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Neuromorphic computing with multi-memristive synapses

Neuromorphic computing has emerged as a promising avenue towards building the next generation of intelligent computing systems. It has been proposed that memristive devices, which exhibit history-dependent conductivity modulation, could efficiently represent the synaptic weights in artificial neural...

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Autores principales: Boybat, Irem, Le Gallo, Manuel, Nandakumar, S. R., Moraitis, Timoleon, Parnell, Thomas, Tuma, Tomas, Rajendran, Bipin, Leblebici, Yusuf, Sebastian, Abu, Eleftheriou, Evangelos
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6023896/
https://www.ncbi.nlm.nih.gov/pubmed/29955057
http://dx.doi.org/10.1038/s41467-018-04933-y
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author Boybat, Irem
Le Gallo, Manuel
Nandakumar, S. R.
Moraitis, Timoleon
Parnell, Thomas
Tuma, Tomas
Rajendran, Bipin
Leblebici, Yusuf
Sebastian, Abu
Eleftheriou, Evangelos
author_facet Boybat, Irem
Le Gallo, Manuel
Nandakumar, S. R.
Moraitis, Timoleon
Parnell, Thomas
Tuma, Tomas
Rajendran, Bipin
Leblebici, Yusuf
Sebastian, Abu
Eleftheriou, Evangelos
author_sort Boybat, Irem
collection PubMed
description Neuromorphic computing has emerged as a promising avenue towards building the next generation of intelligent computing systems. It has been proposed that memristive devices, which exhibit history-dependent conductivity modulation, could efficiently represent the synaptic weights in artificial neural networks. However, precise modulation of the device conductance over a wide dynamic range, necessary to maintain high network accuracy, is proving to be challenging. To address this, we present a multi-memristive synaptic architecture with an efficient global counter-based arbitration scheme. We focus on phase change memory devices, develop a comprehensive model and demonstrate via simulations the effectiveness of the concept for both spiking and non-spiking neural networks. Moreover, we present experimental results involving over a million phase change memory devices for unsupervised learning of temporal correlations using a spiking neural network. The work presents a significant step towards the realization of large-scale and energy-efficient neuromorphic computing systems.
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spelling pubmed-60238962018-07-02 Neuromorphic computing with multi-memristive synapses Boybat, Irem Le Gallo, Manuel Nandakumar, S. R. Moraitis, Timoleon Parnell, Thomas Tuma, Tomas Rajendran, Bipin Leblebici, Yusuf Sebastian, Abu Eleftheriou, Evangelos Nat Commun Article Neuromorphic computing has emerged as a promising avenue towards building the next generation of intelligent computing systems. It has been proposed that memristive devices, which exhibit history-dependent conductivity modulation, could efficiently represent the synaptic weights in artificial neural networks. However, precise modulation of the device conductance over a wide dynamic range, necessary to maintain high network accuracy, is proving to be challenging. To address this, we present a multi-memristive synaptic architecture with an efficient global counter-based arbitration scheme. We focus on phase change memory devices, develop a comprehensive model and demonstrate via simulations the effectiveness of the concept for both spiking and non-spiking neural networks. Moreover, we present experimental results involving over a million phase change memory devices for unsupervised learning of temporal correlations using a spiking neural network. The work presents a significant step towards the realization of large-scale and energy-efficient neuromorphic computing systems. Nature Publishing Group UK 2018-06-28 /pmc/articles/PMC6023896/ /pubmed/29955057 http://dx.doi.org/10.1038/s41467-018-04933-y 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
Boybat, Irem
Le Gallo, Manuel
Nandakumar, S. R.
Moraitis, Timoleon
Parnell, Thomas
Tuma, Tomas
Rajendran, Bipin
Leblebici, Yusuf
Sebastian, Abu
Eleftheriou, Evangelos
Neuromorphic computing with multi-memristive synapses
title Neuromorphic computing with multi-memristive synapses
title_full Neuromorphic computing with multi-memristive synapses
title_fullStr Neuromorphic computing with multi-memristive synapses
title_full_unstemmed Neuromorphic computing with multi-memristive synapses
title_short Neuromorphic computing with multi-memristive synapses
title_sort neuromorphic computing with multi-memristive synapses
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6023896/
https://www.ncbi.nlm.nih.gov/pubmed/29955057
http://dx.doi.org/10.1038/s41467-018-04933-y
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