Optogenetics inspired transition metal dichalcogenide neuristors for in-memory deep recurrent neural networks

Accomplishing complex cognitive tasks such as speech recognition calls for artificial intelligence hardware with high computing precision. John et al. propose deep recurrent neural networks based on optoelectronic transition metal dichalcogenide memristors with high weight precision for in-memory co...

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Autores principales:	Rohit Abraham John, Jyotibdha Acharya, Chao Zhu, Abhijith Surendran, Sumon Kumar Bose, Apoorva Chaturvedi, Nidhi Tiwari, Yang Gao, Yongmin He, Keke K. Zhang, Manzhang Xu, Wei Lin Leong, Zheng Liu, Arindam Basu, Nripan Mathews
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/6c1806d8270d4d5db127aa7ebd08d4ee
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spelling	oai:doaj.org-article:6c1806d8270d4d5db127aa7ebd08d4ee2021-12-02T17:12:24ZOptogenetics inspired transition metal dichalcogenide neuristors for in-memory deep recurrent neural networks10.1038/s41467-020-16985-02041-1723https://doaj.org/article/6c1806d8270d4d5db127aa7ebd08d4ee2020-06-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-16985-0https://doaj.org/toc/2041-1723Accomplishing complex cognitive tasks such as speech recognition calls for artificial intelligence hardware with high computing precision. John et al. propose deep recurrent neural networks based on optoelectronic transition metal dichalcogenide memristors with high weight precision for in-memory computing.Rohit Abraham JohnJyotibdha AcharyaChao ZhuAbhijith SurendranSumon Kumar BoseApoorva ChaturvediNidhi TiwariYang GaoYongmin HeKeke K. ZhangManzhang XuWei Lin LeongZheng LiuArindam BasuNripan MathewsNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-9 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Rohit Abraham John Jyotibdha Acharya Chao Zhu Abhijith Surendran Sumon Kumar Bose Apoorva Chaturvedi Nidhi Tiwari Yang Gao Yongmin He Keke K. Zhang Manzhang Xu Wei Lin Leong Zheng Liu Arindam Basu Nripan Mathews Optogenetics inspired transition metal dichalcogenide neuristors for in-memory deep recurrent neural networks
description	Accomplishing complex cognitive tasks such as speech recognition calls for artificial intelligence hardware with high computing precision. John et al. propose deep recurrent neural networks based on optoelectronic transition metal dichalcogenide memristors with high weight precision for in-memory computing.
format	article
author	Rohit Abraham John Jyotibdha Acharya Chao Zhu Abhijith Surendran Sumon Kumar Bose Apoorva Chaturvedi Nidhi Tiwari Yang Gao Yongmin He Keke K. Zhang Manzhang Xu Wei Lin Leong Zheng Liu Arindam Basu Nripan Mathews
author_facet	Rohit Abraham John Jyotibdha Acharya Chao Zhu Abhijith Surendran Sumon Kumar Bose Apoorva Chaturvedi Nidhi Tiwari Yang Gao Yongmin He Keke K. Zhang Manzhang Xu Wei Lin Leong Zheng Liu Arindam Basu Nripan Mathews
author_sort	Rohit Abraham John
title	Optogenetics inspired transition metal dichalcogenide neuristors for in-memory deep recurrent neural networks
title_short	Optogenetics inspired transition metal dichalcogenide neuristors for in-memory deep recurrent neural networks
title_full	Optogenetics inspired transition metal dichalcogenide neuristors for in-memory deep recurrent neural networks
title_fullStr	Optogenetics inspired transition metal dichalcogenide neuristors for in-memory deep recurrent neural networks
title_full_unstemmed	Optogenetics inspired transition metal dichalcogenide neuristors for in-memory deep recurrent neural networks
title_sort	optogenetics inspired transition metal dichalcogenide neuristors for in-memory deep recurrent neural networks
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/6c1806d8270d4d5db127aa7ebd08d4ee
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Optogenetics inspired transition metal dichalcogenide neuristors for in-memory deep recurrent neural networks

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