Model Compatibility

In the Ryzen AI workflow, the quantized model is converted into ONNX format for deployment. Currently, the IPU supports a subset of ONNX operators. However, with the Vitis AI ONNX Execution Provider (VAI EP), the neural network is automatically partitioned into multiple subgraphs. The subgraph(s) containing IPU-supported operators are executed on the IPU, while the remaining subgraph(s) containing IPU-incompatible operators are executed on the CPU. This “Model Parallel” deployment technique across the CPU and IPU is fully automated. VAI EP manages it and is transparent to the end-user.

The current list of the IPU-supported ONNX operators is as follows:

• Add

• And

• Average pool

• BatchNorm

• Clip

• Concat

• Conv

• ConvTranspose

• Gemm

• GlobalAveragePool

• GlobalMaxPool

• Identity

• LayerNorm

• LSTM

• Max

• MaxPool

• Mul

• Pad

• Prelu

• Relu

• Resize

• RNN

• Sigmoid

• Slice

• Softmax

• Split

• Squeeze

• Unsqueeze

• Upsample

• Spacetodepth