Neural Networks API

The Android Neural Networks API (NNAPI) is an Android C API designed for running computationally intensive operations for machine learning on Android devices. NNAPI is designed to provide a base layer of functionality for higher-level machine learning frameworks, such as TensorFlow Lite and Caffe2, that build and train neural networks. The API is available on all Android devices running Android 8.1 (API level 27) or higher.

NNAPI supports inferencing by applying data from Android devices to previously trained, developer-defined models. Examples of inferencing include classifying images, predicting user behavior, and selecting appropriate responses to a search query.

On-device inferencing has many benefits:

• Latency: You don’t need to send a request over a network connection and wait for a response. For example, this can be critical for video applications that process successive frames coming from a camera.
• Availability: The application runs even when outside of network coverage.
• Speed: New hardware that is specific to neural network processing provides significantly faster computation than a general-purpose CPU, alone.
• Privacy: The data does not leave the Android device.
• Cost: No server farm is needed when all the computations are performed on the Android device.

There are also trade-offs that a developer should keep in mind:

• System utilization: Evaluating neural networks involves a lot of computation, which could increase battery power usage. You should consider monitoring the battery health if this is a concern for your app, especially for long-running computations.
• Application size: Pay attention to the size of your models. Models may take up multiple megabytes of space. If bundling large models in your APK would unduly impact your users, you may want to consider downloading the models after app installation, using smaller models, or running your computations in the cloud. NNAPI does not provide functionality for running models in the cloud.

See the Android Neural Networks API sample to see one example of how to use NNAPI.

Understand the Neural Networks API runtime

NNAPI is meant to be called by machine learning libraries, frameworks, and tools that let developers train their models off-device and deploy them on Android devices. Apps typically would not use NNAPI directly, but would instead use higher-level machine learning frameworks. These frameworks in turn could use NNAPI to perform hardware-accelerated inference operations on supported devices.

Based on an app’s requirements and the hardware capabilities on an Android device, Android’s neural network runtime can efficiently distribute the computation workload across available on-device processors, including dedicated neural network hardware, graphics processing units (GPUs), and digital signal processors (DSPs).

For Android devices that lack a specialized vendor driver, the NNAPI runtime executes the requests on the CPU.

Figure 1 shows the high-level system architecture for NNAPI.

Neural Networks API programming model

To perform computations using NNAPI, you first need to construct a directed graph that defines the computations to perform. This computation graph, combined with your input data (for example, the weights and biases passed down from a machine learning framework), forms the model for NNAPI runtime evaluation.

NNAPI uses four main abstractions:

• Model: A computation graph of mathematical operations and the constant values learned through a training process. These operations are specific to neural networks. They include 2-dimensional (2D) convolution, logistic (sigmoid) activation, rectified linear (ReLU) activation, and more. Creating a model is a synchronous operation. Once successfully created, it can be reused across threads and compilations. In NNAPI, a model is represented as an ANeuralNetworksModel instance.
• Compilation: Represents a configuration for compiling an NNAPI model into lower-level code. Creating a compilation is a synchronous operation. Once successfully created, it can be reused across threads and executions. In NNAPI, each compilation is represented as an ANeuralNetworksCompilation instance.
• Memory: Represents shared memory, memory mapped files, and similar memory buffers. Using a memory buffer lets the NNAPI runtime transfer data to drivers more efficiently. An app typically creates one shared memory buffer that contains every tensor needed to define a model. You can also use memory buffers to store the inputs and outputs for an execution instance. In NNAPI, each memory buffer is represented as an ANeuralNetworksMemory instance.

• Execution: Interface for applying an NNAPI model to a set of inputs and to gather the results. Execution can be performed synchronously or asynchronously.

  For asynchronous execution, multiple threads can wait on the same execution. When this execution completes, all threads are released.

  In NNAPI, each execution is represented as an ANeuralNetworksExecution instance.

Figure 2 shows the basic programming flow.

The rest of this section describes the steps to set up your NNAPI model to perform computation, compile the model, and execute the compiled model.

Provide access to training data

Your trained weights and biases data are likely stored in a file. To provide the NNAPI runtime with efficient access to this data, create an ANeuralNetworksMemory instance by calling the ANeuralNetworksMemory_createFromFd() function and passing in the file descriptor of the opened data file. You also specify memory protection flags and an offset where the shared memory region starts in the file.

// Create a memory buffer from the file that contains the trained data
ANeuralNetworksMemory* mem1 = NULL;
int fd = open("training_data", O_RDONLY);
ANeuralNetworksMemory_createFromFd(file_size, PROT_READ, fd, 0, &mem1);

Although in this example we use only one ANeuralNetworksMemory instance for all our weights, it’s possible to use more than one ANeuralNetworksMemory instance for multiple files.

Use native hardware buffers

You can use native hardware buffers for model inputs, outputs, and constant operand values. In certain cases, an NNAPI accelerator can access AHardwareBuffer objects without the driver needing to copy the data. AHardwareBuffer has many different configurations, and not every NNAPI accelerator may support all of these configurations. Because of this limitation, refer to the constraints listed in ANeuralNetworksMemory_createFromAHardwareBuffer reference documentation and test ahead of time on target devices to ensure compilations and executions that use AHardwareBuffer behave as expected, using device assignment to specify the accelerator.

