Alternative negative weight for simpler hardware implementation of synapse device based neuromorphic system

Abstract Lately, there has been a rapid increase in the use of software-based deep learning neural networks (S-DNN) for the analysis of unstructured data consumption. For implementation of the S-DNN, synapse-device-based hardware DNN (H-DNN) has been proposed as an alternative to typical Von-Neumann...

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Autores principales:	Geonhui Han, Chuljun Lee, Jae-Eun Lee, Jongseon Seo, Myungjun Kim, Yubin Song, Young-Ho Seo, Daeseok Lee
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/aec75e692f634a0a90acfdf00a3e5311
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spelling	oai:doaj.org-article:aec75e692f634a0a90acfdf00a3e53112021-12-05T12:14:47ZAlternative negative weight for simpler hardware implementation of synapse device based neuromorphic system10.1038/s41598-021-02176-42045-2322https://doaj.org/article/aec75e692f634a0a90acfdf00a3e53112021-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-02176-4https://doaj.org/toc/2045-2322Abstract Lately, there has been a rapid increase in the use of software-based deep learning neural networks (S-DNN) for the analysis of unstructured data consumption. For implementation of the S-DNN, synapse-device-based hardware DNN (H-DNN) has been proposed as an alternative to typical Von-Neumann structural computing systems. In the H-DNN, various numerical values such as the synaptic weight, activation function, and etc., have to be realized through electrical device or circuit. Among them, the synaptic weight that should have both positive and negative numerical values needs to be implemented in a simpler way. Because the synaptic weight has been expressed by conductance value of the synapse device, it always has a positive value. Therefore, typically, a pair of synapse devices is required to realize the negative weight values, which leads to additional hardware resources such as more devices, higher power consumption, larger area, and increased circuit complexity. Herein, we propose an alternative simpler method to realize the negative weight (named weight shifter) and its hardware implementation. To demonstrate the weight shifter, we investigated its theoretical, numerical, and circuit-related aspects, following which the H-DNN circuit was successfully implemented on a printed circuit board.Geonhui HanChuljun LeeJae-Eun LeeJongseon SeoMyungjun KimYubin SongYoung-Ho SeoDaeseok LeeNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-7 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Geonhui Han Chuljun Lee Jae-Eun Lee Jongseon Seo Myungjun Kim Yubin Song Young-Ho Seo Daeseok Lee Alternative negative weight for simpler hardware implementation of synapse device based neuromorphic system
description	Abstract Lately, there has been a rapid increase in the use of software-based deep learning neural networks (S-DNN) for the analysis of unstructured data consumption. For implementation of the S-DNN, synapse-device-based hardware DNN (H-DNN) has been proposed as an alternative to typical Von-Neumann structural computing systems. In the H-DNN, various numerical values such as the synaptic weight, activation function, and etc., have to be realized through electrical device or circuit. Among them, the synaptic weight that should have both positive and negative numerical values needs to be implemented in a simpler way. Because the synaptic weight has been expressed by conductance value of the synapse device, it always has a positive value. Therefore, typically, a pair of synapse devices is required to realize the negative weight values, which leads to additional hardware resources such as more devices, higher power consumption, larger area, and increased circuit complexity. Herein, we propose an alternative simpler method to realize the negative weight (named weight shifter) and its hardware implementation. To demonstrate the weight shifter, we investigated its theoretical, numerical, and circuit-related aspects, following which the H-DNN circuit was successfully implemented on a printed circuit board.
format	article
author	Geonhui Han Chuljun Lee Jae-Eun Lee Jongseon Seo Myungjun Kim Yubin Song Young-Ho Seo Daeseok Lee
author_facet	Geonhui Han Chuljun Lee Jae-Eun Lee Jongseon Seo Myungjun Kim Yubin Song Young-Ho Seo Daeseok Lee
author_sort	Geonhui Han
title	Alternative negative weight for simpler hardware implementation of synapse device based neuromorphic system
title_short	Alternative negative weight for simpler hardware implementation of synapse device based neuromorphic system
title_full	Alternative negative weight for simpler hardware implementation of synapse device based neuromorphic system
title_fullStr	Alternative negative weight for simpler hardware implementation of synapse device based neuromorphic system
title_full_unstemmed	Alternative negative weight for simpler hardware implementation of synapse device based neuromorphic system
title_sort	alternative negative weight for simpler hardware implementation of synapse device based neuromorphic system
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/aec75e692f634a0a90acfdf00a3e5311
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Alternative negative weight for simpler hardware implementation of synapse device based neuromorphic system

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