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.

Understanding 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.

Figure 1. System architecture for Android Neural Networks API

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.

Figure 2. Programming flow for Android Neural Networks API

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.

Providing 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.

Using 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:



  • You can use AHardwareBuffer only for the whole buffer; you cannot use it with an ARect parameter.
  • The NNAPI runtime will not flush the buffer. You need to make sure that the input and output buffers are accessible before scheduling the execution.
  • There is no support for sync fence file descriptors.
  • For an AHardwareBuffer with vendor-specific formats and usage bits, it is up to the vendor implementation to determine whether the client or the driver is responsible for flushing the cache.


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


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.

Figure 3. Example of operands for an NNAPI model

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.


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:

Category Operations
Element-wise mathematical operations
Tensor manipulation
Image operations
Lookup operations
Normalization operations
Convolution operations
Pooling operations
Activation operations
Other operations

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.

Building 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;
  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 Providing 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.


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


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:

  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.


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:

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.


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
  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);

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

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,;

Measure performance

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.


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

// Cleanup

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:

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

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