diff --git a/neuralnetworks/1.0/IDevice.hal b/neuralnetworks/1.0/IDevice.hal
index ec3b27f..b6f9433 100644
--- a/neuralnetworks/1.0/IDevice.hal
+++ b/neuralnetworks/1.0/IDevice.hal
@@ -21,14 +21,63 @@
 import IEvent;
 import IPreparedModel;
 
+/**
+ * This interface represents a device driver.
+ */
 interface IDevice {
+    /**
+     * Gets the capabilities of a driver.
+     *
+     * @return status ErrorStatus::NONE if successful.
+     * @return capabilities Capabilities of the driver.
+     */
     getCapabilities() generates (ErrorStatus status, Capabilities capabilities);
 
+    /**
+     * Gets the supported operations in a model.
+     *
+     * getSupportedSubgraph provides a more nuanced indication on whether a
+     * model is able to be compiled by the driver. Having the entire model
+     * allows for additional information such as tensor shapes to inputs or
+     * tensor strides, information which is not known in "initialize".
+     *
+     * @param model A model whose operations--and their corresponding
+     *              operands--are to be verified by the driver.
+     * @return status ErrorStatus::NONE if successful.
+     * @return supportedOperations A list of supported operations, where true
+     *                             indicates the operation is supported and
+     *                             false indicates the operation is not
+     *                             supported. The index of "supported"
+     *                             corresponds with the index of the operation
+     *                             it is describing.
+     */
     getSupportedOperations(Model model)
             generates (ErrorStatus status, vec<bool> supportedOperations);
 
+    /**
+     * Prepares a model for execution.
+     *
+     * prepareModel is used to make any necessary transformations or alternative
+     * representations to a model for execution, possible including
+     * transformations on the constant data, optimization on the model's graph,
+     * or compilation into the device's native binary.
+     *
+     * The only information that may be unknown to the model at this stage is
+     * the shape of the tensors, which may only be known at execution time.
+     *
+     * @param model The model to be prepared for execution.
+     * @param event A synchronization callback that must be signaled once the
+     *              execution has finished.
+     * @return status ErrorStatus::NONE if successful.
+     * @return preparedModel A handle to the resultant prepared model.
+     */
     prepareModel(Model model, IEvent event)
             generates (ErrorStatus status, IPreparedModel preparedModel);
 
+    /**
+     * Returns the current status of a driver.
+     *
+     * @return status Status of the driver.
+     */
     getStatus() generates (DeviceStatus status);
 };
diff --git a/neuralnetworks/1.0/IEvent.hal b/neuralnetworks/1.0/IEvent.hal
index cf71bbc..2ebda58 100644
--- a/neuralnetworks/1.0/IEvent.hal
+++ b/neuralnetworks/1.0/IEvent.hal
@@ -29,21 +29,15 @@
  * indicate to the Neuralnetworks runtime whether the computation was
  * successful or not, and that the corresponding output is ready to be
  * consumed if the execution was successful.
- *
- * TODO: Mention that "notify" is also called by a runtime thread
- * during CPU fallback execution? Depends on whether the HIDL comments
- * are strictly for vendors or not.
  */
 interface IEvent {
 
     /**
-     * IEvent::notify is called by the server thread (i.e. the thread doing the
-     * work) to mark the event as completed so that any threads requiring the
-     * corresponding resources can continue executing.
+     * IEvent::notify is called by the server thread (i.e., the thread doing
+     * the work) to mark the event as completed so that any threads requiring
+     * the corresponding output can continue executing.
      *
-     * @param status Status of the execution associated with the Event.
-     *               Should be SUCCESS or ERROR.
+     * @param status ErrorStatus::NONE if successful.
      */
     oneway notify(ErrorStatus status);
-
 };
diff --git a/neuralnetworks/1.0/IPreparedModel.hal b/neuralnetworks/1.0/IPreparedModel.hal
index 1b82610..a7c3342 100644
--- a/neuralnetworks/1.0/IPreparedModel.hal
+++ b/neuralnetworks/1.0/IPreparedModel.hal
@@ -14,13 +14,33 @@
  * limitations under the License.
  */
 
-/* This HAL is a work in progress */
-
 package android.hardware.neuralnetworks@1.0;
 
 import IEvent;
 
+/**
+ * IPreparedModel describes a model that has been prepared for execution and
+ * is used to launch executions.
+ */
 interface IPreparedModel {
-    // Multiple threads can call this execute function concurrently.
+    /**
+     * Spawns an asynchronous execution on a prepared model.
+     *
+     * Executions are asynchronous with respect to the Neuralnetworks runtime.
+     * To support this, IPreparedModel::execute must spawn a new task and return
+     * whether the task was successfully launched. The asynchronous task which
+     * performs the execution must call event's IEvent::notify with the status
+     * of the execution immediately after the execution has finished.
+     *
+     * Multiple threads can call this execute function concurrently.
+     *
+     * @param request The input and output information on which the prepared
+     *                model is to be executed.
+     *                prepared model.
+     * @param event A callback used for synchronization that must be signaled
+     *              once the execution has finished.
+     * @return status ErrorStatus::NONE if the asynchronous task was
+     *                successfully launched.
+     */
     execute(Request request, IEvent event) generates (ErrorStatus status);
 };
diff --git a/neuralnetworks/1.0/types.hal b/neuralnetworks/1.0/types.hal
index 5e3cf54..39e3d34 100644
--- a/neuralnetworks/1.0/types.hal
+++ b/neuralnetworks/1.0/types.hal
@@ -14,63 +14,945 @@
  * limitations under the License.
  */
 
-/* This HAL is a work in progress */
-
 package android.hardware.neuralnetworks@1.0;
 
-// The types an operand can have.
-// These values are the same as found in the NeuralNetworks.h and NeuralNetworksOEM.h files.
+/**
+ * Operand types.
+ *
+ * The type of an operand in a model.
+ *
+ * Types prefaced with TENSOR_* must be used for tensor data (i.e., tensors
+ * with at least one dimension). Types not prefaced by TENSOR_* represent
+ * scalar values and must have no dimensions.
+ */
 enum OperandType : int32_t {
-    FLOAT32                   = 0,
-    INT32                     = 1,
-    UINT32                    = 2,
-    TENSOR_FLOAT32            = 3,
-    TENSOR_INT32              = 4,
-    TENSOR_QUANT8_ASYMM       = 5,
+    /**
+     * The following entries are used to declare scalars.
+     */
+    FLOAT32             = 0,
+    INT32               = 1,
+    UINT32              = 2,
 
