LSFQ: A Low-Bit Full Integer Quantization for High-Performance FPGA-Based CNN Acceleration

Zhenshan Bao, Guohang Fu,Wenbo Zhang,Kang Zhan,Junnan Guo

IEEE Micro（2022）

Cited 2|Views18

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Abstract

The effective implementation of quantization depends not only on the specific task but also on the hardware resources. This article presents a hardware-aware customized quantization method for convolutional neural networks. We propose a learnable parameter soft clipping full integer quantization (LSFQ), which includes weight and activation quantization with the learnable clipping parameters. Moreo...

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Key words

Quantization (signal),Convolutional neural networks,Design automation,Computer architecture,Field programmable gate arrays,Training data,Neural networks,Accelerator architectures

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