Signal and noise extraction from analog memory elements for neuromorphic computing

The application of resistive and phase-change memories in neuromorphic computation will require efficient methods to quantify device-to-device and switching variability. Here, the authors assess the impact of a broad range of device switching mechanisms using machine learning regression techniques.

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Autores principales:	N. Gong, T. Idé, S. Kim, I. Boybat, A. Sebastian, V. Narayanan, T. Ando
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/a1f3b1e3f21e4dcd812626edb73f3983
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id	oai:doaj.org-article:a1f3b1e3f21e4dcd812626edb73f3983
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spelling	oai:doaj.org-article:a1f3b1e3f21e4dcd812626edb73f39832021-12-02T15:34:22ZSignal and noise extraction from analog memory elements for neuromorphic computing10.1038/s41467-018-04485-12041-1723https://doaj.org/article/a1f3b1e3f21e4dcd812626edb73f39832018-05-01T00:00:00Zhttps://doi.org/10.1038/s41467-018-04485-1https://doaj.org/toc/2041-1723The application of resistive and phase-change memories in neuromorphic computation will require efficient methods to quantify device-to-device and switching variability. Here, the authors assess the impact of a broad range of device switching mechanisms using machine learning regression techniques.N. GongT. IdéS. KimI. BoybatA. SebastianV. NarayananT. AndoNature PortfolioarticleScienceQENNature Communications, Vol 9, Iss 1, Pp 1-8 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q N. Gong T. Idé S. Kim I. Boybat A. Sebastian V. Narayanan T. Ando Signal and noise extraction from analog memory elements for neuromorphic computing
description	The application of resistive and phase-change memories in neuromorphic computation will require efficient methods to quantify device-to-device and switching variability. Here, the authors assess the impact of a broad range of device switching mechanisms using machine learning regression techniques.
format	article
author	N. Gong T. Idé S. Kim I. Boybat A. Sebastian V. Narayanan T. Ando
author_facet	N. Gong T. Idé S. Kim I. Boybat A. Sebastian V. Narayanan T. Ando
author_sort	N. Gong
title	Signal and noise extraction from analog memory elements for neuromorphic computing
title_short	Signal and noise extraction from analog memory elements for neuromorphic computing
title_full	Signal and noise extraction from analog memory elements for neuromorphic computing
title_fullStr	Signal and noise extraction from analog memory elements for neuromorphic computing
title_full_unstemmed	Signal and noise extraction from analog memory elements for neuromorphic computing
title_sort	signal and noise extraction from analog memory elements for neuromorphic computing
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/a1f3b1e3f21e4dcd812626edb73f3983
work_keys_str_mv	AT ngong signalandnoiseextractionfromanalogmemoryelementsforneuromorphiccomputing AT tide signalandnoiseextractionfromanalogmemoryelementsforneuromorphiccomputing AT skim signalandnoiseextractionfromanalogmemoryelementsforneuromorphiccomputing AT iboybat signalandnoiseextractionfromanalogmemoryelementsforneuromorphiccomputing AT asebastian signalandnoiseextractionfromanalogmemoryelementsforneuromorphiccomputing AT vnarayanan signalandnoiseextractionfromanalogmemoryelementsforneuromorphiccomputing AT tando signalandnoiseextractionfromanalogmemoryelementsforneuromorphiccomputing
_version_	1718386853741068288

Signal and noise extraction from analog memory elements for neuromorphic computing

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