BSNN: Towards faster and better conversion of artificial neural networks to spiking neural networks with bistable neurons
文献类型：期刊论文
| 作者 | Li, Yang1,4 ; Zhao, Dongcheng4; Zeng, Yi1,2,3,4
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| 刊名 | FRONTIERS IN NEUROSCIENCE
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| 出版日期 | 2022-10-12 |
| 卷号 | 16页码:13 |
| 关键词 | spiking neural network bistability neuromorphic computing image classification conversion |
| DOI | 10.3389/fnins.2022.991851 |
| 通讯作者 | Zeng, Yi(yi.zeng@ia.ac.cn) |
| 英文摘要 | The spiking neural network (SNN) computes and communicates information through discrete binary events. Recent work has achieved essential progress on an excellent performance by converting ANN to SNN. Due to the difference in information processing, the converted deep SNN usually suffers serious performance loss and large time delay. In this paper, we analyze the reasons for the performance loss and propose a novel bistable spiking neural network (BSNN) that addresses the problem of the phase lead and phase lag. Also, we design synchronous neurons (SN) to help efficiently improve performance when ResNet structure-based ANNs are converted. BSNN significantly improves the performance of the converted SNN by enabling more accurate delivery of information to the next layer after one cycle. Experimental results show that the proposed method only needs 1/4-1/10 of the time steps compared to previous work to achieve nearly lossless conversion. We demonstrate better ANN-SNN conversion for VGG16, ResNet20, and ResNet34 on challenging datasets including CIFAR-10 (95.16% top-1), CIFAR-100 (78.12% top-1), and ImageNet (72.64% top-1). |
| 资助项目 | National Key Research and Development Program ; Strategic Priority Research Program of the Chinese Academy of Sciences ; [2020AAA0107800] ; [XDB32070100] |
| WOS研究方向 | Neurosciences & Neurology |
| 语种 | 英语 |
| WOS记录号 | WOS:000876692100001 |
| 出版者 | FRONTIERS MEDIA SA |
| 资助机构 | National Key Research and Development Program ; Strategic Priority Research Program of the Chinese Academy of Sciences |
| 源URL | [http://ir.ia.ac.cn/handle/173211/50517]  |
| 专题 | 类脑智能研究中心_类脑认知计算 |
| 通讯作者 | Zeng, Yi |
| 作者单位 | 1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China 2.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Shanghai, Peoples R China 3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China 4.Chinese Acad Sci, Inst Automat, Res Ctr Brain Inspired Intelligence, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Yang,Zhao, Dongcheng,Zeng, Yi. BSNN: Towards faster and better conversion of artificial neural networks to spiking neural networks with bistable neurons[J]. FRONTIERS IN NEUROSCIENCE,2022,16:13. |
| APA | Li, Yang,Zhao, Dongcheng,&Zeng, Yi.(2022).BSNN: Towards faster and better conversion of artificial neural networks to spiking neural networks with bistable neurons.FRONTIERS IN NEUROSCIENCE,16,13. |
| MLA | Li, Yang,et al."BSNN: Towards faster and better conversion of artificial neural networks to spiking neural networks with bistable neurons".FRONTIERS IN NEUROSCIENCE 16(2022):13. |
入库方式： OAI收割
来源：自动化研究所
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