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NNAPI Execution Provider

Android Neural Networks API (NNAPI) is a unified interface to CPU, GPU, and NN accelerators on Android.

Contents

Requirements

The NNAPI Execution Provider (EP) requires Android devices with Android 8.1 or higher. It is recommended to use Android devices with Android 9 or higher to achieve optimal performance.

Install

Pre-built packages of ONNX Runtime Mobile with NNAPI EP for Android are published on Maven: com.microsoft.onnxruntime:onnxruntime-mobile.

Build

Please see the ONNX Runtime Mobile deployment information for instructions on building or using a pre-built package that includes the NNAPI EP.

Usage

The ONNX Runtime API details are here.

The NNAPI EP can be used via the C, C++ or Java APIs

The NNAPI EP must be explicitly registered when creating the inference session. For example:

Ort::Env env = Ort::Env{ORT_LOGGING_LEVEL_ERROR, "Default"};
Ort::SessionOptions so;
uint32_t nnapi_flags = 0;
Ort::ThrowOnError(OrtSessionOptionsAppendExecutionProvider_Nnapi(so, nnapi_flags));
Ort::Session session(env, model_path, so);

Configuration Options

There are several run time options available for the NNAPI EP.

To use the NNAPI EP run time options, create an unsigned integer representing the options, and set each individual option by using the bitwise OR operator.

uint32_t nnapi_flags = 0;
nnapi_flags |= NNAPI_FLAG_USE_FP16;

Available Options

NNAPI_FLAG_USE_FP16

Use fp16 relaxation in NNAPI EP.

This may improve performance but can also reduce accuracy due to the lower precision.

NNAPI_FLAG_USE_NCHW

Use the NCHW layout in NNAPI EP.

This is only available for Android API level 29 and higher. Please note that for now, NNAPI might have worse performance using NCHW compared to using NHWC.

NNAPI_FLAG_CPU_DISABLED

Prevent NNAPI from using CPU devices.

NNAPI is more efficient using GPU or NPU for execution, however NNAPI might fall back to its CPU implementation for operations that are not supported by GPU/NPU. The CPU implementation of NNAPI (which is called nnapi-reference) is often less efficient than the optimized versions of the operation of ORT. Due to this, it may be advantageous to disable the NNAPI CPU fallback and handle execution using ORT kernels.

For some models, if NNAPI would use CPU to execute an operation, and this flag is set, the execution of the model may fall back to ORT kernels.

This option is only available for Android API level 29 and higher, and will be ignored for Android API level 28 and lower.

For NNAPI device assignments, see https://developer.android.com/ndk/guides/neuralnetworks#device-assignment

For NNAPI CPU fallback, see https://developer.android.com/ndk/guides/neuralnetworks#cpu-fallback

NNAPI_FLAG_CPU_ONLY

Using CPU only in NNAPI EP, this may decrease the perf but will provide reference output value without precision loss, which is useful for validation.

This option is only available for Android API level 29 and higher, and will be ignored for Android API level 28 and lower.

Supported ops

Following ops are supported by the NNAPI Execution Provider,

Operator Note
ai.onnx:Abs  
ai.onnx:Add  
ai.onnx:AveragePool Only 2D Pool is supported.
ai.onnx:Cast  
ai.onnx:Clip  
ai.onnx:Concat  
ai.onnx:Conv Only 2D Conv is supported.
Weights and bias should be constant.
ai.onnx:DequantizeLinear All quantization scales and zero points should be constant.
ai.onnx:Div  
ai.onnx:Elu  
ai.onnx:Exp  
ai.onnx:Flatten  
ai.onnx:Floor  
ai.onnx:Gemm If input B is not constant, transB should be 1.
ai.onnx:GlobalAveragePool Only 2D Pool is supported.
ai.onnx:GlobalMaxPool Only 2D Pool is supported.
ai.onnx:Identity  
ai.onnx:Log  
ai.onnx:MatMul  
ai.onnx:MaxPool Only 2D Pool is supported.
ai.onnx:Max  
ai.onnx:Min  
ai.onnx:Mul  
ai.onnx:Neg  
ai.onnx:Pow  
ai.onnx:QLinearConv Only 2D Conv is supported.
Weights and bias should be constant.
All quantization scales and zero points should be constant.
ai.onnx:QLinearMatMul All quantization scales and zero points should be constant.
ai.onnx:QuantizeLinear All quantization scales and zero points should be constant.
ai.onnx:Relu  
ai.onnx:Reshape  
ai.onnx:Resize Only 2D Resize is supported.
ai.onnx:Sigmoid  
ai.onnx:Sin  
ai.onnx:Slice  
ai.onnx:Softmax  
ai.onnx:Sqrt  
ai.onnx:Squeeze  
ai.onnx:Sub  
ai.onnx:Tanh  
ai.onnx:Transpose  
com.microsoft:QLinearAdd All quantization scales and zero points should be constant.
com.microsoft:QLinearAveragePool Only 2D Pool is supported.
All quantization scales and zero points should be constant.
com.microsoft:QLinearSigmoid All quantization scales and zero points should be constant.