| Zhang Lei(张磊)* ** ***,Du Zidong* ***,Li Ling****,Chen Yunji* **.[J].高技术通讯(英文),2020,26(2):136~144 |
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| DSNNs: learning transfer from deep neural networks to spiking neural networks |
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| DOI：doi：10.3772/j.issn.1006-6748.2020.02.002 |
| 中文关键词: |
| 英文关键词: deep leaning, spiking neural network (SNN), convert method, spatially folded network |
| 基金项目: |
| Author Name | Affiliation | | Zhang Lei(张磊)* ** *** | (*State Key Laboratory of Computer Architecture, Institute of Computing Technology,Chinese Academy of Sciences, Beijing 100190, P.R.China)
(**Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, P.R.China)
(***Cambricon Tech. Ltd, Beijing 100010, P.R.China) | | Du Zidong* *** | (*State Key Laboratory of Computer Architecture, Institute of Computing Technology,Chinese Academy of Sciences, Beijing 100190, P.R.China)
(***Cambricon Tech. Ltd, Beijing 100010, P.R.China) | | Li Ling**** | (****Institute of Software, Chinese Academy of Sciences, Beijing 100190, P.R.China) | | Chen Yunji* ** | (*State Key Laboratory of Computer Architecture, Institute of Computing Technology,Chinese Academy of Sciences, Beijing 100190, P.R.China)
(**Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, P.R.China) |
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| 中文摘要: |
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| 英文摘要: |
| Deep neural networks (DNNs) have drawn great attention as they perform the state-of-the-art results on many tasks. Compared to DNNs, spiking neural networks (SNNs), which are considered as the new generation of neural networks, fail to achieve comparable performance especially on tasks with large problem sizes. Many previous work tried to close the gap between DNNs and SNNs but used small networks on simple tasks. This work proposes a simple but effective way to construct deep spiking neural networks (DSNNs) by transferring the learned ability of DNNs to SNNs. DSNNs achieve comparable accuracy on large networks and complex datasets. |
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