To allow the NNAPI runtime to access an AHardwareBuffer object, create an ANeuralNetworksMemory instance by calling the ANeuralNetworksMemory_createFromAHardwareBuffer function and passing in the AHardwareBuffer object, as shown in the following code sample:

// Configure and create AHardwareBuffer object
AHardwareBuffer_Desc desc = ...
AHardwareBuffer* awhb = nullptr;
AHardwareBuffer_allocate(&desc, &awhb);

// Create ANeuralNetworksMemory from AHardwareBuffer
ANeuralNetworksMemory* mem2 = NULL;
ANeuralNetworksMemory_createFromAHardwareBuffer(ahwb, &mem2);

When NNAPI no longer needs to access the AHardwareBuffer object, free the corresponding ANeuralNetworksMemory instance:

ANeuralNetworksMemory_free(mem2);

Model

A model is the fundamental unit of computation in NNAPI. Each model is defined by one or more operands and operations.

Operands

Operands are data objects used in defining the graph. These include the inputs and outputs of the model, the intermediate nodes that contain the data that flows from one operation to another, and the constants that are passed to these operations.

There are two types of operands that can be added to NNAPI models: scalars and tensors.

A scalar represents a single value. NNAPI supports scalar values in boolean, 16-bit floating point, 32-bit floating point, 32-bit integer, and unsigned 32-bit integer formats.

Most operations in NNAPI involve tensors. Tensors are n-dimensional arrays. NNAPI supports tensors with 16-bit floating point, 32-bit floating point, 8-bit quantized, 16-bit quantized, 32-bit integer, and 8-bit boolean values.

For example, figure 3 represents a model with two operations: an addition followed by a multiplication. The model takes an input tensor and produces one output tensor.

The model above has seven operands. These operands are identified implicitly by the index of the order in which they are added to the model. The first operand added has an index of 0, the second an index of 1, and so on. Operands 1, 2, 3, and 5 are constant operands.

The order in which you add the operands does not matter. For example, the model output operand could be the first one added. The important part is to use the correct index value when referring to an operand.

Operands have types. These are specified when they are added to the model.

An operand cannot be used as both input and output of a model.

Every operand must either be a model input, a constant, or the output operand of exactly one operation.

For additional information on using operands, see More about operands.

Operations

An operation specifies the computations to be performed. Each operation consists of these elements:

• an operation type (for example, addition, multiplication, convolution),
• a list of indexes of the operands that the operation uses for input, and
• a list of indexes of the operands that the operation uses for output.

The order in these lists matters; see the NNAPI API reference for the expected inputs and outputs of each operation type.

You must add the operands that an operation consumes or produces to the model before adding the operation.

The order in which you add operations does not matter. NNAPI relies on the dependencies established by the computation graph of operands and operations to determine the order in which operations are executed.

The operations that NNAPI supports are summarized in the table below:

Known issue in API level 28: When passing ANEURALNETWORKS_TENSOR_QUANT8_ASYMM tensors to the ANEURALNETWORKS_PAD operation, which is available on Android 9 (API level 28) and higher, the output from NNAPI may not match output from higher-level machine learning frameworks, such as TensorFlow Lite. You should instead pass only ANEURALNETWORKS_TENSOR_FLOAT32. The issue is resolved in Android 10 (API level 29) and higher.

Build models

In the following example, we create the two-operation model found in figure 3.

To build the model, follow these steps:

1. Call the ANeuralNetworksModel_create() function to define an empty model.

   ANeuralNetworksModel* model = NULL;
   ANeuralNetworksModel_create(&model);
   

2. Add the operands to your model by calling ANeuralNetworks_addOperand(). Their data types are defined using the ANeuralNetworksOperandType data structure.

   // In our example, all our tensors are matrices of dimension [3][4]
   ANeuralNetworksOperandType tensor3x4Type;
   tensor3x4Type.type = ANEURALNETWORKS_TENSOR_FLOAT32;
   tensor3x4Type.scale = 0.f;    // These fields are used for quantized tensors
   tensor3x4Type.zeroPoint = 0;  // These fields are used for quantized tensors
   tensor3x4Type.dimensionCount = 2;
   uint32_t dims[2] = {3, 4};
   tensor3x4Type.dimensions = dims;
   
   
   // We also specify operands that are activation function specifiers
   ANeuralNetworksOperandType activationType;
   activationType.type = ANEURALNETWORKS_INT32;
   activationType.scale = 0.f;
   activationType.zeroPoint = 0;
   activationType.dimensionCount = 0;
   activationType.dimensions = NULL;
   
   
   // Now we add the seven operands, in the same order defined in the diagram
   ANeuralNetworksModel_addOperand(model, &tensor3x4Type);  // operand 0
   ANeuralNetworksModel_addOperand(model, &tensor3x4Type);  // operand 1
   ANeuralNetworksModel_addOperand(model, &activationType); // operand 2
   ANeuralNetworksModel_addOperand(model, &tensor3x4Type);  // operand 3
   ANeuralNetworksModel_addOperand(model, &tensor3x4Type);  // operand 4
   ANeuralNetworksModel_addOperand(model, &activationType); // operand 5
   ANeuralNetworksModel_addOperand(model, &tensor3x4Type);  // operand 6
   

3. For operands that have constant values, such as weights and biases that your app obtains from a training process, use the ANeuralNetworksModel_setOperandValue() and ANeuralNetworksModel_setOperandValueFromMemory() functions.

