Spontaneous sparse learning for PCM-based memristor neural networks

Designing energy efficient and scalable artificial networks for neuromorphic computing remains a challenge. Here, the authors present 1 Gb phase change memory memristor array with a spontaneous sparse learning scheme able to leverage the resistance drift issue improving the classification accuracy o...

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Main Authors:	Dong-Hyeok Lim, Shuang Wu, Rong Zhao, Jung-Hoon Lee, Hongsik Jeong, Luping Shi
Format:	Article
Language:	English
Published:	Nature Publishing Group 2021-01-01
Series:	Nature Communications
Online Access:	https://doi.org/10.1038/s41467-020-20519-z

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spelling	doaj-ececb419570943f191ce1816c577a3512021-01-17T12:12:12ZengNature Publishing GroupNature Communications2041-17232021-01-0112111410.1038/s41467-020-20519-zSpontaneous sparse learning for PCM-based memristor neural networksDong-Hyeok Lim0Shuang Wu1Rong Zhao2Jung-Hoon Lee3Hongsik Jeong4Luping Shi5Department of Precision Instrument, Center for Brain Inspired Computing Research, Tsinghua UniversityDepartment of Precision Instrument, Center for Brain Inspired Computing Research, Tsinghua UniversityDepartment of Precision Instrument, Center for Brain Inspired Computing Research, Tsinghua UniversityDepartment of Electronic Engineering, Center for Brain Inspired Computing Research, Tsinghua UniversityDepartment of Electronic Engineering, Center for Brain Inspired Computing Research, Tsinghua UniversityDepartment of Precision Instrument, Center for Brain Inspired Computing Research, Tsinghua UniversityDesigning energy efficient and scalable artificial networks for neuromorphic computing remains a challenge. Here, the authors present 1 Gb phase change memory memristor array with a spontaneous sparse learning scheme able to leverage the resistance drift issue improving the classification accuracy on MNIST handwritten digit dataset.https://doi.org/10.1038/s41467-020-20519-z
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Dong-Hyeok Lim Shuang Wu Rong Zhao Jung-Hoon Lee Hongsik Jeong Luping Shi
spellingShingle	Dong-Hyeok Lim Shuang Wu Rong Zhao Jung-Hoon Lee Hongsik Jeong Luping Shi Spontaneous sparse learning for PCM-based memristor neural networks Nature Communications
author_facet	Dong-Hyeok Lim Shuang Wu Rong Zhao Jung-Hoon Lee Hongsik Jeong Luping Shi
author_sort	Dong-Hyeok Lim
title	Spontaneous sparse learning for PCM-based memristor neural networks
title_short	Spontaneous sparse learning for PCM-based memristor neural networks
title_full	Spontaneous sparse learning for PCM-based memristor neural networks
title_fullStr	Spontaneous sparse learning for PCM-based memristor neural networks
title_full_unstemmed	Spontaneous sparse learning for PCM-based memristor neural networks
title_sort	spontaneous sparse learning for pcm-based memristor neural networks
publisher	Nature Publishing Group
series	Nature Communications
issn	2041-1723
publishDate	2021-01-01
description	Designing energy efficient and scalable artificial networks for neuromorphic computing remains a challenge. Here, the authors present 1 Gb phase change memory memristor array with a spontaneous sparse learning scheme able to leverage the resistance drift issue improving the classification accuracy on MNIST handwritten digit dataset.
url	https://doi.org/10.1038/s41467-020-20519-z
work_keys_str_mv	AT donghyeoklim spontaneoussparselearningforpcmbasedmemristorneuralnetworks AT shuangwu spontaneoussparselearningforpcmbasedmemristorneuralnetworks AT rongzhao spontaneoussparselearningforpcmbasedmemristorneuralnetworks AT junghoonlee spontaneoussparselearningforpcmbasedmemristorneuralnetworks AT hongsikjeong spontaneoussparselearningforpcmbasedmemristorneuralnetworks AT lupingshi spontaneoussparselearningforpcmbasedmemristorneuralnetworks
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Spontaneous sparse learning for PCM-based memristor neural networks

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