Memristive Artificial Synapses for Neuromorphic Computing

被引:12
作者
Wen Huang [1 ]
Xuwen Xia [1 ]
Chen Zhu [2 ]
Parker Steichen [3 ]
Weidong Quan [1 ]
Weiwei Mao [1 ]
Jianping Yang [1 ]
Liang Chu [1 ]
Xingao Li [1 ,4 ]
机构
[1] New Energy Technology Engineering Laboratory of Jiangsu Province and School of Science, Nanjing University of Posts and Telecommunications (NJUPT)
[2] College of Electronic and Optical Engineering and College of Microelectronics, Nanjing University of Posts and Telecommunications (NJUPT)
[3] Department of Materials Science and Engineering, University of Washington
[4] Key Laboratory for Organic Electronics and Information Displays and Institute of Advanced Materials, Jiangsu National Synergistic Innovation Center for Advanced Materials, School of Materials Science and Engineering, Nanjing University of Posts and Teleco
关键词
D O I
暂无
中图分类号
TN60 [一般性问题]; TP183 [人工神经网络与计算];
学科分类号
080903 ; 081104 ; 0812 ; 0835 ; 1405 ;
摘要
Neuromorphic computing simulates the operation of biological brain function for information processing and can potentially solve the bottleneck of the von Neumann architecture.This computing is realized based on memristive hardware neural networks in which synaptic devices that mimic biological synapses of the brain are the primary units.Mimicking synaptic functions with these devices is critical in neuromorphic systems.In the last decade,electrical and optical signals have been incorporated into the synaptic devices and promoted the simulation of various synaptic functions.In this review,these devices are discussed by categorizing them into electrically stimulated,optically stimulated,and photoelectric synergetic synaptic devices based on stimulation of electrical and optical signals.The working mechanisms of the devices are analyzed in detail.This is followed by a discussion of the progress in mimicking synaptic functions.In addition,existing application scenarios of various synaptic devices are outlined.Furthermore,the performances and future development of the synaptic devices that could be significant for building efficient neuromorphic systems are prospected.
引用
收藏
页码:224 / 251
页数:28
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