-    OEM                       = 10000,
-    TENSOR_OEM_BYTE           = 10001,
+    /**
+     * The following entries are used to declare tensors.
+     */
+    TENSOR_FLOAT32      = 3,
+    TENSOR_INT32        = 4,
+
+    /**
+     * A tensor of 8 bit integers that represent real numbers.
+     *
+     * Attached to this tensor are two numbers that can be used to convert the
+     * 8 bit integer to the real value and vice versa. These two numbers are:
+     * - scale: a 32 bit floating point value
+     * - zero_value: a 32 bit integer
+     *
+     * The formula is:
+     * real_value = (integer_value - zero_value) * scale.
+     */
+    TENSOR_QUANT8_ASYMM = 5,
+
+    /**
+     * The following entries are OEM specific operand types.
+     */
+    OEM                 = 10000,
+    TENSOR_OEM_BYTE     = 10001,
 };
 
-// The type of operations.  Unlike the operation types found in the
-// NeuralNetworks.h and NeuralNetworksOEM.h files, these specify the data type they operate on.
-// This is done to simplify the work of drivers.
-// TODO: Currently they are the same.  Add a conversion when finalizing the model.
+/**
+ * Operation types.
+ *
+ * The type of an operation in a model.
+ */
 enum OperationType : int32_t {
-    ADD                          = 0,
-    AVERAGE_POOL_2D              = 1,
-    CONCATENATION                = 2,
-    CONV_2D                      = 3,
-    DEPTHWISE_CONV_2D            = 4,
-    DEPTH_TO_SPACE               = 5,
-    DEQUANTIZE                   = 6,
-    EMBEDDING_LOOKUP             = 7,
-    FLOOR                        = 8,
-    FULLY_CONNECTED              = 9,
-    HASHTABLE_LOOKUP             = 10,
-    L2_NORMALIZATION             = 11,
-    L2_POOL_2D                   = 12,
-    LOCAL_RESPONSE_NORMALIZATION = 13,
-    LOGISTIC                     = 14,
-    LSH_PROJECTION               = 15,
-    LSTM                         = 16,
-    MAX_POOL_2D                  = 17,
-    MUL                          = 18,
-    RELU                         = 19,
-    RELU1                        = 20,
-    RELU6                        = 21,
-    RESHAPE                      = 22,
-    RESIZE_BILINEAR              = 23,
-    RNN                          = 24,
-    SOFTMAX                      = 25,
-    SPACE_TO_DEPTH               = 26,
-    SVDF                         = 27,
-    TANH                         = 28,
+    /**
+     * Adds two tensors, elment-wise.
+     *
+     * Takes two input tensors of identical type and compatible dimensions.  The output
+     * is the sum of both input tensors, optionally modified by an activation function.
+     *
+     * Two dimensions are compatible when:
+     *     1. they are equal, or
+     *     2. one of them is 1
+     *
+     * The size of the output is the maximum size along each dimension of the input operands.
+     * It starts with the trailing dimensions, and works its way forward.
+     *
+     * Example:
+     *     input1.dimension =    {4, 1, 2}
+     *     input2.dimension = {5, 4, 3, 1}
+     *     output.dimension = {5, 4, 3, 2}
+     *
+     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     * Supported tensor rank: up to 4
+     *
+     * Inputs:
+     * 0: A tensor.
+     * 1: A tensor of the same type, and compatible dimensions as input0.
+     * 2: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+     *    Specifies the activation to invoke on the result of each addition.
+     *
+     * Ouputs:
+     * 0: The sum, a tensor of the same type as input0.
+     */
+    ADD = 0,
 
