Self-Powered Memristive Systems for Storage and Neuromorphic Computing

Author:

Shi Jiajuan,Wang Zhongqiang,Tao Ye,Xu Haiyang,Zhao Xiaoning,Lin Ya,Liu Yichun

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.

Funder

Ministry of Science and Technology of the People's Republic of China

National Natural Science Foundation of China

National Science Fund for Distinguished Young Scholars

Publisher

Frontiers Media SA

Subject

General Neuroscience

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