Committee machines—a universal method to deal with non-idealities in memristor-based neural networks

Committee machines—a universal method to deal with non-idealities in memristor-based neural networks

Designing reliable and energy-efficient memristor-based artificial neural networks remains a challenge. Here, the authors demonstrate a technology-agnostic approach, committee machines, which increases the inference accuracy of memristive neural networks that suffer from device variability, faulty d...

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Autores principales: D. Joksas, P. Freitas, Z. Chai, W. H. Ng, M. Buckwell, C. Li, W. D. Zhang, Q. Xia, A. J. Kenyon, A. Mehonic
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2020
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Acceso en línea:https://doaj.org/article/1568419195894594b620bd5c234eada1
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spelling oai:doaj.org-article:1568419195894594b620bd5c234eada12021-12-02T15:09:10ZCommittee machines—a universal method to deal with non-idealities in memristor-based neural networks10.1038/s41467-020-18098-02041-1723https://doaj.org/article/1568419195894594b620bd5c234eada12020-08-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-18098-0https://doaj.org/toc/2041-1723Designing reliable and energy-efficient memristor-based artificial neural networks remains a challenge. Here, the authors demonstrate a technology-agnostic approach, committee machines, which increases the inference accuracy of memristive neural networks that suffer from device variability, faulty devices, random telegraph noise and line resistance.D. JoksasP. FreitasZ. ChaiW. H. NgM. BuckwellC. LiW. D. ZhangQ. XiaA. J. KenyonA. MehonicNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-10 (2020)
institution DOAJ
collection DOAJ
language EN
topic Science
Q
spellingShingle Science
Q
D. Joksas
P. Freitas
Z. Chai
W. H. Ng
M. Buckwell
C. Li
W. D. Zhang
Q. Xia
A. J. Kenyon
A. Mehonic
Committee machines—a universal method to deal with non-idealities in memristor-based neural networks
description Designing reliable and energy-efficient memristor-based artificial neural networks remains a challenge. Here, the authors demonstrate a technology-agnostic approach, committee machines, which increases the inference accuracy of memristive neural networks that suffer from device variability, faulty devices, random telegraph noise and line resistance.
format article
author D. Joksas
P. Freitas
Z. Chai
W. H. Ng
M. Buckwell
C. Li
W. D. Zhang
Q. Xia
A. J. Kenyon
A. Mehonic
author_facet D. Joksas
P. Freitas
Z. Chai
W. H. Ng
M. Buckwell
C. Li
W. D. Zhang
Q. Xia
A. J. Kenyon
A. Mehonic
author_sort D. Joksas
title Committee machines—a universal method to deal with non-idealities in memristor-based neural networks
title_short Committee machines—a universal method to deal with non-idealities in memristor-based neural networks
title_full Committee machines—a universal method to deal with non-idealities in memristor-based neural networks
title_fullStr Committee machines—a universal method to deal with non-idealities in memristor-based neural networks
title_full_unstemmed Committee machines—a universal method to deal with non-idealities in memristor-based neural networks
title_sort committee machines—a universal method to deal with non-idealities in memristor-based neural networks
publisher Nature Portfolio
publishDate 2020
url https://doaj.org/article/1568419195894594b620bd5c234eada1
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