   In the following example, we set constant values from the training data file corresponding to the memory buffer we created in Provide access to training data.

   // In our example, operands 1 and 3 are constant tensors whose values were
   // established during the training process
   const int sizeOfTensor = 3 * 4 * 4;    // The formula for size calculation is dim0 * dim1 * elementSize
   ANeuralNetworksModel_setOperandValueFromMemory(model, 1, mem1, 0, sizeOfTensor);
   ANeuralNetworksModel_setOperandValueFromMemory(model, 3, mem1, sizeOfTensor, sizeOfTensor);
   
   
   // We set the values of the activation operands, in our example operands 2 and 5
   int32_t noneValue = ANEURALNETWORKS_FUSED_NONE;
   ANeuralNetworksModel_setOperandValue(model, 2, &noneValue, sizeof(noneValue));
   ANeuralNetworksModel_setOperandValue(model, 5, &noneValue, sizeof(noneValue));
   

4. For each operation in the directed graph you want to compute, add the operation to your model by calling the ANeuralNetworksModel_addOperation() function.

   As parameters to this call, your app must provide:

   • the operation type
   • the count of input values
   • the array of the indexes for input operands
   • the count of output values
   • the array of the indexes for output operands

   Note that an operand cannot be used for both input and output of the same operation.

   // We have two operations in our example
   // The first consumes operands 1, 0, 2, and produces operand 4
   uint32_t addInputIndexes[3] = {1, 0, 2};
   uint32_t addOutputIndexes[1] = {4};
   ANeuralNetworksModel_addOperation(model, ANEURALNETWORKS_ADD, 3, addInputIndexes, 1, addOutputIndexes);
   
   
   // The second consumes operands 3, 4, 5, and produces operand 6
   uint32_t multInputIndexes[3] = {3, 4, 5};
   uint32_t multOutputIndexes[1] = {6};
   ANeuralNetworksModel_addOperation(model, ANEURALNETWORKS_MUL, 3, multInputIndexes, 1, multOutputIndexes);
   

5. Identify which operands the model should treat as its inputs and outputs by calling the ANeuralNetworksModel_identifyInputsAndOutputs() function.

   // Our model has one input (0) and one output (6)
   uint32_t modelInputIndexes[1] = {0};
   uint32_t modelOutputIndexes[1] = {6};
   ANeuralNetworksModel_identifyInputsAndOutputs(model, 1, modelInputIndexes, 1 modelOutputIndexes);
   

6. Optionally, specify whether ANEURALNETWORKS_TENSOR_FLOAT32 is allowed to be calculated with range or precision as low as that of the IEEE 754 16-bit floating-point format by calling ANeuralNetworksModel_relaxComputationFloat32toFloat16().

7. Call ANeuralNetworksModel_finish() to finalize the definition of your model. If there are no errors, this function returns a result code of ANEURALNETWORKS_NO_ERROR.

   ANeuralNetworksModel_finish(model);
   

Once you create a model, you can compile it any number of times and execute each compilation any number of times.

Control flow

To incorporate control flow in an NNAPI model, do the following:

1. Construct the corresponding execution subgraphs (then and else subgraphs for an IF statement, condition and body subgraphs for a WHILE loop) as standalone ANeuralNetworksModel* models:

   ANeuralNetworksModel* thenModel = makeThenModel();
   ANeuralNetworksModel* elseModel = makeElseModel();
   

2. Create operands that reference those models within the model containing the control flow:

   ANeuralNetworksOperandType modelType = {
       .type = ANEURALNETWORKS_MODEL,
   };
   ANeuralNetworksModel_addOperand(model, &modelType);  // kThenOperandIndex
   ANeuralNetworksModel_addOperand(model, &modelType);  // kElseOperandIndex
   ANeuralNetworksModel_setOperandValueFromModel(model, kThenOperandIndex, &thenModel);
   ANeuralNetworksModel_setOperandValueFromModel(model, kElseOperandIndex, &elseModel);
   

3. Add the control flow operation:

   uint32_t inputs[] = {kConditionOperandIndex,
                        kThenOperandIndex,
                        kElseOperandIndex,
                        kInput1, kInput2, kInput3};
   uint32_t outputs[] = {kOutput1, kOutput2};
   ANeuralNetworksModel_addOperation(model, ANEURALNETWORKS_IF,
                                     std::size(inputs), inputs,
                                     std::size(output), outputs);
   

Compilation

The compilation step determines on which processors your model will be executed and asks the corresponding drivers to prepare for its execution. This could include the generation of machine code specific to the processors your model will run on.

To compile a model, follow these steps:

1. Call the ANeuralNetworksCompilation_create() function to create a new compilation instance.

   // Compile the model
   ANeuralNetworksCompilation* compilation;
   ANeuralNetworksCompilation_create(model, &compilation);
   

   Optionally, you can use device assignment to explicitly choose what devices to execute on.

