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Signal and noise extraction from analog memory elements for neuromorphic computing

Dense crossbar arrays of non-volatile memory (NVM) can potentially enable massively parallel and highly energy-efficient neuromorphic computing systems. The key requirements for the NVM elements are continuous (analog-like) conductance tuning capability and switching symmetry with acceptable noise l...

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Autores principales: Gong, N., Idé, T., Kim, S., Boybat, I., Sebastian, A., Narayanan, V., Ando, T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5974407/
https://www.ncbi.nlm.nih.gov/pubmed/29844421
http://dx.doi.org/10.1038/s41467-018-04485-1
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author Gong, N.
Idé, T.
Kim, S.
Boybat, I.
Sebastian, A.
Narayanan, V.
Ando, T.
author_facet Gong, N.
Idé, T.
Kim, S.
Boybat, I.
Sebastian, A.
Narayanan, V.
Ando, T.
author_sort Gong, N.
collection PubMed
description Dense crossbar arrays of non-volatile memory (NVM) can potentially enable massively parallel and highly energy-efficient neuromorphic computing systems. The key requirements for the NVM elements are continuous (analog-like) conductance tuning capability and switching symmetry with acceptable noise levels. However, most NVM devices show non-linear and asymmetric switching behaviors. Such non-linear behaviors render separation of signal and noise extremely difficult with conventional characterization techniques. In this study, we establish a practical methodology based on Gaussian process regression to address this issue. The methodology is agnostic to switching mechanisms and applicable to various NVM devices. We show tradeoff between switching symmetry and signal-to-noise ratio for HfO(2)-based resistive random access memory. Then, we characterize 1000 phase-change memory devices based on Ge(2)Sb(2)Te(5) and separate total variability into device-to-device variability and inherent randomness from individual devices. These results highlight the usefulness of our methodology to realize ideal NVM devices for neuromorphic computing.
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spelling pubmed-59744072018-05-31 Signal and noise extraction from analog memory elements for neuromorphic computing Gong, N. Idé, T. Kim, S. Boybat, I. Sebastian, A. Narayanan, V. Ando, T. Nat Commun Article Dense crossbar arrays of non-volatile memory (NVM) can potentially enable massively parallel and highly energy-efficient neuromorphic computing systems. The key requirements for the NVM elements are continuous (analog-like) conductance tuning capability and switching symmetry with acceptable noise levels. However, most NVM devices show non-linear and asymmetric switching behaviors. Such non-linear behaviors render separation of signal and noise extremely difficult with conventional characterization techniques. In this study, we establish a practical methodology based on Gaussian process regression to address this issue. The methodology is agnostic to switching mechanisms and applicable to various NVM devices. We show tradeoff between switching symmetry and signal-to-noise ratio for HfO(2)-based resistive random access memory. Then, we characterize 1000 phase-change memory devices based on Ge(2)Sb(2)Te(5) and separate total variability into device-to-device variability and inherent randomness from individual devices. These results highlight the usefulness of our methodology to realize ideal NVM devices for neuromorphic computing. Nature Publishing Group UK 2018-05-29 /pmc/articles/PMC5974407/ /pubmed/29844421 http://dx.doi.org/10.1038/s41467-018-04485-1 Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Gong, N.
Idé, T.
Kim, S.
Boybat, I.
Sebastian, A.
Narayanan, V.
Ando, T.
Signal and noise extraction from analog memory elements for neuromorphic computing
title 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_short Signal and noise extraction from analog memory elements for neuromorphic computing
title_sort signal and noise extraction from analog memory elements for neuromorphic computing
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5974407/
https://www.ncbi.nlm.nih.gov/pubmed/29844421
http://dx.doi.org/10.1038/s41467-018-04485-1
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