-    OEM_OPERATION                = 10000,
+    /**
+     * Performs a 2-D average pooling operation.
+     *
+     * The output dimensions are functions of the filter dimensions, stride, and padding.
+     *
+     * The values in output Tensor is computed as:
+     *     output[batch, row, col, channel] =
+     *         sum_{i, j}(input[batch, row + i, col + j, channel]) / sum(1)
+     *
+     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor rank: 4, with "NHWC" data layout.
+     *
+     * Inputs:
+     * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
+     * 1: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
+     * 2: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
+     * 3: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
+     * 4: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
+     * 5: An INT32 value, specifying the output stride in the ‘width’ dimension.
+     * 6: An INT32 value, specifying the output stride in the ‘height’ dimension.
+     * 7: An INT32 value, specifying the filter width.
+     * 8: An INT32 value, specifying the filter height.
+     * 9: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+     *    Specifies the activation to invoke on the result of each addition.
+     *
+     * Ouputs:
+     * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
+     */
+    AVERAGE_POOL_2D = 1,
+
+    /**
+     * Concatenates the input tensors along the given dimension.
+     *
+     * The input tensors must have identical type and the same dimensions except the
+     * dimension along the concatenation axis.
+     *
+     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor rank: up to 4
+     *
+     * Inputs:
+     * 0 ~ n: The list on n input tensors, of shape [D0, D1, ..., Daxis(i), ..., Dm]
+     * n+1: An INT32 value, specifying the concatenation axis.
+     * n+2: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+     *    Specifies the activation to invoke on the result of each addition.
+     *
+     * Ouputs:
+     * 0: The output, a tensor of the same type as the input tensors.
+          The output shape is [D0, D1, ..., sum(Daxis(i)), ..., Dm].
+     */
+    CONCATENATION = 2,
+
+    /**
+     * Performs an 2-D convolution operation.
+     *
+     * The CONV_2D op sweeps a 2-D filter that can mix channels together over a batch of
+     * images, applying the filter to each window of each image of the appropriate size.
+     *
+     * The output dimensions are functions of the filter dimensions, stride, and padding.
+     *
+     * The values in output Tensor is computed as:
+     *     output[batch, row, col, channel] =
+     *         sum_{i, j} (
+     *             input[batch, row + i, col + j, k] *
+     *             filter[channel, row + i, col + j, k] +
+     *             bias[channel]
+     *         )
+     *
+     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor rank: 4, with "NHWC" data layout.
+     *
+     * Inputs:
+     * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
+     * 1: A 4-D tensor, of shape [depth_out, filter_height, filter_width, depth_in],
+     *    specifying the filter.
+     * 2: A 1-D tensor, of shape [depth_out], specifying the bias.
+     *    For input tensor of {@link OperandType::TENSOR_FLOAT32} type, the bias should
+     *    also be of {@link OperandType::TENSOR_FLOAT32}.
+     *    For input tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the bias
+     *    should be of {@link OperandType::TENSOR_INT32}.
+     * 3: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
+     * 4: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
+     * 5: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
+     * 6: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
+     * 7: An INT32 value, specifying the output stride in the ‘width’ dimension.
+     * 8: An INT32 value, specifying the output stride in the ‘height’ dimension.
+     * 9: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+     *    Specifies the activation to invoke on the result of each addition.
+     *
+     * Ouputs:
+     * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth_out].
+     */
+    CONV_2D = 3,
+
+    /**
+     * Performs an depthwise 2-D convolution operation.
+     *
+     * Given an input tensor of shape [batches, height, width, depth_in] and a filter
+     * tensor of shape [depth_out, filter_height, filter_width, depth_in] containing
+     * in_channels convolutional filters of depth 1, DEPTHWISE_CONV applies a different
+     * filter to each input channel (expanding from 1 channel to channel_multiplier channels
+     * for each), then concatenates the results together.
+     *
+     * The output has depth_out = depth_in * depth_multiplier channels.
+     * The output dimensions are functions of the filter dimensions, stride, and padding.
+     *
+     * The values in output Tensor is computed as:
+     *     output[b, i, j, k * channel_multiplier + q] =
+     *         sum_{di, dj} (
+     *             input[b, strides[1] * i + di, strides[2] * j + dj, k] *
+     *             filter[di, dj, k, q]
+     *         )
+     *
+     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor rank: 4, with "NHWC" data layout.
+     *
+     * Inputs:
+     * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
+     * 1: A 4-D tensor, of shape [depth_out, filter_height, filter_width, depth_in],
+     *    specifying the filter.
+     * 2: A 1-D tensor, of shape [depth_out], specifying the bias.
+     *    For input tensor of {@link OperandType::TENSOR_FLOAT32} type, the bias should
+     *    also be of {@link OperandType::TENSOR_FLOAT32}.
+     *    For input tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the bias
+     *    should be of {@link OperandType::TENSOR_INT32}.
+     * 3: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
+     * 4: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
+     * 5: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
+     * 6: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
+     * 7: An INT32 value, specifying the output stride in the ‘width’ dimension.
+     * 8: An INT32 value, specifying the output stride in the ‘height’ dimension.
+     * 9: An INT32 value, specifying the depthwise multiplier.
+     * 10: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+     *    Specifies the activation to invoke on the result of each addition.
+     *
+     * Ouputs:
+     * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth_out].
+     */
+    DEPTHWISE_CONV_2D = 4,
+
+    /**
+     * Rearranges data from depth into blocks of spatial data.
+     *
+     * More specifically, this op outputs a copy of the input tensor where values from
+     * the depth dimension are moved in spatial blocks to the height and width dimensions.
+     * The value block_size indicates the input block size and how the data is moved.
+     *
+     * Chunks of data of size block_size * block_size from depth are rearranged into
+     * non-overlapping blocks of size block_size x block_size.
+     *
+     * The width of the output tensor is input_depth * block_size, whereas the height is
+     * input_height * block_size.
+     * The depth of the input tensor must be divisible by block_size * block_size
+     *
+     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor rank: 4, with "NHWC" data layout.
+     *
+     * Inputs:
+     * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
+     * 1: An INT32 value, specifying the block_size. block_size must be >=1 and
+     *    block_size * block_size must be a divisor of the input depth.
+     *
+     * Ouputs:
+     * 0: The output 4-D tensor, of shape [batch, height*block_size, width*block_size,
+     *    depth/(block_size*block_size)].
+     */
+    DEPTH_TO_SPACE = 5,
+
+    /**
+     * Dequantizes the input tensor.
+     *
+     * The formula is:
+     *     output = (input - zero_value) * scale.
+     *
+     * Supported tensor types: {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor rank: up to 4
+     *
+     * Inputs:
+     * 0: A tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}.
+     *
+     * Ouputs:
+     * 0: The output tensor of same shape as input0, but with type
+          {@link OperandType::TENSOR_FLOAT32}.
+     */
+    DEQUANTIZE = 6,
+
+    /**
+     * Looks up items from a given tensor.
+     *
+     * Each item in the output is a raw copy of the corresponding item in
+     * the input “values”. If the the given “lookup” indices are out of bounds,
+     * the op will fail and an error will be reported.
+     *
+     * Inputs:
+     * * 0: Values. An n-D tensor of any type X (where n >= 2). E.g., if n is 2,
+     *      then the shape would be [lookup_dimension, values_dimension], where
+     *      “lookup_dimension” corresponds to the indexing dimension in the lookup
+     *      table, and “values_dimension” to the contents.
+     * * 1: Lookups. An 1-D tensor of type T, of shape [lookup_size], where
+     *      “lookup_size” is the number of elements to look for, and each entry
+     *      corresponds to the first dimension of the “values” tensor.
+     *
+     * Output:
+     * * 0: A n-D tensor of type X and the same rank and shape as the “values”
+     *      tensor, except for the first dimension which has size “lookup_size”.
+     */
+    EMBEDDING_LOOKUP = 7,
+
+    /**
+     * Computes element-wise floor() on the input tensor.
+     *
+     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     * Supported tensor rank: up to 4
+     *
+     * Inputs:
+     * 0: A tensor.
+     *
+     * Ouputs:
+     * 0: The output, a tensor of the same type and dimensions as input0.
+     */
+    FLOOR = 8,
+
+    /**
+     * Denotes a fully (densely) connected layer, which connects all elements in the input
+     * tensor with each element in the output tensor.
+     *
+     * This layer implements the operation:
+     *     outputs = activation(inputs * weights’ + bias)
+     *
+     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor rank: up to 4.
+     *
+     * Inputs:
+     * 0: A tensor, specifying the input. If rank is greater than 2, then it gets flattened to
+     *    a 2-D Tensor. The 2-D Tensor is handled as if dimensions corresponded to shape
+     *    [batch_size, input_size], where “batch_size” corresponds to the batching dimension,
+     *    and “input_size” is the size of the input.
+     * 1: A 2-D tensor, specifying the weights, of shape [num_units, input_size], where “num_units”
+     *    corresponds to the number of output nodes.
+     * 2: A 1-D tensor, of shape [num_units], specifying the bias.
+     *    For input tensor of {@link OperandType::TENSOR_FLOAT32} type, the bias should
+     *    also be of {@link OperandType::TENSOR_FLOAT32}.
+     *    For input tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the bias
+     *    should be of {@link OperandType::TENSOR_INT32}.
+     * 3: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+     *    Specifies the activation to invoke on the result of each addition.
+     *
+     * Ouputs:
+     * 0: The output tensor, of shape [batch_size, num_units].
+     */
+    FULLY_CONNECTED = 9,
+
+    /**
+     * Looks up values of a hash table with given keys.
+     *
+     * Inputs:
+     * * 0: Lookups. A 1-D int32 tensor with shape [ k ].
+     * * 1: Keys. A 1-D int32 tensor with shape [ n ], *MUST* be sorted in
+     *      ascending order.
+     * * 2: Values. A tensor with shape [ n … ].
+     *
