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Derek Chickles

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Pre-Quantized Deep Learning Models Codified in ONNX to Enable Hardware/Software Co-Design

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Oct 04, 2021
Ulf Hanebutte, Andrew Baldwin, Senad Durakovic, Igor Filipovich, Chien-Chun, Chou, Damian Adamowicz, Derek Chickles, David Hawkes

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This paper presents a methodology to separate the quantization process from the hardware-specific model compilation stage via a pre-quantized deep learning model description in standard ONNX format. Separating the quantization process from the model compilation stage enables independent development. The methodology is expressive to convey hardware-specific operations and to embed key quantization parameters into a ONNX model which enables hardware/software co-design. Detailed examples are given for both MLP and CNN based networks, which can be extended to other networks in a straightforward fashion.

* 5 pages, 6 figures 
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