Google is committed to advancing racial equity for Black communities. See how.

Vendor Extensions

Neural Networks API (NNAPI) vendor extensions, introduced in Android 10, are collections of vendor-defined operations and data types. On devices running NN HAL 1.2 or higher, drivers can provide custom hardware-accelerated operations by supporting corresponding vendor extensions. Vendor extensions don't modify the behavior of existing operations.

Vendor extensions provide a more structured alternative to OEM operation and data types, which were deprecated in Android 10. For more information, see OEM operation and data types.

Extensions usage allowlist

Vendor extensions can only be used by explicitly specified Android apps and native binaries on the /product, /vendor, /odm, and /data partitions. Apps and native binaries located on the /system partition can't use vendor extensions.

A list of Android apps and binaries permitted to use NNAPI vendor extensions is stored in /vendor/etc/nnapi_extensions_app_allowlist. Each line of the file contains a new entry. An entry can be a native binary path that is prefixed with a slash (/), for example, /data/foo, or a name of an Android app package, for example,

The allowlist is enforced from the NNAPI runtime shared library. This library protects against accidental usage but not against deliberate circumvention by an app directly using the NNAPI driver HAL interface.

Vendor extension definition

The vendor creates and maintains a header file with the extension definition. A complete example of an extension definition can be found in example/fibonacci/FibonacciExtension.h.

Each extension must have a unique name that starts with the reverse domain name of the vendor.

const char EXAMPLE_EXTENSION_NAME[] = "com.example.my_extension";

The name acts as a namespace for operations and data types. The NNAPI uses this name to distinguish between vendor extensions.

Operations and data types are declared in a way similar to those in runtime/include/NeuralNetworks.h.

enum {
     * A custom scalar type.

     * A custom tensor type.
     * Attached to this tensor is {@link ExampleTensorParams}.

enum {
     * Computes example function.
     * Inputs:
     * * 0: A scalar of {@link EXAMPLE_SCALAR}.
     * Outputs:
     * * 0: A tensor of {@link EXAMPLE_TENSOR}.

An extension operation can use any operand type, including nonextension operand types and operand types from other extensions. When using an operand type from another extension, the driver must support the other extension.

Extensions can also declare custom structures to accompany extension operands.

 * Quantization parameters for {@link EXAMPLE_TENSOR}.
typedef struct ExampleTensorParams {
    double scale;
    int64_t zeroPoint;
} ExampleTensorParams;

Using extensions in NNAPI clients

The runtime/include/NeuralNetworksExtensions.h (C API) file provides runtime extension support. This section provides an overview of the C API.

To check whether a device supports an extension, use ANeuralNetworksDevice_getExtensionSupport.

bool isExtensionSupported;
CHECK_EQ(ANeuralNetworksDevice_getExtensionSupport(device, EXAMPLE_EXTENSION_NAME,
if (isExtensionSupported) {
    // The device supports the extension.

To build a model with an extension operand, use ANeuralNetworksModel_getExtensionOperandType to obtain the operand type and call ANeuralNetworksModel_addOperand.

int32_t type;
CHECK_EQ(ANeuralNetworksModel_getExtensionOperandType(model, EXAMPLE_EXTENSION_NAME, EXAMPLE_TENSOR, &type),
ANeuralNetworksOperandType operandType{
        .type = type,
        .dimensionCount = dimensionCount,
        .dimensions = dimensions,
CHECK_EQ(ANeuralNetworksModel_addOperand(model, &operandType), ANEURALNETWORKS_NO_ERROR);

Optionally, use ANeuralNetworksModel_setOperandExtensionData to associate additional data with an extension operand.

ExampleTensorParams params{
        .scale = 0.5,
        .zeroPoint = 128,
CHECK_EQ(ANeuralNetworksModel_setOperandExtensionData(model, operandIndex, &params, sizeof(params)),

To build a model with an extension operation, use ANeuralNetworksModel_getExtensionOperationType to obtain the operation type and call ANeuralNetworksModel_addOperation.

ANeuralNetworksOperationType type;
CHECK_EQ(ANeuralNetworksModel_getExtensionOperationType(model, EXAMPLE_EXTENSION_NAME, EXAMPLE_FUNCTION,
CHECK_EQ(ANeuralNetworksModel_addOperation(model, type, inputCount, inputs, outputCount, outputs),

Adding extension support to an NNAPI driver

Drivers report supported extensions through the IDevice::getSupportedExtensions method. The returned list must contain an entry describing each supported extension.

Extension {
    .operandTypes = {
            .type = EXAMPLE_SCALAR,
            .isTensor = false,
            .byteSize = 8,
            .type = EXAMPLE_TENSOR,
            .isTensor = true,
            .byteSize = 8,

Of the 32 bits used to identify types and operations, the high Model::ExtensionTypeEncoding::HIGH_BITS_PREFIX bits is the extension prefix and the low Model::ExtensionTypeEncoding::LOW_BITS_TYPE bits represents the type or operation of the extension.

When handling an operation or operand type, the driver must check the extension prefix. If the extension prefix has a nonzero value, the operation or operand type is an extension type. If the value is 0, the operation or operand type isn't an extension type.

To map the prefix to an extension name, look it up in model.extensionNameToPrefix. The mapping from the prefix to the extension name is a one-to-one correspondence (bijection) for a given model. Different prefix values might correspond to the same extension name in different models.

The driver must validate extension operations and data types because the NNAPI runtime can't validate particular extension operations and data types.

Extension operands can have associated data in operand.extraParams.extension, which the runtime treats as a raw data blob of arbitrary size.

OEM operation and data types

NNAPI has an OEM operation and OEM data types to allow device manufacturers to provide custom, driver-specific functionality. These operation and data types are only used by OEM applications. The semantics of OEM operation and data types are OEM-specific and can change at any time. The OEM operation and data types are encoded using OperationType::OEM_OPERATION, OperandType::OEM, and OperandType::TENSOR_OEM_BYTE.