+     * Outputs:
+     * * 0: Output. A tensor with shape [ k …].
+     * * 1: Hits. A uint8 tensor with shape [ k ] indicates whether the lookup
+     *      hits or not.
+     */
+    HASHTABLE_LOOKUP = 10,
+
+    /**
+     * Applies L2 normalization along a the depth dimension.
+     *
+     * The values in output Tensor is computed as:
+     *     output[batch, row, col, channel] =
+     *         input[batch, row, col, channel] /
+     *         sqrt(sum_{c} pow(input[batch, row, col, c], 2))
+     *
+     * For x with more dimensions, independently normalizes each 1-D slice along dimension dim.
+     *
+     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     * Supported tensor rank: 4, with "NHWC" data layout.
+     *
+     * Inputs:
+     * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
+     *
+     * Ouputs:
+     * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
+     */
+    L2_NORMALIZATION = 11,
+
+    /**
+     * Performs an 2-D L2 pooling operation.
+     *
+     * The output dimensions are functions of the filter dimensions, stride, and padding.
+     *
+     * The values in output Tensor is computed as:
+     *     output[batch, row, col, channel] =
+     *         sqrt(sum_{i, j} pow(input[batch, row + i, col + j, channel], 2) / sum(1))
+     *
+     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     * Supported tensor rank: 4, with "NHWC" data layout.
+     *
+     * Inputs:
+     * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
+     * 1: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
+     * 2: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
+     * 3: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
+     * 4: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
+     * 5: An INT32 value, specifying the output stride in the ‘width’ dimension.
+     * 6: An INT32 value, specifying the output stride in the ‘height’ dimension.
+     * 7: An INT32 value, specifying the filter width.
+     * 8: An INT32 value, specifying the filter height.
+     * 9: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+     *    Specifies the activation to invoke on the result of each addition.
+     *
+     * Ouputs:
+     * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
+     */
+    L2_POOL_2D = 12,
+
+    /**
+     * Applies Local Response Normalization along the depth dimension.
+     *
+     * The 4-D input tensor is treated as a 3-D array of 1-D vectors (along the last
+     * dimension), and each vector is normalized independently. Within a given vector,
+     * each component is divided by the weighted, squared sum of inputs within depth_radius.
+     *
+     * In details:
+     *     sqr_sum[a, b, c, d] =
+     *         sum(pow(input[a, b, c, d - depth_radius : d + depth_radius + 1], 2)
+     *     output = input / pow((bias + alpha * sqr_sum), beta)
+     *
+     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     * Supported tensor rank: 4, with "NHWC" data layout.
+     *
+     * Inputs:
+     * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
+     * 1: An INT32 value, specifying the radius of the normalization window.
+     * 2: A FLOAT32 value, specifying the bias, must not be zero.
+     * 3: A FLOAT32 value, specifying the scale factor, alpha.
+     * 4: A FLOAT32 value, specifying the exponent, beta.
+     *
+     * Ouputs:
+     * 0: The output tensor of same shape as input0.
+     */
+    LOCAL_RESPONSE_NORMALIZATION = 13,
+
+    /**
+     * Computes sigmoid activation on the input tensor element-wise.
+     *
+     * In details:
+     *     output = 1 / (1 + exp(-input))
+     *
+     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor rank: up to 4.
+     *
+     * Inputs:
+     * 0: A tensor, specifying the input.
+     *
+     * Ouputs:
+     * 0: The output tensor of same shape as input0.
+     */
+    LOGISTIC = 14,
+
+    /**
+     * Projects an input to a bit vector via locality senstive hashing.
+     *
+     * Inputs:
+     * * 0: Hash functions. Dim.size == 2, DataType: Float.
+     *            Tensor[0].Dim[0]: Number of hash functions.
+     *            Tensor[0].Dim[1]: Number of seeds per hash functions.
+     *            Tensor[0].Dim[1] <= 32 in sparse case.
+     *
+     * * 1: Input. Dim.size >= 1, no restriction on DataType.
+     * * 2: Weight. Optional. Dim.size == 1, DataType: Float.
+     *     If not set, each input element is considered to have the same weight of
+     *     1.0.
+     *     Tensor[1].Dim[0] == Tensor[2].Dim[0]
+     * * 3: Type:
+     *        Sparse: Value LSHProjectionType_SPARSE(=1).
+     *          Computed bit vector is considered to be sparse.
+     *          Each output element is an int32 made up of multiple bits computed from
+     *          hash functions.
+     *
+     *        Dense: Value LSHProjectionType_DENSE(=2).
+     *          Computed bit vector is considered to be dense. Each output element
+     *          represents a bit and can take the value of either 0 or 1.
+     *
+     * Outputs:
+     * * 0: If the projection type is sparse:
+     *        Output.Dim == { Tensor[0].Dim[0] }
+     *        A tensor of int32 that represents hash signatures.
+     *      If the projection type is Dense:
+     *        Output.Dim == { Tensor[0].Dim[0] * Tensor[0].Dim[1] }
+     *        A flattened tensor that represents projected bit vectors.
+     */
+    LSH_PROJECTION = 15,
+
+    /**
+     * Long short-term memory unit (LSTM) recurrent network layer.
+     *
+     * The default non-peephole implementation is based on:
+     * http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf
+     * S. Hochreiter and J. Schmidhuber. "Long Short-Term Memory". Neural
+     * Computation, 9(8):1735-1780, 1997.
+     *
+     * The peephole implementation is based on:
+     * https://research.google.com/pubs/archive/43905.pdf
+     * Hasim Sak, Andrew Senior, and Francoise Beaufays. "Long short-term memory
+     * recurrent neural network architectures for large scale acoustic modeling."
+     * INTERSPEECH, 2014.
+     *
+     * The coupling of input and forget gate (CIFG) is based on:
+     * http://arxiv.org/pdf/1503.04069.pdf
+     * Greff et al. "LSTM: A Search Space Odyssey"
+     *
+     * The class has the following independently optional inputs:
+     * * If input gate (if CIFG): “input_to_forget_weights”,
+     *   “recurrent_to_input_weights”, “cell_to_input_weights”, “input_gate_bias”.
+     * * If no peephole connections: “cell_to_input_weights”,
+     *   “cell_to_forget_weights”, “cell_to_output_weights”.
+     * * If no projection layer: “projection_weights” and “projection_bias”.
+     * * If no projection bias: “projection_bias”.
+     *
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     *
+     * Inputs:
+     * * 0: Input.
+     *      A 2-D tensor of type T, of shape [batch_size, input_size], where
+     *      “batch_size” corresponds to the batching dimension, and “input_size”
+     *      is the size of the input.
+     * * 1: input_to_input_weights.
+     *      A 2-D tensor of type T, of shape [num_units, input_size], where
+     *      “num_units” corresponds to the number of cell units.
+     * * 2: input_to_forget_weights.
+     *      A 2-D tensor of type T, of shape [num_units, input_size].
+     * * 3: input_to_cell_weights.
+     *      A 2-D tensor of type T, of shape [num_units, input_size].
+     * * 4: input_to_output_weights.
+     *      A 2-D tensor of type T, of shape [num_units, input_size].
+     * * 5: recurrent_to_input_weights.
+     *      A 2-D tensor of type T, of shape [num_units, output_size], where
+     *      “output_size” corresponds to either the number of cell units (i.e.,
+     *      “num_units”), or the second dimension of the “projection_weights”, if
+     *      defined.
+     * * 6: recurrent_to_forget_weights.
+     *      A 2-D tensor of type T, of shape [num_units, output_size].
+     * * 7: recurrent_to_cell_weights.
+     *      A 2-D tensor of type T, of shape [num_units, output_size].
+     * * 8: recurrent_to_output_weights.
+     *      A 2-D tensor of type T, of shape [num_units, output_size].
+     * * 9: cell_to_input_weights.
+     *      A 1-D tensor of type T, of shape [num_units].
+     * * 10:cell_to_forget_weights.
+     *      A 1-D tensor of type T, of shape [num_units].
+     * * 11:cell_to_output_weights.
+     *      A 1-D tensor of type T, of shape [num_units].
+     * * 12:input_gate_bias.
+     *      A 1-D tensor of type T, of shape [num_units].
+     * * 13:forget_gate_bias.
+     *      A 1-D tensor of type T, of shape [num_units].
+     * * 14:cell_bias.
+     *      A 1-D tensor of type T, of shape [num_units].
+     * * 15:output_gate_bias.
+     *      A 1-D tensor of type T, of shape [num_units].
+     * * 16:projection_weights.
+     *      A 2-D tensor of type T, of shape [output_size, num_units].
+     * * 17:projection_bias.
+     *      A 1-D tensor of type T, of shape [output_size].
+     *
+     * Parameters:
+     * * 18:fused_activation_function.
+     *      An (optional) ActivationFunctionType indicating the activation
+     *      function.
+     *      If “NONE” is specified then it results in a linear activation.
+     * * 19:cell_clip.
+     *      A clipping threshold for the cell state, such that values are bound
+     *      within [-cell_clip, cell_clip]. If set to 0.0 then clipping is
+     *      disabled.
+     * * 20:proj_clip.
+     *      A clipping threshold for the output from the projection layer, such
+     *      that values are bound within [-proj_clip, proj_clip]. If set to 0.0
+     *      then clipping is disabled.
+     *
+     * Outputs:
+     * * 0: scratch_buffer.
+     *      A 3-D tensor of type T, of shape [batch_size, num_cell, 4].
+     * * 1: output_state.
+     *      A 2-D tensor of type T, of shape [batch_size, output_size].
+     * * 2: cell_state.
+     *      A 2-D tensor of type T, of shape [batch_size, num_units].
+     * * 3: output.
+     *      A 2-D tensor of type T, of shape [batch_size, output_size]. This is
+     *      effectively the same as the current “output_state” value.
+     */
+    LSTM = 16,
+
+    /**
+     * Performs an 2-D max pooling operation.
+     *
+     * The output dimensions are functions of the filter dimensions, stride, and padding.
+     *
+     * The values in output Tensor is computed as:
+     *     output[batch, row, col, channel] =
+     *         max_{i, j} (input[batch, row + i, col + j, channel])
+     *
+     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor rank: 4, with "NHWC" data layout.
+     *
+     * Inputs:
+     * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
+     * 1: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
+     * 2: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
+     * 3: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
+     * 4: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
+     * 5: An INT32 value, specifying the output stride in the ‘width’ dimension.
+     * 6: An INT32 value, specifying the output stride in the ‘height’ dimension.
+     * 7: An INT32 value, specifying the filter width.
+     * 8: An INT32 value, specifying the filter height.
+     * 9: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+     *    Specifies the activation to invoke on the result of each addition.
+     *
+     * Ouputs:
+     * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
+     */
+    MAX_POOL_2D = 17,
+
+    /**
