, SimpleExecuteGraphPositiveTest) {
-    std::vector<float> outputData = {-1.0f, -1.0f, -1.0f, -1.0f};
-    std::vector<float> expectedData = {6.0f, 8.0f, 10.0f, 12.0f};
-    const uint32_t OUTPUT = 1;
-
-    sp<IPreparedModel> preparedModel;
-    ASSERT_NO_FATAL_FAILURE(doPrepareModelShortcut(device, &preparedModel));
-    if (preparedModel == nullptr) {
-        return;
-    }
-    Request request = createValidTestRequest();
-
-    auto postWork = [&] {
-        sp<IMemory> outputMemory = mapMemory(request.pools[OUTPUT]);
-        if (outputMemory == nullptr) {
-            return false;
-        }
-        float* outputPtr = reinterpret_cast<float*>(static_cast<void*>(outputMemory->getPointer()));
-        if (outputPtr == nullptr) {
-            return false;
-        }
-        outputMemory->read();
-        std::copy(outputPtr, outputPtr + outputData.size(), outputData.begin());
-        outputMemory->commit();
-        return true;
-    };
-
-    sp<ExecutionCallback> executionCallback = new ExecutionCallback();
-    ASSERT_NE(nullptr, executionCallback.get());
-    executionCallback->on_finish(postWork);
-    Return<ErrorStatus> executeLaunchStatus = preparedModel->execute(request, executionCallback);
-    ASSERT_TRUE(executeLaunchStatus.isOk());
-    EXPECT_EQ(ErrorStatus::NONE, static_cast<ErrorStatus>(executeLaunchStatus));
-
-    executionCallback->wait();
-    ErrorStatus executionReturnStatus = executionCallback->getStatus();
-    EXPECT_EQ(ErrorStatus::NONE, executionReturnStatus);
-    EXPECT_EQ(expectedData, outputData);
-}
-
-// execute simple graph negative test 1
-TEST_F(NeuralnetworksHidlTest, SimpleExecuteGraphNegativeTest1) {
-    sp<IPreparedModel> preparedModel;
-    ASSERT_NO_FATAL_FAILURE(doPrepareModelShortcut(device, &preparedModel));
-    if (preparedModel == nullptr) {
-        return;
-    }
-    Request request = createInvalidTestRequest1();
-
-    sp<ExecutionCallback> executionCallback = new ExecutionCallback();
-    ASSERT_NE(nullptr, executionCallback.get());
-    Return<ErrorStatus> executeLaunchStatus = preparedModel->execute(request, executionCallback);
-    ASSERT_TRUE(executeLaunchStatus.isOk());
-    EXPECT_EQ(ErrorStatus::INVALID_ARGUMENT, static_cast<ErrorStatus>(executeLaunchStatus));
-
-    executionCallback->wait();
-    ErrorStatus executionReturnStatus = executionCallback->getStatus();
-    EXPECT_EQ(ErrorStatus::INVALID_ARGUMENT, executionReturnStatus);
-}
-
-// execute simple graph negative test 2
-TEST_F(NeuralnetworksHidlTest, SimpleExecuteGraphNegativeTest2) {
-    sp<IPreparedModel> preparedModel;
-    ASSERT_NO_FATAL_FAILURE(doPrepareModelShortcut(device, &preparedModel));
-    if (preparedModel == nullptr) {
-        return;
-    }
-    Request request = createInvalidTestRequest2();
-
-    sp<ExecutionCallback> executionCallback = new ExecutionCallback();
-    ASSERT_NE(nullptr, executionCallback.get());
-    Return<ErrorStatus> executeLaunchStatus = preparedModel->execute(request, executionCallback);
-    ASSERT_TRUE(executeLaunchStatus.isOk());
-    EXPECT_EQ(ErrorStatus::INVALID_ARGUMENT, static_cast<ErrorStatus>(executeLaunchStatus));
-
-    executionCallback->wait();
-    ErrorStatus executionReturnStatus = executionCallback->getStatus();
-    EXPECT_EQ(ErrorStatus::INVALID_ARGUMENT, executionReturnStatus);
-}
-
-}  // namespace functional
-}  // namespace vts
