Accurate deep neural network inference using computational phase-change memory

Designing deep learning inference hardware based on in-memory computing remains a challenge. Here, the authors propose a strategy to train ResNet-type convolutional neural networks which results in reduced accuracy loss when transferring weights to in-memory computing hardware based on phase-change...

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Autores principales:	Vinay Joshi, Manuel Le Gallo, Simon Haefeli, Irem Boybat, S. R. Nandakumar, Christophe Piveteau, Martino Dazzi, Bipin Rajendran, Abu Sebastian, Evangelos Eleftheriou
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/9493f30e46f1467787cb2f92f01d219f
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spelling	oai:doaj.org-article:9493f30e46f1467787cb2f92f01d219f2021-12-02T16:51:35ZAccurate deep neural network inference using computational phase-change memory10.1038/s41467-020-16108-92041-1723https://doaj.org/article/9493f30e46f1467787cb2f92f01d219f2020-05-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-16108-9https://doaj.org/toc/2041-1723Designing deep learning inference hardware based on in-memory computing remains a challenge. Here, the authors propose a strategy to train ResNet-type convolutional neural networks which results in reduced accuracy loss when transferring weights to in-memory computing hardware based on phase-change memory.Vinay JoshiManuel Le GalloSimon HaefeliIrem BoybatS. R. NandakumarChristophe PiveteauMartino DazziBipin RajendranAbu SebastianEvangelos EleftheriouNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-13 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Vinay Joshi Manuel Le Gallo Simon Haefeli Irem Boybat S. R. Nandakumar Christophe Piveteau Martino Dazzi Bipin Rajendran Abu Sebastian Evangelos Eleftheriou Accurate deep neural network inference using computational phase-change memory
description	Designing deep learning inference hardware based on in-memory computing remains a challenge. Here, the authors propose a strategy to train ResNet-type convolutional neural networks which results in reduced accuracy loss when transferring weights to in-memory computing hardware based on phase-change memory.
format	article
author	Vinay Joshi Manuel Le Gallo Simon Haefeli Irem Boybat S. R. Nandakumar Christophe Piveteau Martino Dazzi Bipin Rajendran Abu Sebastian Evangelos Eleftheriou
author_facet	Vinay Joshi Manuel Le Gallo Simon Haefeli Irem Boybat S. R. Nandakumar Christophe Piveteau Martino Dazzi Bipin Rajendran Abu Sebastian Evangelos Eleftheriou
author_sort	Vinay Joshi
title	Accurate deep neural network inference using computational phase-change memory
title_short	Accurate deep neural network inference using computational phase-change memory
title_full	Accurate deep neural network inference using computational phase-change memory
title_fullStr	Accurate deep neural network inference using computational phase-change memory
title_full_unstemmed	Accurate deep neural network inference using computational phase-change memory
title_sort	accurate deep neural network inference using computational phase-change memory
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/9493f30e46f1467787cb2f92f01d219f
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Accurate deep neural network inference using computational phase-change memory

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