+     * Multiplies two tensors, elment-wise.
+     *
+     * Takes two input tensors of identical type and compatible dimensions.  The output
+     * is the product of both input tensors, optionally modified by an activation function.
+     *
+     * Two dimensions are compatible when:
+     *     1. they are equal, or
+     *     2. one of them is 1
+     *
+     * The size of the resulting output is the maximum size along each dimension of the
+     * input operands. It starts with the trailing dimensions, and works its way forward.
+     *
+     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     * Supported tensor rank: up to 4
+     *
+     * Inputs:
+     * 0: A tensor.
+     * 1: A tensor of the same type, and compatible dimensions as input0.
+     * 2: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+     *    Specifies the activation to invoke on the result of each addition.
+     *
+     * Ouputs:
+     * 0: The product, a tensor of the same type as input0.
+     */
+    MUL = 18,
+
+    /**
+     * Computes rectified linear activation on the input tensor element-wise.
+     *
+     * In details:
+     *     output = max(0, input)
+     *
+     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor rank: up to 4.
+     *
+     * Inputs:
+     * 0: A tensor, specifying the input.
+     *
+     * Ouputs:
+     * 0: The output tensor of same shape as input0.
+     */
+    RELU = 19,
+
+    /**
+     * Computes rectified linear 1 activation on the input tensor element-wise.
+     *
+     * In details:
+     *     output = min(1.f, max(-1.f, input))
+     *
+     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor rank: up to 4.
+     *
+     * Inputs:
+     * 0: A tensor, specifying the input.
+     *
+     * Ouputs:
+     * 0: The output tensor of same shape as input0.
+     */
+    RELU1 = 20,
+
+    /**
+     * Computes rectified linear 6 activation on the input tensor element-wise.
+     *
+     * In details:
+     *     output = min(6, max(0, input))
+     *
+     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor rank: up to 4.
+     *
+     * Inputs:
+     * 0: A tensor, specifying the input.
+     *
+     * Ouputs:
+     * 0: The output tensor of same shape as input0.
+     */
+    RELU6 = 21,
+
+    /**
+     * Reshapes a tensor.
+     *
+     * Given tensor, this operation returns a tensor that has the same values as tensor,
+     * but with a newly specified shape.
+     *
+     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor rank: up to 4.
+     *
+     * Inputs:
+     * 0: A tensor, specifying the tensor to be reshaped.
+     * 1: A 1-D tensor of type {@link OperandType::TENSOR_INT32}, defining the shape
+     *    of the output tensor. The number of elements implied by shape must be the same
+     *    as the number of elements in the input tensor.
+     *
+     * Ouputs:
+     * 0: The output tensor, of shape specified by the input shape.
+     */
+    RESHAPE = 22,
+
+    /**
+     * Resizes images to given size using the bilinear interpretation.
+     *
+     * Resized images will be distorted if their original aspect ratio is not the same as input.
+     *
+     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     * Supported tensor rank: 4, with "NHWC" data layout.
+     *
+     * Inputs:
+     * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
+     * 1: An INT32 value, specifying the output width of the output tensor.
+     * 2: An INT32 value, specifying the output height of the output tensor.
+     *
+     * Ouputs:
+     * 0: The output 4-D tensor, of shape [batches, new_height, new_width, depth].
+     */
+    RESIZE_BILINEAR = 23,
+
+    /**
+     * A basic recurrent neural network layer.
+     *
+     * This layer implements the operation:
+     * outputs = state = activation(inputs * input_weights + state * recurrent_weights + bias)
+     *
+     * Where:
+     * * “input_weights” is a weight matrix that multiplies the inputs;
+     * * “recurrent_weights” is a weight matrix that multiplies the current
+     *    “state” which itself is the output from the previous time step
+     *    computation;
+     * * “bias” is a bias vector (added to each output vector in the batch);
+     * * “activation” is the function passed as the “fused_activation_function”
+     *   argument (if not “NONE”).
+     *
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     *
+     * Inputs:
+     * * 0: input.
+     *      A 2-D tensor of type T, of shape [batch_size, input_size], where
+     *      “batch_size” corresponds to the batching dimension, and “input_size” is
+     *      the size of the input.
+     * * 1: weights.
+     *      A 2-D tensor of type T, of shape [num_units, input_size], where
+     *      “num_units” corresponds to the number of units.
+     * * 2: recurrent_weights.
+     *      A 2-D tensor of type T, of shape [num_units, num_units], with columns
+     *      corresponding to the weights from each unit.
+     * * 3: bias.
+     *      A 1-D tensor of type T, of shape [num_units].
+     *
+     *    For FLOAT32 input tensor, bias must also be FLOAT32.
+     *    For UINT8 input tensor, bias must be INT32.
+     *
+     * Parameters
+     * * 4: fused_activation_function.
+     *      An (optional) ActivationFunctionType indicating the activation
+     *      function. If “NONE” is specified then it results in a linear
+     *      activation.
+     *
+     * * 5: Hidden state.
+     *      A 2-D tensor of type T, of shape [batch_size, num_units].
+     *
+     * Outputs:
+     * * 0: output.
+     *      A 2-D tensor of type T, of shape [batch_size, num_units]. This is
+     *      effectively the same as the current state value.
+     */
+    RNN = 24,
+
+    /**
+     * Computes the softmax activation on the input tensor element-wise, per batch, by
+     * normalizing the input vector so the maximum coefficient is zero.
+     *
+     * In details:
+     *     output[batch, i] =
+     *         exp((input[batch, i] - max(input[batch, :])) * beta) /
+     *         sum_{k}{exp((input[batch, k] - max(input[batch, :])) * beta)}
+     *
+     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor rank: 2 or 4.
+     *
+     * Inputs:
+     * 0: A 2-D or 4-D tensor, specifying the tensor to be reshaped.
+     * 1: A FLOAT32 value, specifying the scaling factor for the exponent, beta.
+     *
+     * Ouputs:
+     * 0: The output tensor of same shape as input0.
+     */
+    SOFTMAX = 25,
+
+    /**
+     * Rearranges blocks of spatial data, into depth.
+     *
+     * More specifically, this op outputs a copy of the input tensor where values from
+     * the height and width dimensions are moved to the depth dimension.
+     * The value block_size indicates the input block size and how the data is moved.
+     *
+     * Chunks of data of size block_size * block_size from depth are rearranged into
+     * non-overlapping blocks of size block_size x block_size.
+     *
+     * The depth of the output tensor is input_depth * block_size * block_size.
+     * The input tensor's height and width must be divisible by block_size.
+     *
+     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor rank: 4, with "NHWC" data layout.
+     *
+     * Inputs:
+     * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
+     * 1: An INT32 value, specifying the block_size. block_size must be >=1 and
+     *    block_size must be a divisor of both the input height and width.
+     *
+     * Ouputs:
+     * 0: The output 4-D tensor, of shape [batch, height/block_size, width/block_size,
+     *    depth*block_size*block_size].
+     */
+    SPACE_TO_DEPTH = 26,
+
+    /**
+     * SVDF op is a kind of stateful layer derived from the notion that a
+     * densely connected layer that's processing a sequence of input frames can
+     * be approximated by using a singular value decomposition of each of its
+     * nodes. The implementation is based on:
+     *
+     * https://research.google.com/pubs/archive/43813.pdf
+     *
+     * P. Nakkiran, R. Alvarez, R. Prabhavalkar, C. Parada.
+     * “Compressing Deep Neural Networks using a Rank-Constrained Topology”.
+     * INTERSPEECH, 2015.
+     *
+     * It processes the incoming input using a 2-stage filtering mechanism:
+     * * stage 1 performs filtering on the "features" dimension, whose outputs get
+     *   pushed into a memory of fixed-size memory_size.
+     * * stage 2 performs filtering on the "time" dimension of the memory_size
+     *   memoized outputs of stage 1.
+     *
+     * Specifically, for rank 1, this layer implements the operation:
+     *
+     *    memory = push(conv1d(inputs, weights_feature, feature_dim, "VALID"));
+     *    outputs = activation(memory * weights_time + bias);
+     *
+     * Where:
+     * * “weights_feature” is a weights matrix that processes the inputs (by
+     *   convolving the input with every “feature filter”), and whose outputs get
+     *   pushed, stacked in order, into the fixed-size “memory” (the oldest entry
+     *   gets dropped);
+     * * “weights_time” is a weights matrix that processes the “memory” (by a
+     *   batched matrix multiplication on the num_units);
+     * * “bias” is an optional bias vector (added to each output vector in the
+     *   batch); and
+     * * “activation” is the function passed as the “fused_activation_function”
+     *   argument (if not “NONE”).
+     *
+     * Each rank adds a dimension to the weights matrices by means of stacking
+     * the filters.
+     *
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     *
+     * Inputs:
+     * * 0: input.
+     *      A 2-D tensor of type T, of shape [batch_size, input_size], where
+     *      “batch_size” corresponds to the batching dimension, and “input_size” is
+     *      the size of the input.
+     * * 1: weights_feature.
+     *      A 2-D tensor of type T, of shape [num_units, input_size], where
+     *      “num_units” corresponds to the number of units.
+     * * 2: weights_time.
+     *      A 2-D tensor of type T, of shape [num_units, memory_size], where
+     *      “memory_size” corresponds to the fixed-size of the memory.
+     * * 3: bias.
+     *      A optional 1-D tensor of type T, of shape [num_units].
+     *
+     *    For FLOAT32 input tensor, bias must also be FLOAT32.
+     *    For UINT8 input tensor, bias must be INT32.
+     *
+     * Parameters:
+     * * 4: rank.
+     *      The rank of the SVD approximation.
+     * * 5: fused_activation_function.
+     *      An (optional) ActivationFunctionType indicating the activation function.
+     *      If “NONE” is specified then it results in a linear activation.
+     *
+     * Outputs:
+     * * 0: state.
+     *      A 2-D tensor of type T, of shape [batch_size, (memory_size - 1) * num_units * rank].
+     * * 1: output.
+     *      A 2-D tensor of type T, of shape [batch_size, num_units].
+     */
+    SVDF = 27,
+
+    /**
+     * Computes hyperbolic tangent of input tensor element-wise.
+     *
+     * In details:
+     *     output = tanh(input)
+     *
+     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     * Supported tensor rank: up to 4.
+     *
+     * Inputs:
+     * 0: A tensor, specifying the input.
+     *
+     * Ouputs:
+     * 0: The output tensor of same shape as input0.
+     */
+    TANH = 28,
+
+    /**
+     * OEM specific operation.
+     *
+     * This operation is OEM specific. It should only be used for OEM applications.
+     */
+    OEM_OPERATION = 10000,
 };
 
