Attention Spiking Neural Networks

doi:10.1109/TPAMI.2023.3241201

	Attention Spiking Neural Networks
	Yao, Man 1,2; Zhao, Guangshe 1; Zhang, Hengyu 3; Hu, Yifan 4; Deng, Lei 4; Tian, Yonghong 2,5; Xu, Bo6 ; Li, Guoqi6
发表期刊	IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
ISSN	0162-8828
	2023-08-01
卷号	45 期号:8 页码:9393-9410
通讯作者	Li, Guoqi(guoqi.li@ia.ac.cn)
摘要	Brain-inspired spiking neural networks (SNNs) are becoming a promising energy-efficient alternative to traditional artificial neural networks (ANNs). However, the performance gap between SNNs and ANNs has been a significant hindrance to deploying SNNs ubiquitously. To leverage the full potential of SNNs, in this paper we study the attention mechanisms, which can help human focus on important information. We present our idea of attention in SNNs with a multi-dimensional attention module, which infers attention weights along the temporal, channel, as well as spatial dimension separately or simultaneously. Based on the existing neuroscience theories, we exploit the attention weights to optimize membrane potentials, which in turn regulate the spiking response. Extensive experimental results on event-based action recognition and image classification datasets demonstrate that attention facilitates vanilla SNNs to achieve sparser spiking firing, better performance, and energy efficiency concurrently. In particular, we achieve top-1 accuracy of 75.92% and 77.08% on ImageNet-1 K with single/4-step Res-SNN-104, which are state-of-the-art results in SNNs. Comparedwith counterpart Res-ANN-104, the performance gap becomes -0.95/+0.21 percent and the energy efficiency is 31.8x/7.4x. To analyze the effectiveness of attention SNNs, we theoretically prove that the spiking degradation or the gradient vanishing, which usually holds in general SNNs, can be resolved by introducing the block dynamical isometry theory. We also analyze the efficiency of attention SNNs based on our proposed spiking response visualization method. Our work lights up SNN's potential as a general backbone to support various applications in the field of SNNresearch, with a great balance between effectiveness and energy efficiency.
关键词	Attention mechanism efficient neuromorphic inference neuromorphic computing spiking neural network
DOI	10.1109/TPAMI.2023.3241201
关键词[WOS]	CLASSIFICATION ; COMMUNICATION ; INTELLIGENCE ; MECHANISMS
收录类别	SCI
语种	英语
资助项目	Beijing Natural Science Foundation for Distinguished Young Scholars[JQ21015] ; National Key R&D Program of China[2018AAA0102600] ; National Natural Science Foundation of China[62236009] ; National Natural Science Foundation of China[61836004] ; National Natural Science Foundation of China[U22A20103]
项目资助者	Beijing Natural Science Foundation for Distinguished Young Scholars ; National Key R&D Program of China ; National Natural Science Foundation of China
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:001022958600008
出版者	IEEE COMPUTER SOC
引用统计	被引频次：22[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/53886
专题	复杂系统认知与决策实验室脑图谱与类脑智能实验室
通讯作者	Li, Guoqi
作者单位	1.Xi An Jiao Tong Univ, Sch Automat Sci & Engn, Xian 710049, Shaanxi, Peoples R China 2.Peng Cheng Lab, Shenzhen 518066, Guangdong, Peoples R China 3.Tsinghua Univ, Tsinghua Shenzhen Int Grad Sch, Shenzhen 518071, Guangdong, Peoples R China 4.Tsinghua Univ, Dept Precis Instrument, Ctr Brain Inspired Comp Res, Beijing 100190, Peoples R China 5.Peking Univ, Inst Artificial Intelligence, Beijing 100871, Peoples R China 6.Chinese Acad Sci, Inst Automat, Beijing 100045, Peoples R China
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Yao, Man,Zhao, Guangshe,Zhang, Hengyu,et al. Attention Spiking Neural Networks[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2023,45(8):9393-9410.
APA	Yao, Man.,Zhao, Guangshe.,Zhang, Hengyu.,Hu, Yifan.,Deng, Lei.,...&Li, Guoqi.(2023).Attention Spiking Neural Networks.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,45(8),9393-9410.
MLA	Yao, Man,et al."Attention Spiking Neural Networks".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 45.8(2023):9393-9410.