DSNNs:learning transfer from deep neural networks to spiking neural networks

张磊; Du Zidong; Li Ling; Chen Yunji

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DSNNs:learning transfer from deep neural networks to spiking neural networks

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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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《高技术通讯：英文版》 |2020年第2期|136-144|共9页
作者
张磊; Du Zidong; Li Ling; Chen Yunji;
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作者单位

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;

Institute of Software;

Chinese Academy of Sciences;

Beijing 100190;

P.R.China;

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原文格式 PDF
正文语种 chi
中图分类 TN9;
关键词
deep; leaning; spiking; neural; network(SNN); convert; method; spatially; folded; network;

DSNNs:learning transfer from deep neural networks to spiking neural networks

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