-// Fused activation functions
+/**
+ * Fused activation function types.
+ */
 enum FusedActivationFunc : int32_t {
     NONE  = 0,
     RELU  = 1,
@@ -78,130 +960,309 @@
     RELU6 = 3,
 };
 
-// How an operand is used.
+/**
+ * How an operand is used.
+ */
 enum OperandLifeTime : int32_t {
-    // The operand is internal to the model.  It's created by an operation
-    // and consumed by other operations.
+    /**
+     * The operand is internal to the model.  It's created by an operation
+     * and consumed by other operations.
+     */
     TEMPORARY_VARIABLE,
-    // The operand is an input of the model. An operand can't be both
-    // input and output of a model.
+
+    /**
+     * The operand is an input of the model. An operand can't be both
+     * input and output of a model.
+     */
     MODEL_INPUT,
-    // The operand is an output of the model.
+
+    /**
+     * The operand is an output of the model.
+     */
     MODEL_OUTPUT,
-    // The operand is a constant found in Model.operandValues.
+
+    /**
+     * The operand is a constant found in Model.operandValues.
+     */
     CONSTANT_COPY,
-    // The operand is a constant that was specified via a Memory object.
-    CONSTANT_REFERENCE
+
+    /**
+     * The operand is a constant that was specified via a Memory object.
+     */
+    CONSTANT_REFERENCE,
 };
 
