QONNX: Representing Arbitrary-Precision Quantized Neural Networks. (arXiv:2206.07527v3 [cs.LG] UPDATED)

We present extensions to the Open Neural Network Exchange (ONNX) intermediate
representation format to represent arbitrary-precision quantized neural
networks. We first introduce support for low precision quantization in existing
ONNX-based quantization formats by leveraging integer clipping, resulting in
two new backward-compatible variants: the quantized operator format with
clipping and quantize-clip-dequantize (QCDQ) format. We then introduce a novel
higher-level ONNX format called quantized ONNX (QONNX) that introduces three
new operators -- Quant, BipolarQuant, and Trunc -- in order to represent
uniform …

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