2. You can optionally influence how the runtime trades off between battery power usage and execution speed. You can do so by calling ANeuralNetworksCompilation_setPreference().

   // Ask to optimize for low power consumption
   ANeuralNetworksCompilation_setPreference(compilation, ANEURALNETWORKS_PREFER_LOW_POWER);
   

   The preferences you can specify include:

   • ANEURALNETWORKS_PREFER_LOW_POWER: Prefer executing in a way that minimizes battery drain. This is desirable for compilations that are executed often.
   • ANEURALNETWORKS_PREFER_FAST_SINGLE_ANSWER: Prefer returning a single answer as fast as possible, even if this causes more power consumption. This is the default.
   • ANEURALNETWORKS_PREFER_SUSTAINED_SPEED: Prefer maximizing the throughput of successive frames, such as when processing successive frames coming from the camera.

3. You can optionally set up compilation caching by calling ANeuralNetworksCompilation_setCaching.

   // Set up compilation caching
   ANeuralNetworksCompilation_setCaching(compilation, cacheDir, token);
   

   Use getCodeCacheDir() for the cacheDir. The token specified must be unique to each model within the application.

4. Finalize the compilation definition by calling ANeuralNetworksCompilation_finish(). If there are no errors, this function returns a result code of ANEURALNETWORKS_NO_ERROR.

   ANeuralNetworksCompilation_finish(compilation);
   

Device discovery and assignment

On Android devices running Android 10 (API level 29) and higher, NNAPI provides functions that allow machine learning framework libraries and apps to get information about the devices available and specify devices to be used for execution. Providing information about the available devices allows apps to get the exact version of the drivers found on a device to avoid known incompatibilities. By giving apps the ability to specify which devices are to execute different sections of a model, apps can be optimized for the Android device on which they are deployed.

Device discovery

Use ANeuralNetworks_getDeviceCount to get the number of available devices. For each device, use ANeuralNetworks_getDevice to set an ANeuralNetworksDevice instance to a reference to that device.

Once you have a device reference, you can find out additional information about that device using the following functions:

• ANeuralNetworksDevice_getFeatureLevel
• ANeuralNetworksDevice_getName
• ANeuralNetworksDevice_getType
• ANeuralNetworksDevice_getVersion

Device assignment

Use ANeuralNetworksModel_getSupportedOperationsForDevices to discover which operations of a model can be run on specific devices.

To control which accelerators to use for execution, call ANeuralNetworksCompilation_createForDevices in place of ANeuralNetworksCompilation_create. Use the resulting ANeuralNetworksCompilation object, as normal. The function returns an error if the provided model contains operations that are not supported by the selected devices.

If multiple devices are specified, the runtime is responsible for distributing the work across the devices.

Similar to other devices, the NNAPI CPU implementation is represented by an ANeuralNetworksDevice with the name nnapi-reference and the type ANEURALNETWORKS_DEVICE_TYPE_CPU. When calling ANeuralNetworksCompilation_createForDevices, the CPU implementation is not used to handle the failure cases for model compilation and execution.

It is an application’s responsibility to partition a model into sub-models that can run on the specified devices. Applications that don't need to do manual partitioning should continue to call the simpler ANeuralNetworksCompilation_create to use all available devices (including the CPU) to accelerate the model. If the model couldn't be fully supported by the devices you specified using ANeuralNetworksCompilation_createForDevices, ANEURALNETWORKS_BAD_DATA is returned.

Model partitioning

When multiple devices are available for the model, the NNAPI runtime distributes the work across the devices. For example, if more than one device was provided to ANeuralNetworksCompilation_createForDevices, all the specified ones will be considered when allocating the work. Note that, if the CPU device is not in the list, CPU execution will be disabled. When using ANeuralNetworksCompilation_create all available devices will be taken into account, including CPU.

The distribution is done by selecting from the list of available devices, for each of the operations in the model, the device supporting the operation and declaring the best performance, i.e. the fastest execution time or the lowest power consumption, depending on the execution preference specified by the client. This partitioning algorithm doesn't account for possible inefficiencies caused by the IO between the different processors so, when specifying multiple processors (either explicitly when using ANeuralNetworksCompilation_createForDevices or implicitly by using ANeuralNetworksCompilation_create) it's important to profile the resulting application.

To understand how your model has been partitioned by NNAPI, check the Android logs for a message (at INFO level with tag ExecutionPlan):

ModelBuilder::findBestDeviceForEachOperation(op-name): device-index

op-name is the descriptive name of the operation in the graph and device-index is the index of the candidate device in the list of devices. This list is the input provided to ANeuralNetworksCompilation_createForDevices or, if using ANeuralNetworksCompilation_createForDevices, the list of devices returned when iterating over all devices using ANeuralNetworks_getDeviceCount and ANeuralNetworks_getDevice.

The message (at INFO level with tag ExecutionPlan):

ModelBuilder::partitionTheWork: only one best device: device-name

This message indicates that the whole graph has been accelerated on the device device-name.

