神经形态工程学
记忆电阻器
冯·诺依曼建筑
计算机科学
尖峰神经网络
计算机数据存储
电阻随机存取存储器
油藏计算
人工神经网络
计算机体系结构
嵌入式系统
电子工程
电子线路
人工智能
计算机硬件
工程类
操作系统
作者
Jiajuan Shi,Haiyang Xu,Ye Tao,Xiaoning Zhao,Ya Lin,Yichun Liu
标识
DOI:10.3389/fnins.2021.662457
摘要
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.
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