-// Status of a device.
+/**
+ * Status of a device.
+ */
 enum DeviceStatus : int32_t {
     AVAILABLE,
     BUSY,
     OFFLINE,
-    UNKNOWN  // Do we need this?
+    UNKNOWN,
 };
 
-// For the reference workload
-// Used by a driver to report its performance characteristics.
-// TODO revisit the data types and scales.
-struct PerformanceInfo {
-    float execTime;    // in nanoseconds
-    float powerUsage;  // in picoJoules
-};
-
+/**
+ * A typed operation.
+ */
 struct OperationTuple {
-    // The type of operation.
+    /**
+     * The type of operation.
+     */
     OperationType operationType;
-    // The input data type of operation.
+
+    /**
+     * The input data type of operation.
+     */
     OperandType operandType;
 };
 
-// The capabilities of a driver.
+/**
+ * Performance information for the reference workload.
+ *
+ * Used by a driver to report its performance characteristics.
+ */
+struct PerformanceInfo {
+    /**
+     * Execution time in nanoseconds.
+     */
+    float execTime;
+
+    /**
+     * Power usage in picoJoules.
+     */
+    float powerUsage;
+};
+
+/**
+ * The capabilities of a driver.
+ */
 struct Capabilities {
+    /**
+     * A collection of typed operations supported by the driver.
+     */
     vec<OperationTuple> supportedOperationTuples;
+
+    /**
+     * Indicates whether a driver caches its prepared model for reuse the next
+     * time the application begins. This is useful because the model may have
+     * been prepared in a previous run.
+     *
+     * True if caching is supported, false otherwise.
+     */
     bool cachesCompilation;
-    // TODO revisit the data types and scales.
+
+    /**
+     * Driver performance when operating on float32 data.
+     */
     PerformanceInfo float32Performance;
+
+    /**
+     * Driver performance when operating on asymmetric 8-bit quantized data.
+     */
     PerformanceInfo quantized8Performance;
 };
 