Execution

The execution step applies the model to a set of inputs and stores the computation outputs to one or more user buffers or memory spaces that your app allocated.

To execute a compiled model, follow these steps:

1. Call the ANeuralNetworksExecution_create() function to create a new execution instance.

   // Run the compiled model against a set of inputs
   ANeuralNetworksExecution* run1 = NULL;
   ANeuralNetworksExecution_create(compilation, &run1);
   

2. Specify where your app reads the input values for the computation. Your app can read input values from either a user buffer or an allocated memory space by calling ANeuralNetworksExecution_setInput() or ANeuralNetworksExecution_setInputFromMemory() respectively.

   // Set the single input to our sample model. Since it is small, we won't use a memory buffer
   float32 myInput[3][4] = { ...the data... };
   ANeuralNetworksExecution_setInput(run1, 0, NULL, myInput, sizeof(myInput));
   

3. Specify where your app writes the output values. Your app can write output values to either a user buffer or an allocated memory space, by calling ANeuralNetworksExecution_setOutput() or ANeuralNetworksExecution_setOutputFromMemory() respectively.

   // Set the output
   float32 myOutput[3][4];
   ANeuralNetworksExecution_setOutput(run1, 0, NULL, myOutput, sizeof(myOutput));
   

4. Schedule the execution to start, by calling the ANeuralNetworksExecution_startCompute() function. If there are no errors, this function returns a result code of ANEURALNETWORKS_NO_ERROR.

   // Starts the work. The work proceeds asynchronously
   ANeuralNetworksEvent* run1_end = NULL;
   ANeuralNetworksExecution_startCompute(run1, &run1_end);
   

5. Call the ANeuralNetworksEvent_wait() function to wait for the execution to complete. If the execution was successful, this function returns a result code of ANEURALNETWORKS_NO_ERROR. Waiting can be done on a different thread than the one starting the execution.

   // For our example, we have no other work to do and will just wait for the completion
   ANeuralNetworksEvent_wait(run1_end);
   ANeuralNetworksEvent_free(run1_end);
   ANeuralNetworksExecution_free(run1);
   

6. Optionally, you can apply a different set of inputs to the compiled model by using the same compilation instance to create a new ANeuralNetworksExecution instance.

   // Apply the compiled model to a different set of inputs
   ANeuralNetworksExecution* run2;
   ANeuralNetworksExecution_create(compilation, &run2);
   ANeuralNetworksExecution_setInput(run2, ...);
   ANeuralNetworksExecution_setOutput(run2, ...);
   ANeuralNetworksEvent* run2_end = NULL;
   ANeuralNetworksExecution_startCompute(run2, &run2_end);
   ANeuralNetworksEvent_wait(run2_end);
   ANeuralNetworksEvent_free(run2_end);
   ANeuralNetworksExecution_free(run2);
   

Synchronous execution

Asynchronous execution spends time to spawn and synchronize threads. Furthermore, the latency can be hugely variable, with the longest delays reaching up to 500 microseconds between the time a thread is notified or woken and the time it is eventually bound to a CPU core.

To improve latency, you can instead direct an application to make a synchronous inference call to the runtime. That call will return only once an inference has been completed rather than returning once an inference has been started. Instead of calling ANeuralNetworksExecution_startCompute for an asynchronous inference call to the runtime, the application calls ANeuralNetworksExecution_compute to make a synchronous call to the runtime. A call to ANeuralNetworksExecution_compute does not take an ANeuralNetworksEvent and is not paired with a call to ANeuralNetworksEvent_wait.

Burst executions

On Android devices running Android 10 (API level 29) and higher, the NNAPI supports burst executions through the ANeuralNetworksBurst object. Burst executions are a sequence of executions of the same compilation that occur in rapid succession, such as those operating on frames of a camera capture or successive audio samples. Using ANeuralNetworksBurst objects may result in faster executions, as they indicate to accelerators that resources may be reused between executions and that accelerators should remain in a high-performance state for the duration of the burst.

ANeuralNetworksBurst introduces only a small change in the normal execution path. You create a burst object using ANeuralNetworksBurst_create, as shown in the following code snippet:

// Create burst object to be reused across a sequence of executions
ANeuralNetworksBurst* burst = NULL;
ANeuralNetworksBurst_create(compilation, &burst);

Burst executions are synchronous. However, instead of using ANeuralNetworksExecution_compute to perform each inference, you pair the various ANeuralNetworksExecution objects with the same ANeuralNetworksBurst in calls to the function ANeuralNetworksExecution_burstCompute.

// Create and configure first execution object
// ...

// Execute using the burst object
ANeuralNetworksExecution_burstCompute(execution1, burst);

// Use results of first execution and free the execution object
// ...

// Create and configure second execution object
// ...

// Execute using the same burst object
ANeuralNetworksExecution_burstCompute(execution2, burst);

// Use results of second execution and free the execution object
// ...

Free the ANeuralNetworksBurst object with ANeuralNetworksBurst_free when it is no longer needed.

// Cleanup
ANeuralNetworksBurst_free(burst);

Dynamically sized outputs

To support models where the size of the output depends on the input data—that is, where the size cannot be determined at model execution time—use ANeuralNetworksExecution_getOutputOperandRank and ANeuralNetworksExecution_getOutputOperandDimensions.

