On-Chip Training Spiking Neural Networks Using Approximated Backpropagation With Analog Synaptic Devices

On-Chip Training Spiking Neural Networks Using Approximated Backpropagation With Analog Synaptic Devices

Hardware-based spiking neural networks (SNNs) inspired by a biological nervous system are regarded as an innovative computing system with very low power consumption and massively parallel operation. To train SNNs with supervision, we propose an efficient on-chip training scheme approximating backpro...

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Bibliographic Details
Main Authors: Dongseok Kwon, Suhwan Lim, Jong-Ho Bae, Sung-Tae Lee, Hyeongsu Kim, Young-Tak Seo, Seongbin Oh, Jangsaeng Kim, Kyuho Yeom, Byung-Gook Park, Jong-Ho Lee
Format: Article
Language:English
Published: Frontiers Media S.A. 2020-07-01
Series:Frontiers in Neuroscience
Subjects:
neuromorphic
spiking neural networks
deep neural networks
on-chip training
supervised learning
hardware-based neural networks
Online Access:https://www.frontiersin.org/article/10.3389/fnins.2020.00423/full

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https://www.frontiersin.org/article/10.3389/fnins.2020.00423/full

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