Attention Spiking Neural Networks

尖峰神经网络 计算机科学 人工智能 杠杆(统计) 块(置换群论) 模式识别(心理学) MNIST数据库 人工神经网络 机器学习 几何学 数学
作者
Man Yao,Guangshe Zhao,Hengyu Zhang,Yifan Hu,Lei Deng,Yonghong Tian,Bo Xu,Guoqi Li
出处
期刊:IEEE Transactions on Pattern Analysis and Machine Intelligence [IEEE Computer Society]
卷期号:45 (8): 9393-9410 被引量:65
标识
DOI:10.1109/tpami.2023.3241201
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. Compared with counterpart Res-ANN-104, the performance gap becomes -0.95/+0.21 percent and the energy efficiency is 31.8×/7.4×. 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 SNN research, with a great balance between effectiveness and energy efficiency.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
沈ff发布了新的文献求助10
刚刚
朴实的绣连完成签到,获得积分10
刚刚
2秒前
牧歌发布了新的文献求助30
2秒前
QQiang6发布了新的文献求助10
3秒前
4秒前
孟欣玥发布了新的文献求助10
4秒前
机灵的囧完成签到,获得积分10
5秒前
llll发布了新的文献求助10
5秒前
7秒前
8秒前
10秒前
TMX完成签到,获得积分20
10秒前
soldatJiang发布了新的文献求助10
11秒前
NL14D发布了新的文献求助10
11秒前
wq1020完成签到,获得积分10
11秒前
11秒前
科研通AI5应助孟欣玥采纳,获得20
12秒前
12秒前
Ava应助什么东西这么好看采纳,获得10
12秒前
超级丝发布了新的文献求助10
12秒前
TMX发布了新的文献求助10
15秒前
星星完成签到,获得积分10
15秒前
wenbinvan完成签到,获得积分0
15秒前
17秒前
科研通AI2S应助SAVP采纳,获得10
17秒前
Lycerdoctor发布了新的文献求助10
17秒前
李健应助wudan采纳,获得10
18秒前
18秒前
ANmin发布了新的文献求助10
18秒前
Inory007发布了新的文献求助10
19秒前
19秒前
桐桐应助冰棍采纳,获得10
19秒前
牧歌完成签到,获得积分0
19秒前
烟花应助怡然的一斩采纳,获得10
19秒前
20秒前
20秒前
SciGPT应助Nxxxxxx采纳,获得10
20秒前
21秒前
丘比特应助汪汪采纳,获得10
21秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3988838
求助须知:如何正确求助?哪些是违规求助? 3531250
关于积分的说明 11252914
捐赠科研通 3269838
什么是DOI,文献DOI怎么找? 1804820
邀请新用户注册赠送积分活动 881943
科研通“疑难数据库(出版商)”最低求助积分说明 809028