ONNX operators that the Arm NN SDK supports

This reference guide provides a list of ONNX operators the Arm NN SDK currently supports.

The Arm NN SDK ONNX parser currently only supports fp32 operators.

Fully supported

Add

See the ONNX Add documentation for more information

AveragePool

See the ONNX AveragePool documentation for more information.

Constant

See the ONNX Constant documentation for more information.

Clip

See the ONNX Clip documentation for more information.

Flatten

See the ONNX Flatten documentation for more information.

GlobalAveragePool

See the ONNX GlobalAveragePool documentation for more information.

LeakyRelu

See the ONNX LeakyRelu documentation for more information.

MaxPool

See the ONNX max_pool documentation for more information.

Relu

See the ONNX Relu documentation for more information.

Reshape

See the ONNX Reshape documentation for more information.

Sigmoid

See the ONNX Sigmoid documentation for more information.

Tanh

See the ONNX Tanh documentation for more information.

Partially supported

Conv

The parser only supports 2D convolutions with a dilation rate of [1, 1] and group = 1 or group = #Nb_of_channel (depthwise convolution) See the ONNX Conv documentation for more information.

BatchNormalization

The parser does not support training mode. See the ONNX BatchNormalization documentation for more information.

MatMul

The parser only supports constant weights in a fully connected layer.

Tested networks

Arm tested these operators with the following ONNX fp32 neural networks:

• Mobilenet_v2. See the ONNX MobileNet documentation for more information.

• Simple MNIST. This is no longer directly documented by ONNX. The model and test data may be downloaded from the ONNX model zoo.

More machine learning operators will be supported in future releases as time allows. If you require specific operator support contribution are welcome.