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JBNN: A Hardware Design for Binarized Neural Networks Using Single-Flux-Quantum Circuits

文献类型：期刊论文

作者Fu, Rongliang2; Huang, Junying1; Wu, Haibin1; Ye, Xiaochun1; Fan, Dongrui1; Ho, Tsung-Yi2
刊名IEEE TRANSACTIONS ON COMPUTERS
出版日期2022-12-01
卷号71期号:12页码:3203-3214
关键词Superconducting single-flux-quantum accelerator binarized neural network
ISSN号0018-9340
DOI10.1109/TC.2022.3215085
英文摘要As a high-performance application of low-temperature superconductivity, superconducting single-flux-quantum (SFQ) circuits have high speed and low-power consumption characteristics, which have recently received extensive attention, especially in the field of neural network inference accelerations. Despite these promising advantages, they are still limited by storage capacity and manufacture reliability, making them unfriendly for feedback loops and very large-scale circuits. The Binarized Neural Network (BNN), with minimal memory requirements and no reliance on multiplication, is undoubtedly an attractive candidate for implementing inference hardware using SFQ circuits. This work presents the first SFQ-based Binarized Neural Network inference accelerator, namely JBNN, with a new representation to binarize weights and activation variables. Every SFQ gate is essentially a pipeline stage, making conventional design methods of the accumulator unsuitable for SFQ circuits. So an SFQ-based accumulative parallel counter using SFQ logic cells including T1, OR, and AND is designed to realize the accumulation, where the data size is reduced to a quarter after passing the XNOR column and the AU layer, largely declining the hardware cost. Our evaluation shows that the proposed design outperforms a cryogenic CMOS-based BNN accelerator design running at 77K by 70.92 times while maintaining 97.89% accuracy on the MNIST benchmark dataset. Without the cooling cost, the power efficiency increases up to 929.18 times.
资助项目Hong Kong Jockey Club Charities Trust ; Chinese Academy of Sciences[XDA18000000] ; National Natural Science Foundation of China[61732018] ; National Natural Science Foundation of China[61872335] ; Youth Innovation Promotion Association CAS
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000886309300012
出版者IEEE COMPUTER SOC
源URL[http://119.78.100.204/handle/2XEOYT63/20314]  
专题中国科学院计算技术研究所期刊论文
通讯作者Fu, Rongliang
作者单位1.Chinese Acad Sci, Inst Comp Technol, SKLP, Beijing, Peoples R China
2.Chinese Univ Hong Kong, Dept Comp Sci & Engn, Hong Kong 999077, Peoples R China
推荐引用方式
GB/T 7714		 Fu, Rongliang,Huang, Junying,Wu, Haibin,et al. JBNN: A Hardware Design for Binarized Neural Networks Using Single-Flux-Quantum Circuits[J]. IEEE TRANSACTIONS ON COMPUTERS,2022,71(12):3203-3214.
APA Fu, Rongliang,Huang, Junying,Wu, Haibin,Ye, Xiaochun,Fan, Dongrui,&Ho, Tsung-Yi.(2022).JBNN: A Hardware Design for Binarized Neural Networks Using Single-Flux-Quantum Circuits.IEEE TRANSACTIONS ON COMPUTERS,71(12),3203-3214.
MLA Fu, Rongliang,et al."JBNN: A Hardware Design for Binarized Neural Networks Using Single-Flux-Quantum Circuits".IEEE TRANSACTIONS ON COMPUTERS 71.12(2022):3203-3214.

入库方式： OAI收割

来源：计算技术研究所

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