Temporal correlation detection using computational phase-change memory

New computing paradigms, such as in-memory computing, are expected to overcome the limitations of conventional computing approaches. Sebastian et al. report a large-scale demonstration of computational phase change memory (PCM) by performing high-level computational primitives using one million PCM...

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Autores principales:	Abu Sebastian, Tomas Tuma, Nikolaos Papandreou, Manuel Le Gallo, Lukas Kull, Thomas Parnell, Evangelos Eleftheriou
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/b78bba0b57c14ed192f98db76b3dc146
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id	oai:doaj.org-article:b78bba0b57c14ed192f98db76b3dc146
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spelling	oai:doaj.org-article:b78bba0b57c14ed192f98db76b3dc1462021-12-02T17:06:19ZTemporal correlation detection using computational phase-change memory10.1038/s41467-017-01481-92041-1723https://doaj.org/article/b78bba0b57c14ed192f98db76b3dc1462017-10-01T00:00:00Zhttps://doi.org/10.1038/s41467-017-01481-9https://doaj.org/toc/2041-1723New computing paradigms, such as in-memory computing, are expected to overcome the limitations of conventional computing approaches. Sebastian et al. report a large-scale demonstration of computational phase change memory (PCM) by performing high-level computational primitives using one million PCM devices.Abu SebastianTomas TumaNikolaos PapandreouManuel Le GalloLukas KullThomas ParnellEvangelos EleftheriouNature PortfolioarticleScienceQENNature Communications, Vol 8, Iss 1, Pp 1-10 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Abu Sebastian Tomas Tuma Nikolaos Papandreou Manuel Le Gallo Lukas Kull Thomas Parnell Evangelos Eleftheriou Temporal correlation detection using computational phase-change memory
description	New computing paradigms, such as in-memory computing, are expected to overcome the limitations of conventional computing approaches. Sebastian et al. report a large-scale demonstration of computational phase change memory (PCM) by performing high-level computational primitives using one million PCM devices.
format	article
author	Abu Sebastian Tomas Tuma Nikolaos Papandreou Manuel Le Gallo Lukas Kull Thomas Parnell Evangelos Eleftheriou
author_facet	Abu Sebastian Tomas Tuma Nikolaos Papandreou Manuel Le Gallo Lukas Kull Thomas Parnell Evangelos Eleftheriou
author_sort	Abu Sebastian
title	Temporal correlation detection using computational phase-change memory
title_short	Temporal correlation detection using computational phase-change memory
title_full	Temporal correlation detection using computational phase-change memory
title_fullStr	Temporal correlation detection using computational phase-change memory
title_full_unstemmed	Temporal correlation detection using computational phase-change memory
title_sort	temporal correlation detection using computational phase-change memory
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/b78bba0b57c14ed192f98db76b3dc146
work_keys_str_mv	AT abusebastian temporalcorrelationdetectionusingcomputationalphasechangememory AT tomastuma temporalcorrelationdetectionusingcomputationalphasechangememory AT nikolaospapandreou temporalcorrelationdetectionusingcomputationalphasechangememory AT manuellegallo temporalcorrelationdetectionusingcomputationalphasechangememory AT lukaskull temporalcorrelationdetectionusingcomputationalphasechangememory AT thomasparnell temporalcorrelationdetectionusingcomputationalphasechangememory AT evangeloseleftheriou temporalcorrelationdetectionusingcomputationalphasechangememory
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Temporal correlation detection using computational phase-change memory

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