Hardware-based spiking neural network architecture using simplified backpropagation algorithm and homeostasis functionality

Bio-inspired hardware-based spiking neural networks (SNNs) has been suggested as a promising computing system with low power consumption and parallel operation. We propose the supervised on-chip training method approximating the backpropagation algorithm and the pulse scheme applicable to the hardwa...

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Bibliographic Details

Published in:	Neurocomputing (Amsterdam) Vol. 428; pp. 153 - 165
Main Authors:	Kim, Jangsaeng, Kwon, Dongseok, Woo, Sung Yun, Kang, Won-Mook, Lee, Soochang, Oh, Seongbin, Kim, Chul-Heung, Bae, Jong-Ho, Park, Byung-Gook, Lee, Jong-Ho
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 07.03.2021
Subjects:	Hardware-based neural networks Homeostasis On-chip training Spiking Neural Networks (SNNs) Supervised learning Synaptic devices Homeostasis Hardware-based neural networks On-chip training Spiking Neural Networks (SNNs) Synaptic devices Supervised learning
ISSN:	0925-2312, 1872-8286
Online Access:	Get full text
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