The following code sample shows how to do this:

// Get the rank of the output
uint32_t myOutputRank = 0;
ANeuralNetworksExecution_getOutputOperandRank(run1, 0, &myOutputRank);

// Get the dimensions of the output
std::vector<uint32_t> myOutputDimensions(myOutputRank);
ANeuralNetworksExecution_getOutputOperandDimensions(run1, 0, myOutputDimensions.data());

Cleanup

The cleanup step handles the freeing of internal resources used for your computation.

// Cleanup
ANeuralNetworksCompilation_free(compilation);
ANeuralNetworksModel_free(model);
ANeuralNetworksMemory_free(mem1);

Error management and CPU fallback

If there is an error during partitioning, if a driver fails to compile a (piece of a) model, or if a driver fails to execute a compiled (piece of a) model, NNAPI might fall back to its own CPU implementation of the one or more operations.

If the NNAPI client contains optimized versions of the operation (as, for example, TFLite) it might be advantageous to disable the CPU fallback and handle the failures with the client's optimized operation implementation.

In Android 10, if compilation is performed using ANeuralNetworksCompilation_createForDevices, then CPU fallback will be disabled.

In Android P, NNAPI execution falls back to the CPU if execution on the driver fails. This is also true on Android 10 when ANeuralNetworksCompilation_create rather than ANeuralNetworksCompilation_createForDevices is used.

First execution falls back for that single partition, and if that still fails, it retries the entire model on the CPU.

If partitioning or compilation fails, the entire model will be tried on CPU.

There are cases where some operations are not supported on CPU, and in such situations compilation or execution will fail rather than falling back.

Even after disabling CPU fallback, there may still be operations in the model that are scheduled on the CPU. If the CPU is in the list of processors supplied to ANeuralNetworksCompilation_createForDevices, and is either the only processor that supports those operations or is the processor that claims best performance for those operations, it will be chosen as a primary (non-fallback) executor.

To ensure there is no CPU execution, use ANeuralNetworksCompilation_createForDevices while excluding the nnapi-reference from the list of devices. Starting in Android P, it is possible to disable fallback at execution time on DEBUG builds by setting the debug.nn.partition property to 2.

Measure performance

You can evaluate your app's performance by measuring execution time or by profiling.

Execution time

When you want to determine total execution time through the runtime, you can use the synchronous execution API and measure the time taken by the call. When you want to determine total execution time through a lower level of the software stack, you can use ANeuralNetworksExecution_setMeasureTiming and ANeuralNetworksExecution_getDuration to get:

• execution time on an accelerator (not in the driver, which runs on the host processor).
• execution time in the driver, including time on the accelerator.

The execution time in the driver excludes overhead such as that of the runtime itself and the IPC needed for the runtime to communicate with the driver.

These APIs measure duration between the work submitted and work completed events, rather than the time a driver or accelerator devotes to performing the inference, possibly interrupted by context switching.

For example, if inference 1 begins, then the driver stops work to perform inference 2, then it resumes and completes inference 1, the execution time for inference 1 will include the time when work was stopped to perform inference 2.

This timing information may be useful for a production deployment of an application to collect telemetry for offline use. You can use the timing data to modify the app for higher performance.

When using this functionality, bear in mind the following:

• Collecting timing information might have a performance cost.
• Only a driver is capable of computing the time spent in itself or on the accelerator, excluding time spent in NNAPI runtime and in IPC.
• You can use these APIs only with an ANeuralNetworksExecution that was created with ANeuralNetworksCompilation_createForDevices with numDevices = 1.
• No driver is required to be able to report timing information.

Profile your application with Android Systrace

Starting with Android 10, NNAPI automatically generates systrace events that you can use to profile your application.

The NNAPI Source comes with a parse_systrace utility to process the systrace events generated by your application and generate a table view showing the time spent in the different phases of the model lifecycle (Instantiation, Preparation, Compilation Execution and Termination) and different layers of the applications. The layers in which your application is split are:

• Application: the main application code
• Runtime: NNAPI Runtime
• IPC: The inter process communication between NNAPI Runtime and the Driver code
• Driver: the accelerator driver process.

Generate the profiling analysys data

Assuming you checked out the AOSP source tree at $ANDROID_BUILD_TOP, and using the TFLite image classification example as target application, you can generate the NNAPI profiling data with the following steps:

1. Start the Android systrace with the following command:

$ANDROID_BUILD_TOP/external/chromium-trace/systrace.py  -o trace.html -a org.tensorflow.lite.examples.classification nnapi hal freq sched idle load binder_driver

The -o trace.html parameter indicates that the traces will be written in the trace.html. When profiling own application you will need to replace org.tensorflow.lite.examples.classification with the process name specified in your app manifest.

This will keep one of your shell console busy, don't run the command in background since it is interactively waiting for an enter to terminate.

1. After the systrace collector is started, start your app and run your benchmark test.

In our case you can start the Image Classification app from Android Studio or directly from your test phone UI if the app has already been installed. To generate some NNAPI data you need to configure the app to use NNAPI by selecting NNAPI as target device in the app configuration dialog.