-// Describes the location of a data object.
+/**
+ * Describes the location of a data object.
+ */
 struct DataLocation {
-    // The index of the memory pool where this location is found.
-    // Two special values can also be used.  See the LOCATION_* constants above.
+    /**
+     * The index of the memory pool where this location is found.
+     */
     uint32_t poolIndex;
-    // Offset in bytes from the start of the pool.
+
+    /**
+     * Offset in bytes from the start of the pool.
+     */
     uint32_t offset;
-    // The length of the data, in bytes.
+
+    /**
+     * The length of the data in bytes.
+     */
     uint32_t length;
 };
 
+/**
+ * Describes one operand of the model's graph.
+ */
 struct Operand {
+    /**
+     * Data type of the operand.
+     */
     OperandType type;
+
+    /**
+     * Dimensions of the operand.
+     */
     vec<uint32_t> dimensions;
 
-    // The number of operations that uses this operand as input.
-    // TODO It would be nice to track the actual consumers, e.g. vec<uint32_t> consumers;
+    /**
+     * The number of operations that use this operand as input.
+     */
     uint32_t numberOfConsumers;
 
+    /**
+     * Quantized scale of the operand.
+     *
+     * Only applicable if the operand is of type TENSOR_QUANT8_ASYMM.
+     */
     float scale;
+
+    /**
+     * Quantized zero-point offset of the operand.
+     *
+     * Only applicable if the operand is of type TENSOR_QUANT8_ASYMM.
+     */
     int32_t zeroPoint;
 
-    // How the operand is used.
+    /**
+     * How the operand is used.
+     */
     OperandLifeTime lifetime;
 
-    // Where to find the data for this operand.
-    // If the lifetime is TEMPORARY_VARIABLE, MODEL_INPUT, or MODEL_OUTPUT:
-    // - All the fields will be 0.
-    // If the lifetime is CONSTANT_COPY:
-    // - location.poolIndex is 0.
-    // - location.offset is the offset in bytes into Model.operandValues.
-    // - location.length is set.
-    // If the lifetime is CONSTANT_REFERENCE:
-    // - location.poolIndex is set.
-    // - location.offset is the offset in bytes into the specified pool.
-    // - location.length is set.
+    /**
+     * Where to find the data for this operand.
+     * If the lifetime is TEMPORARY_VARIABLE, MODEL_INPUT, or MODEL_OUTPUT:
+     * - All the fields will be 0.
+     * If the lifetime is CONSTANT_COPY:
+     * - location.poolIndex is 0.
+     * - location.offset is the offset in bytes into Model.operandValues.
+     * - location.length is set.
+     * If the lifetime is CONSTANT_REFERENCE:
+     * - location.poolIndex is set.
+     * - location.offset is the offset in bytes into the specified pool.
+     * - location.length is set.
+     */
     DataLocation location;
 };
 
-// Describes one operation of the graph.
+/**
+ * Describes one operation of the model's graph.
+ */
 struct Operation {
-    // The tuple describing the operation type and input type.
+    /**
+     * The tuple describing the operation type and input type.
+     */
     OperationTuple opTuple;
-    // Describes the table that contains the indexes of the inputs of the
-    // operation. The offset is the index in the operandIndexes table.
+
+    /**
+     * Describes the table that contains the indexes of the inputs of the
+     * operation. The offset is the index in the operandIndexes table.
+     */
     vec<uint32_t> inputs;
-    // Describes the table that contains the indexes of the outputs of the
-    // operation. The offset is the index in the operandIndexes table.
+
+    /**
+     * Describes the table that contains the indexes of the outputs of the
+     * operation. The offset is the index in the operandIndexes table.
+     */
     vec<uint32_t> outputs;
 };
 
+/**
+ * A Neural Network Model.
+ *
+ * This includes not only the execution graph, but also constant data such as
+ * weights or scalars added at construction time. The only information that
+ * might not be known is the shape of the input tensors.
+ */
 struct Model {
+    /**
+     * All operands included in the model.
+     */
     vec<Operand> operands;
+
+    /**
+     * All operations included in the model.
+     *
+     * The operations are sorted into execution order.
+     */
     vec<Operation> operations;
+
+    /**
+     * Input indexes of the model.
+     *
+     * Each value corresponds to the index of the operand in "operands".
+     */
     vec<uint32_t> inputIndexes;
+
+    /**
+     * Output indexes of the model.
+     *
+     * Each value corresponds to the index of the operand in "operands".
+     */
     vec<uint32_t> outputIndexes;
+
+    /**
+     * A byte buffer containing operand data that were copied into the model.
+     */
     vec<uint8_t> operandValues;
+
+    /**
+     * A collection of shared memory pools containing operand data that were
+     * registered by the model.
+     */
     vec<memory> pools;
 };
 
+/**
+ * Metadata information specifying the location of the input or output data and
+ * any updates to the input or output operand.
+ */
 struct RequestArgument {
-    // The location within one of the memory pools
+    /**
+     * The location within one of the memory pools passed in the Request.
+     */
     DataLocation location;
-    // If dimensions.size() > 0, dimension information was provided along with the
-    // argument.  This can be the case for models that accept inputs of varying size.
-    // This can't change the rank, just the value of the dimensions that were
-    // unspecified in the model.
+
+    /**
+     * Updated dimension information.
+     *
+     * If dimensions.size() > 0, dimension information was provided along with the
+     * argument.  This can be the case for models that accept inputs of varying size.
+     * This can't change the rank, just the value of the dimensions that were
+     * unspecified in the model.
+     */
     vec<uint32_t> dimensions;
 };
 
+/**
+ * Inputs to be sent to and outputs to be retrieved from a prepared model.
+ *
+ * A Request serves two primary tasks:
+ * 1) Provides the input and output data to be used when executing the model.
+ * 2) Specifies any updates to the input operand metadata that were left
+ *    unspecified at model preparation time.
+ */
 struct Request {
+    /**
+     * Input data and information to be used in the execution of a prepared
+     * model.
+     *
+     * The index of the input corresponds to the index in Model.inputIndexes.
+     *   E.g., input[i] corresponds to Model.inputIndexes[i].
+     */
     vec<RequestArgument> inputs;
+
+    /**
+     * Output data and information to be used in the execution of a prepared
+     * model.
+     *
+     * The index of the output corresponds to the index in Model.outputIndexes.
+     *   E.g., output[i] corresponds to Model.outputIndexes[i].
+     */
     vec<RequestArgument> outputs;
+
+    /**
+     * A collection of shared memory pools containing operand data for both the
+     * inputs and the outputs to a model.
+     */
     vec<memory> pools;
 };
 
+/**
+ * Return status of a function.
+ */
 enum ErrorStatus : int32_t {
     NONE,
     DEVICE_UNAVAILABLE,