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