-}  // namespace V1_1
-}  // namespace neuralnetworks
-}  // namespace hardware
-}  // namespace android
-
-using android::hardware::neuralnetworks::V1_1::vts::functional::NeuralnetworksHidlEnvironment;
-
-int main(int argc, char** argv) {
-    ::testing::AddGlobalTestEnvironment(NeuralnetworksHidlEnvironment::getInstance());
-    ::testing::InitGoogleTest(&argc, argv);
-    NeuralnetworksHidlEnvironment::getInstance()->init(&argc, argv);
-
-    int status = RUN_ALL_TESTS();
-    return status;
-}
diff --git a/neuralnetworks/1.1/vts/functional/VtsHalNeuralnetworksV1_1GeneratedTest.cpp b/neuralnetworks/1.1/vts/functional/VtsHalNeuralnetworksV1_1GeneratedTest.cpp
deleted file mode 100644
index 025d9fe..0000000
--- a/neuralnetworks/1.1/vts/functional/VtsHalNeuralnetworksV1_1GeneratedTest.cpp
+++ /dev/null
@@ -1,80 +0,0 @@
-/*
- * Copyright (C) 2018 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 "neuralnetworks_hidl_hal_test"
-
-#include "VtsHalNeuralnetworksV1_1.h"
-
-#include "Callbacks.h"
-#include "TestHarness.h"
-
-#include <android-base/logging.h>
-#include <android/hardware/neuralnetworks/1.1/IDevice.h>
-#include <android/hardware/neuralnetworks/1.1/types.h>
-#include <android/hidl/memory/1.0/IMemory.h>
-#include <hidlmemory/mapping.h>
-
-using ::android::hardware::neuralnetworks::V1_0::IPreparedModel;
-using ::android::hardware::neuralnetworks::V1_0::Capabilities;
-using ::android::hardware::neuralnetworks::V1_0::DeviceStatus;
-using ::android::hardware::neuralnetworks::V1_0::ErrorStatus;
-using ::android::hardware::neuralnetworks::V1_0::FusedActivationFunc;
-using ::android::hardware::neuralnetworks::V1_0::Operand;
-using ::android::hardware::neuralnetworks::V1_0::OperandLifeTime;
-using ::android::hardware::neuralnetworks::V1_0::OperandType;
-using ::android::hardware::neuralnetworks::V1_0::Request;
-using ::android::hardware::neuralnetworks::V1_1::IDevice;
-using ::android::hardware::neuralnetworks::V1_1::Model;
-using ::android::hardware::neuralnetworks::V1_1::Operation;
-using ::android::hardware::neuralnetworks::V1_1::OperationType;
-using ::android::hardware::Return;
-using ::android::hardware::Void;
-using ::android::hardware::hidl_memory;
-using ::android::hardware::hidl_string;
-using ::android::hardware::hidl_vec;
-using ::android::hidl::allocator::V1_0::IAllocator;
-using ::android::hidl::memory::V1_0::IMemory;
-using ::android::sp;
-
-namespace android {
-namespace hardware {
-namespace neuralnetworks {
-
-namespace generated_tests {
-using ::generated_tests::MixedTypedExampleType;
-extern void Execute(sp<V1_1::IDevice>&, std::function<Model(void)>, std::function<bool(int)>,
-                    const std::vector<MixedTypedExampleType>&);
-}  // namespace generated_tests
-
-namespace V1_1 {
-namespace vts {
-namespace functional {
-using ::android::hardware::neuralnetworks::V1_0::implementation::ExecutionCallback;
-using ::android::hardware::neuralnetworks::V1_0::implementation::PreparedModelCallback;
-
-// Mixed-typed examples
-typedef generated_tests::MixedTypedExampleType MixedTypedExample;
-
-// in frameworks/ml/nn/runtime/tests/generated/
-#include "all_generated_V1_0_vts_tests.cpp"
-#include "all_generated_V1_1_vts_tests.cpp"
-
-}  // namespace functional
-}  // namespace vts
-}  // namespace V1_1
-}  // namespace neuralnetworks
-}  // namespace hardware
-}  // namespace android