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Self-Powered Memristive Systems for Storage and Neuromorphic Computing

Authors :
Jiajuan Shi
Zhongqiang Wang
Ye Tao
Haiyang Xu
Xiaoning Zhao
Ya Lin
Yichun Liu
Source :
Frontiers in Neuroscience, Vol 15 (2021)
Publication Year :
2021
Publisher :
Frontiers Media S.A., 2021.

Abstract

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.

Details

Language :
English
ISSN :
1662453X
Volume :
15
Database :
Directory of Open Access Journals
Journal :
Frontiers in Neuroscience
Publication Type :
Academic Journal
Accession number :
edsdoj.9eb34512d254125885cab818e362b5a
Document Type :
article
Full Text :
https://doi.org/10.3389/fnins.2021.662457