Comprehensive SNN Compression Using ADMM Optimization and Activity Regularization

doi:10.1109/TNNLS.2021.3109064

	Comprehensive SNN Compression Using ADMM Optimization and Activity Regularization
	Deng, Lei 1; Wu, Yujie 1; Hu, Yifan 1; Liang, Ling 2; Li, Guoqi 1; Hu, Xing 3; Ding, Yufei 4; Li, Peng 2; Xie, Yuan 2
	2021-10-29
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN	2162-237X
页码	15
摘要	As well known, the huge memory and compute costs of both artificial neural networks (ANNs) and spiking neural networks (SNNs) greatly hinder their deployment on edge devices with high efficiency. Model compression has been proposed as a promising technique to improve the running efficiency via parameter and operation reduction, whereas this technique is mainly practiced in ANNs rather than SNNs. It is interesting to answer how much an SNN model can be compressed without compromising its functionality, where two challenges should be addressed: 1) the accuracy of SNNs is usually sensitive to model compression, which requires an accurate compression methodology and 2) the computation of SNNs is event-driven rather than static, which produces an extra compression dimension on dynamic spikes. To this end, we realize a comprehensive SNN compression through three steps. First, we formulate the connection pruning and weight quantization as a constrained optimization problem. Second, we combine spatiotemporal backpropagation (STBP) and alternating direction method of multipliers (ADMMs) to solve the problem with minimum accuracy loss. Third, we further propose activity regularization to reduce the spike events for fewer active operations. These methods can be applied in either a single way for moderate compression or a joint way for aggressive compression. We define several quantitative metrics to evaluate the compression performance for SNNs. Our methodology is validated in pattern recognition tasks over MNIST, N-MNIST, CIFAR10, and CIFAR100 datasets, where extensive comparisons, analyses, and insights are provided. To the best of our knowledge, this is the first work that studies SNN compression in a comprehensive manner by exploiting all compressible components and achieves better results.
关键词	Neurons Computational modeling Quantization (signal) Optimization Encoding Task analysis Synapses Activity regularization alternating direction method of multiplier (ADMM) connection pruning spiking neural network (SNN) compression weight quantization
DOI	10.1109/TNNLS.2021.3109064
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2018AAA0102600] ; National Key Research and Development Program of China[2018YEF0200200] ; National Natural Science Foundation of China[61876215] ; Beijing Academy of Artificial Intelligence (BAAI) ; Science and Technology Major Project of Guangzhou[202007030006] ; Open Project of Zhejiang Laboratory ; Institute for Guo Qiang of Tsinghua University
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:000733540300001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：18[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/17920
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Li, Guoqi; Hu, Xing
作者单位	1.Tsinghua Univ, Dept Precis Instrument, Ctr Brain Inspired Comp Res, Beijing 100084, Peoples R China 2.Univ Calif Santa Barbara, Dept Elect & Comp Engn, Santa Barbara, CA 93106 USA 3.Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Beijing 100190, Peoples R China 4.Univ Calif Santa Barbara, Dept Comp Sci, Santa Barbara, CA 93106 USA
推荐引用方式 GB/T 7714	Deng, Lei,Wu, Yujie,Hu, Yifan,et al. Comprehensive SNN Compression Using ADMM Optimization and Activity Regularization[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2021:15.
APA	Deng, Lei.,Wu, Yujie.,Hu, Yifan.,Liang, Ling.,Li, Guoqi.,...&Xie, Yuan.(2021).Comprehensive SNN Compression Using ADMM Optimization and Activity Regularization.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,15.
MLA	Deng, Lei,et al."Comprehensive SNN Compression Using ADMM Optimization and Activity Regularization".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2021):15.