A novel hardware acceleration method for Deep Neural Networks-Reference-Cited by-同舟云学术

A novel hardware acceleration method for Deep Neural Networks

Published:2023-12-14 Issue: Volume: Page:573-578
ISSN:
Container-title:Proceedings of the 2023 9th International Conference on Communication and Information Processing
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Author:

Yan Dingjiang¹^ORCID,He Fangzhou¹^ORCID,Cai Fenglin¹^ORCID,Jiang Mingfei²^ORCID,Li Jie¹^ORCID

Affiliation:

1. School of Intelligent Technology and Engineering, Chongqing University of Science and Technology, China

2. School of Information Science and Technology, Tibet University, China

Funder

Luzhou Science and Technology Plan Project

Chongqing Municipal undergraduate universities and institutes affiliated to the Chinese Academy of Sciences in 2021

Key Project of Chongqing Education Commission?s Science and Technology Research Program

Sichuan Provincial Science and Technology Plan Project

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3638884.3638974

Reference22 articles.

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3. Jungwook Choi, Zhuo Wang, Swagath Venkataramani, Pierce I-Jen Chuang, Vijayalakshmi Srinivasan, and Kailash Gopalakrishnan. 2018. Pact: Parameterized clipping activation for quantized neural networks. arXiv preprint arXiv:1805.06085 (2018).

4. DeepShift: Towards Multiplication-Less Neural Networks

5. Song Han, Huizi Mao, and William J Dally. 2015. Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding. arXiv preprint arXiv:1510.00149 (2015).