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Self-Powered Memristive Systems for Storage and Neuromorphic Computing
A neuromorphic computing chip that can imitate the human brain’s ability to process multiple types of data simultaneously could fundamentally innovate and improve the von-neumann computer architecture, which has been criticized. Memristive devices are among the best hardware units for building neuro...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8044301/ https://www.ncbi.nlm.nih.gov/pubmed/33867930 http://dx.doi.org/10.3389/fnins.2021.662457 |
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author | Shi, Jiajuan Wang, Zhongqiang Tao, Ye Xu, Haiyang Zhao, Xiaoning Lin, Ya Liu, Yichun |
author_facet | Shi, Jiajuan Wang, Zhongqiang Tao, Ye Xu, Haiyang Zhao, Xiaoning Lin, Ya Liu, Yichun |
author_sort | Shi, Jiajuan |
collection | PubMed |
description | A neuromorphic computing chip that can imitate the human brain’s ability to process multiple types of data simultaneously could fundamentally innovate and improve the von-neumann computer architecture, which has been criticized. Memristive devices are among the best hardware units for building neuromorphic intelligence systems due to the fact that they operate at an inherent low voltage, use multi-bit storage, and are cost-effective to manufacture. However, as a passive device, the memristor cell needs external energy to operate, resulting in high power consumption and complicated circuit structure. Recently, an emerging self-powered memristive system, which mainly consists of a memristor and an electric nanogenerator, had the potential to perfectly solve the above problems. It has attracted great interest due to the advantages of its power-free operations. In this review, we give a systematic description of self-powered memristive systems from storage to neuromorphic computing. The review also proves a perspective on the application of artificial intelligence with the self-powered memristive system. |
format | Online Article Text |
id | pubmed-8044301 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-80443012021-04-15 Self-Powered Memristive Systems for Storage and Neuromorphic Computing Shi, Jiajuan Wang, Zhongqiang Tao, Ye Xu, Haiyang Zhao, Xiaoning Lin, Ya Liu, Yichun Front Neurosci Neuroscience A neuromorphic computing chip that can imitate the human brain’s ability to process multiple types of data simultaneously could fundamentally innovate and improve the von-neumann computer architecture, which has been criticized. Memristive devices are among the best hardware units for building neuromorphic intelligence systems due to the fact that they operate at an inherent low voltage, use multi-bit storage, and are cost-effective to manufacture. However, as a passive device, the memristor cell needs external energy to operate, resulting in high power consumption and complicated circuit structure. Recently, an emerging self-powered memristive system, which mainly consists of a memristor and an electric nanogenerator, had the potential to perfectly solve the above problems. It has attracted great interest due to the advantages of its power-free operations. In this review, we give a systematic description of self-powered memristive systems from storage to neuromorphic computing. The review also proves a perspective on the application of artificial intelligence with the self-powered memristive system. Frontiers Media S.A. 2021-03-31 /pmc/articles/PMC8044301/ /pubmed/33867930 http://dx.doi.org/10.3389/fnins.2021.662457 Text en Copyright © 2021 Shi, Wang, Tao, Xu, Zhao, Lin and Liu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Shi, Jiajuan Wang, Zhongqiang Tao, Ye Xu, Haiyang Zhao, Xiaoning Lin, Ya Liu, Yichun Self-Powered Memristive Systems for Storage and Neuromorphic Computing |
title | Self-Powered Memristive Systems for Storage and Neuromorphic Computing |
title_full | Self-Powered Memristive Systems for Storage and Neuromorphic Computing |
title_fullStr | Self-Powered Memristive Systems for Storage and Neuromorphic Computing |
title_full_unstemmed | Self-Powered Memristive Systems for Storage and Neuromorphic Computing |
title_short | Self-Powered Memristive Systems for Storage and Neuromorphic Computing |
title_sort | self-powered memristive systems for storage and neuromorphic computing |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8044301/ https://www.ncbi.nlm.nih.gov/pubmed/33867930 http://dx.doi.org/10.3389/fnins.2021.662457 |
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