Asymmetric quantization in hardware accelerator

Kuen Hung Tsoi; Chao Xiong; Wei Zou; Xinyu Niu

doi:10.1117/12.3009562

16 October 2023 Asymmetric quantization in hardware accelerator

Kuen Hung Tsoi, Chao Xiong, Wei Zou, Xinyu Niu

Proceedings Volume 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023); 128033P (2023) https://doi.org/10.1117/12.3009562
Event: 2023 5th International Conference on Artificial Intelligence and Computer Science (AICS 2023), 2023, Wuhan, China

Abstract

This paper presents an efficient implementation of asymmetric quantization in hardware accelerator for deep learning applications. In this work, we show that asymmetric quantization provides better accuracy performance in AI inferencing with the same amount of storage and bandwidth requirements of a symmetric approach. Also, we provide the method to support the asymmetric approach in digital circuit. The results show that this software and hardware collaboration provide sufficient AI performance while achieving over significant silicon resources reduction.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Kuen Hung Tsoi, Chao Xiong, Wei Zou, and Xinyu Niu "Asymmetric quantization in hardware accelerator", Proc. SPIE 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023), 128033P (16 October 2023); https://doi.org/10.1117/12.3009562

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