Rethinking Pretraining as a Bridge From ANNs to SNNs

doi:10.1109/TNNLS.2022.3217796

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 模式分析与学习

	Rethinking Pretraining as a Bridge From ANNs to SNNs
	Lin, Yihan 1; Hu, Yifan 1; Ma, Shijie2,3 ; Yu, Dongjie 4; Li, Guoqi5,6,7
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN	2162-237X
	2022-11-14
页码	14
通讯作者	Li, Guoqi(guoqi.li@ia.ac.cn)
摘要	Spiking neural networks (SNNs) are known as typical kinds of brain-inspired models with their unique features of rich neuronal dynamics, diverse coding schemes, and low power consumption properties. How to obtain a high-accuracy model has always been the main challenge in the field of SNN. Currently, there are two mainstream methods, i.e., obtaining a converted SNN through converting a well-trained artificial NN (ANN) to its SNN counterpart or training an SNN directly. However, the inference time of a converted SNN is too long, while SNN training is generally very costly and inefficient. In this work, a new SNN training paradigm is proposed by combining the concepts of the two different training methods with the help of the pretrain technique and BP-based deep SNN training mechanism. We believe that the proposed paradigm is a more efficient pipeline for training SNNs. The pipeline includes pipe-S for static data transfer tasks and pipe-D for dynamic data transfer tasks. State-of-the-art (SOTA) results are obtained in a large-scale event-driven dataset ES-ImageNet. For training acceleration, we achieve the same (or higher) best accuracy as similar leaky-integrate-and-fire (LIF)-SNNs using 1/8 training time on ImageNet-1K and 1/2 training time on ES-ImageNet and also provide a time-accuracy benchmark for a new dataset ES-UCF101. These experimental results reveal the similarity of the functions of parameters between ANNs and SNNs and also demonstrate various potential applications of this SNN training pipeline.
关键词	Training Task analysis Neurons Pipelines Artificial neural networks Feature extraction Transfer learning Event-driven dataset neural network (NN) analysis pretraining technique spiking NN (SNN) transfer learning
DOI	10.1109/TNNLS.2022.3217796
关键词[WOS]	INTELLIGENCE ; NETWORKS
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61836004] ; National Natural Science Foundation of China[62236009] ; National Natural Science Foundation of China[U22A20103] ; Beijing Natural Science Foundation[JQ21015] ; National Key Research and Development Program of China[2018AAA0102600] ; Beijing Academy of Artificial Intelligence
项目资助者	National Natural Science Foundation of China ; Beijing Natural Science Foundation ; National Key Research and Development Program of China ; Beijing Academy of Artificial Intelligence
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:000888970400001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：4[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/173211/51285
专题	多模态人工智能系统全国重点实验室_模式分析与学习
通讯作者	Li, Guoqi
作者单位	1.Tsinghua Univ, Dept Precis Instrument, Beijing 100084, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 4.Tsinghua Univ, Sch Vehicle & Mobil, Beijing 100084, Peoples R China 5.Chinese Acad Sci, Inst Automat, Beijing 100045, Peoples R China 6.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 101408, Peoples R China 7.Peng Cheng Lab, Shenzhen 518055, 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	Lin, Yihan,Hu, Yifan,Ma, Shijie,et al. Rethinking Pretraining as a Bridge From ANNs to SNNs[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2022:14.
APA	Lin, Yihan,Hu, Yifan,Ma, Shijie,Yu, Dongjie,&Li, Guoqi.(2022).Rethinking Pretraining as a Bridge From ANNs to SNNs.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,14.
MLA	Lin, Yihan,et al."Rethinking Pretraining as a Bridge From ANNs to SNNs".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2022):14.