1. When the test completes, terminate the systrace by pressing enter on the console terminal active since step 1.

2. Run the systrace_parser utility generate cumulative statistics:

$ANDROID_BUILD_TOP/frameworks/ml/nn/tools/systrace_parser/parse_systrace.py --total-times trace.html

The parser accepts the following parameters: - --total-times: shows the total time spent in a layer including the time spent waiting for execution on a call to an underlying layer - --print-detail: prints all the events that have been collected from systrace - --per-execution: prints only the execution and its subphases (as per-execution times) instead of stats for all phases - --json: produces the output in JSON format

An example of the output is shown below:

===========================================================================================================================================
NNAPI timing summary (total time, ms wall-clock)                                                      Execution
                                                           ----------------------------------------------------
              Initialization   Preparation   Compilation           I/O       Compute      Results     Ex. total   Termination        Total
              --------------   -----------   -----------   -----------  ------------  -----------   -----------   -----------   ----------
Application              n/a         19.06       1789.25           n/a           n/a         6.70         21.37           n/a      1831.17*
Runtime                    -         18.60       1787.48          2.93         11.37         0.12         14.42          1.32      1821.81
IPC                     1.77             -       1781.36          0.02          8.86            -          8.88             -      1792.01
Driver                  1.04             -       1779.21           n/a           n/a          n/a          7.70             -      1787.95

Total                   1.77*        19.06*      1789.25*         2.93*        11.74*        6.70*        21.37*         1.32*     1831.17*
===========================================================================================================================================
* This total ignores missing (n/a) values and thus is not necessarily consistent with the rest of the numbers

The parser might fail if the collected events do not represent a complete application trace. In particular it might fail if systrace events generated to mark the end of a section are present in the trace without an associated section start event. This usually happens if some events from a previous profiling session are being generated when you start the systrace collector. In this case you would have to run your profiling again.

Add statistics for your application code to systrace_parser output

The parse_systrace application is based on the built-in Android systrace functionality. You can add traces for specific operations in your app using the systrace API (for Java , for native applications ) with custom event names.

To associate your custom events with phases of the Application lifecycle, prepend your event name with one of the following strings:

• [NN_LA_PI]: Application level event for Initialization
• [NN_LA_PP]: Application level event for Preparation
• [NN_LA_PC]: Application level event for Compilation
• [NN_LA_PE]: Application level event for Execution

Here is an example of how you can alter the TFLite image classification example code by adding a runInferenceModel section for the Execution phase and the Application layer containing another other sections preprocessBitmap that won't be considered in NNAPI traces. The runInferenceModel section will be part of the systrace events processed by the nnapi systrace parser:

/** Runs inference and returns the classification results. */
fun recognizeImage(bitmap: Bitmap): List {
   // This section won’t appear in the NNAPI systrace analysis
   Trace.beginSection("preprocessBitmap")
   convertBitmapToByteBuffer(bitmap)
   Trace.endSection()

   // Run the inference call.
   // Add this method in to NNAPI systrace analysis.
   Trace.beginSection("[NN_LA_PE]runInferenceModel")
   long startTime = SystemClock.uptimeMillis()
   runInference()
   long endTime = SystemClock.uptimeMillis()
   Trace.endSection()
    ...
   return recognitions
}

/** Runs inference and returns the classification results. */
public List recognizeImage(final Bitmap bitmap) {

 // This section won’t appear in the NNAPI systrace analysis
 Trace.beginSection("preprocessBitmap");
 convertBitmapToByteBuffer(bitmap);
 Trace.endSection();

 // Run the inference call.
 // Add this method in to NNAPI systrace analysis.
 Trace.beginSection("[NN_LA_PE]runInferenceModel");
 long startTime = SystemClock.uptimeMillis();
 runInference();
 long endTime = SystemClock.uptimeMillis();
 Trace.endSection();
  ...
 Trace.endSection();
 return recognitions;
}

More about operands

The following section covers advanced topics about using operands.

Quantized tensors

A quantized tensor is a compact way to represent an n-dimensional array of floating point values.

NNAPI supports 8-bit asymmetric quantized tensors. For these tensors, the value of each cell is represented by an 8-bit integer. Associated with the tensor is a scale and a zero point value. These are used to convert the 8-bit integers into the floating point values that are being represented.

The formula is:

(cellValue - zeroPoint) * scale

where the zeroPoint value is a 32-bit integer and the scale a 32-bit floating point value.

Compared to tensors of 32-bit floating point values, 8-bit quantized tensors have two advantages:

• Your application is smaller, as the trained weights take a quarter of the size of 32-bit tensors.
• Computations can often be executed faster. This is due to the smaller amount of data that needs to be fetched from memory and the efficiency of processors such as DSPs in doing integer math.

While it is possible to convert a floating point model to a quantized one, our experience has shown that better results are achieved by training a quantized model directly. In effect, the neural network learns to compensate for the increased granularity of each value. For each quantized tensor, the scale and zeroPoint values are determined during the training process.

