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Extremely Sparse Networks via Binary Augmented Pruning for Fast Image Classification

文献类型：期刊论文

作者Wang, Peisong2; Li, Fanrong3; Li, Gang1; Cheng, Jian2
刊名IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
出版日期2021-11-08
页码14
关键词Hardware acceleration image classification neural networks pruning software-hardware codesign
ISSN号2162-237X
DOI10.1109/TNNLS.2021.3120409
通讯作者Cheng, Jian(jcheng@nlpr.ia.ac.cn)
英文摘要Network pruning and binarization have been demonstrated to be effective in neural network accelerator design for high speed and energy efficiency. However, most existing pruning approaches achieve a poor tradeoff between accuracy and efficiency, which on the other hand, has limited the progress of neural network accelerators. At the same time, binary networks are highly efficient, however, a large accuracy gap exists between binary networks and their full-precision counterparts. In this article, we investigate the merits of extremely sparse networks with binary connections for image classification through software-hardware codesign. More specifically, we first propose a binary augmented extremely pruning method that can achieve similar to 98% sparsity with small accuracy degradation. Then we design the hardware architecture based on the resulting sparse and binary networks, which extensively explores the benefits of extreme sparsity with negligible resource consumption introduced by binary branch. Experiments on large-scale ImageNet classification and field-programmable gate array (FPGA) demonstrate that the proposed software-hardware architecture can achieve a prominent tradeoff between accuracy and efficiency.
资助项目National Natural Science Foundation of China[61906193] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDA27040300] ; National Key Research and Development Program of China[2020AAA0103402]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000732358700001
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Natural Science Foundation of China ; Strategic Priority Research Program of Chinese Academy of Sciences ; National Key Research and Development Program of China
源URL[http://ir.ia.ac.cn/handle/173211/46977]  
专题类脑芯片与系统研究
通讯作者Cheng, Jian
作者单位1.Shanghai Jiao Tong Univ, Adv Comp Architecture Lab, Dept Comp Sci & Engn, Shanghai 200240, Peoples R China
2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
3.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Peisong,Li, Fanrong,Li, Gang,et al. Extremely Sparse Networks via Binary Augmented Pruning for Fast Image Classification[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2021:14.
APA Wang, Peisong,Li, Fanrong,Li, Gang,&Cheng, Jian.(2021).Extremely Sparse Networks via Binary Augmented Pruning for Fast Image Classification.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,14.
MLA Wang, Peisong,et al."Extremely Sparse Networks via Binary Augmented Pruning for Fast Image Classification".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2021):14.

入库方式： OAI收割

来源：自动化研究所

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