Reconfigurable Stochastic neurons based on tin oxide/MoS2 hetero-memristors for simulated annealing and the Boltzmann machine

Boltzmann Machines offer the potential of more efficient solutions to combinatorial problems compared to von Neumann computing architectures. Here, Yan et al introduce a stochastic memristor with dynamically tunable properties, a vital feature for the efficient implementation of a Boltzmann Machine.

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Autores principales:	Xiaodong Yan, Jiahui Ma, Tong Wu, Aoyang Zhang, Jiangbin Wu, Matthew Chin, Zhihan Zhang, Madan Dubey, Wei Wu, Mike Shuo-Wei Chen, Jing Guo, Han Wang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/9f972e06789b46f28aedffae3abf322b
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spelling	oai:doaj.org-article:9f972e06789b46f28aedffae3abf322b2021-12-02T17:18:17ZReconfigurable Stochastic neurons based on tin oxide/MoS2 hetero-memristors for simulated annealing and the Boltzmann machine10.1038/s41467-021-26012-52041-1723https://doaj.org/article/9f972e06789b46f28aedffae3abf322b2021-09-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-26012-5https://doaj.org/toc/2041-1723Boltzmann Machines offer the potential of more efficient solutions to combinatorial problems compared to von Neumann computing architectures. Here, Yan et al introduce a stochastic memristor with dynamically tunable properties, a vital feature for the efficient implementation of a Boltzmann Machine.Xiaodong YanJiahui MaTong WuAoyang ZhangJiangbin WuMatthew ChinZhihan ZhangMadan DubeyWei WuMike Shuo-Wei ChenJing GuoHan WangNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Xiaodong Yan Jiahui Ma Tong Wu Aoyang Zhang Jiangbin Wu Matthew Chin Zhihan Zhang Madan Dubey Wei Wu Mike Shuo-Wei Chen Jing Guo Han Wang Reconfigurable Stochastic neurons based on tin oxide/MoS2 hetero-memristors for simulated annealing and the Boltzmann machine
description	Boltzmann Machines offer the potential of more efficient solutions to combinatorial problems compared to von Neumann computing architectures. Here, Yan et al introduce a stochastic memristor with dynamically tunable properties, a vital feature for the efficient implementation of a Boltzmann Machine.
format	article
author	Xiaodong Yan Jiahui Ma Tong Wu Aoyang Zhang Jiangbin Wu Matthew Chin Zhihan Zhang Madan Dubey Wei Wu Mike Shuo-Wei Chen Jing Guo Han Wang
author_facet	Xiaodong Yan Jiahui Ma Tong Wu Aoyang Zhang Jiangbin Wu Matthew Chin Zhihan Zhang Madan Dubey Wei Wu Mike Shuo-Wei Chen Jing Guo Han Wang
author_sort	Xiaodong Yan
title	Reconfigurable Stochastic neurons based on tin oxide/MoS2 hetero-memristors for simulated annealing and the Boltzmann machine
title_short	Reconfigurable Stochastic neurons based on tin oxide/MoS2 hetero-memristors for simulated annealing and the Boltzmann machine
title_full	Reconfigurable Stochastic neurons based on tin oxide/MoS2 hetero-memristors for simulated annealing and the Boltzmann machine
title_fullStr	Reconfigurable Stochastic neurons based on tin oxide/MoS2 hetero-memristors for simulated annealing and the Boltzmann machine
title_full_unstemmed	Reconfigurable Stochastic neurons based on tin oxide/MoS2 hetero-memristors for simulated annealing and the Boltzmann machine
title_sort	reconfigurable stochastic neurons based on tin oxide/mos2 hetero-memristors for simulated annealing and the boltzmann machine
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/9f972e06789b46f28aedffae3abf322b
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Reconfigurable Stochastic neurons based on tin oxide/MoS2 hetero-memristors for simulated annealing and the Boltzmann machine

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