In NNAPI, you define quantized tensors types by setting the type field of the ANeuralNetworksOperandType data structure to ANEURALNETWORKS_TENSOR_QUANT8_ASYMM. You also specify the scale and zeroPoint value of the tensor in that data structure.

In addition to 8-bit asymmetric quantized tensors, NNAPI supports the following:

• ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL which you can use for representing weights to CONV/DEPTHWISE_CONV/TRANSPOSED_CONV operations.
• ANEURALNETWORKS_TENSOR_QUANT16_ASYMM which you can use for the internal state of QUANTIZED_16BIT_LSTM.
• ANEURALNETWORKS_TENSOR_QUANT8_SYMM which can be an input to ANEURALNETWORKS_DEQUANTIZE.

Optional operands

A few operations, like ANEURALNETWORKS_LSH_PROJECTION, take optional operands. To indicate in the model that the optional operand is omitted, call the ANeuralNetworksModel_setOperandValue() function, passing NULL for the buffer and 0 for the length.

If the decision on whether the operand is present or not varies for each execution, you indicate that the operand is omitted by using the ANeuralNetworksExecution_setInput() or ANeuralNetworksExecution_setOutput() functions, passing NULL for the buffer and 0 for the length.

Tensors of unknown rank

Android 9 (API level 28) introduced model operands of unknown dimensions but known rank (the number of dimensions). Android 10 (API level 29) introduced tensors of unknown rank, as shown in ANeuralNetworksOperandType.

NNAPI benchmark

The NNAPI benchmark is available on AOSP in platform/test/mlts/benchmark (benchmark app) and platform/test/mlts/models (models and datasets).

The benchmark evaluates latency and accuracy and compares drivers to the same work done using Tensorflow Lite running on the CPU, for the same models and datasets.

To use the benchmark, do the following:

1. Connect a target Android device to your computer, open a terminal window, and make sure the device is reachable through adb.

2. If more than one Android device is connected, export the target device ANDROID_SERIAL environment variable.

3. Navigate to the Android top-level source directory.

4. Run the following commands:

   lunch aosp_arm-userdebug # Or aosp_arm64-userdebug if available
   ./test/mlts/benchmark/build_and_run_benchmark.sh
   

   At the end of a benchmark run, its results will be presented as an HTML page passed to xdg-open.

NNAPI logs

NNAPI generates useful diagnostic information in the system logs. To analyze the logs, use the logcat utility.

Enable verbose NNAPI logging for specific phases or components by setting the property debug.nn.vlog (using adb shell) to the following list of values, separated by space, colon, or comma:

• model: Model building
• compilation: Generation of the model execution plan and compilation
• execution: Model execution
• cpuexe: Execution of operations using the NNAPI CPU implementation
• manager: NNAPI extensions, available interfaces and capabilities related info
• all or 1: All the elements above

For example, to enable full verbose logging use the command adb shell setprop debug.nn.vlog all. To disable verbose logging, use the command adb shell setprop debug.nn.vlog '""'.

Once enabled, verbose logging generates log entries at INFO level with a tag set to the phase or component name.

Beside the debug.nn.vlog controlled messages, NNAPI API components provide other log entries at various levels, each one using a specific log tag.

To get a list of components, search the source tree using the following expression:

grep -R 'define LOG_TAG' | awk -F '"' '{print $2}' | sort -u | egrep -v "Sample|FileTag|test"

This expression currently returns the following tags:

• BurstBuilder
• Callbacks
• CompilationBuilder
• CpuExecutor
• ExecutionBuilder
• ExecutionBurstController
• ExecutionBurstServer
• ExecutionPlan
• FibonacciDriver
• GraphDump
• IndexedShapeWrapper
• IonWatcher
• Manager
• Memory
• MemoryUtils
• MetaModel
• ModelArgumentInfo
• ModelBuilder
• NeuralNetworks
• OperationResolver
• Operations
• OperationsUtils
• PackageInfo
• TokenHasher
• TypeManager
• Utils
• ValidateHal
• VersionedInterfaces

To control the level of log messages shown by logcat, use the environment variable ANDROID_LOG_TAGS.

To show the full set of NNAPI log messages and disable any others, set ANDROID_LOG_TAGS to the following:

BurstBuilder:V Callbacks:V CompilationBuilder:V CpuExecutor:V ExecutionBuilder:V ExecutionBurstController:V ExecutionBurstServer:V ExecutionPlan:V FibonacciDriver:V GraphDump:V IndexedShapeWrapper:V IonWatcher:V Manager:V MemoryUtils:V Memory:V MetaModel:V ModelArgumentInfo:V ModelBuilder:V NeuralNetworks:V OperationResolver:V OperationsUtils:V Operations:V PackageInfo:V TokenHasher:V TypeManager:V Utils:V ValidateHal:V VersionedInterfaces:V *:S.

You can set ANDROID_LOG_TAGS using the following command:

export ANDROID_LOG_TAGS=$(grep -R 'define LOG_TAG' | awk -F '"' '{ print $2 ":V" }' | sort -u | egrep -v "Sample|FileTag|test" | xargs echo -n; echo ' *:S')

Note that this is just a filter that applies to logcat. You still need to set the property debug.nn.vlog to all to generate verbose log info.