Design of multilayer cellular neural network based on memristor crossbar and its application to edge detection

作     者：YU Yongbin TANG Haowen FENG Xiao WANG Xiangxiang HUANG Hang YU Yongbin;TANG Haowen;FENG Xiao;WANG Xiangxiang;HUANG Hang

作者机构：School of Information and software EngineeringUniversity of Electronic Science and Technology of ChinaChengdu 610054China 

出 版 物：《Journal of Systems Engineering and Electronics》 (系统工程与电子技术（英文版）)

年 卷 期：2023年第34卷第3期

页      面：641-649页

核心收录：

学科分类：1201[管理学-管理科学与工程(可授管理学、工学学位)] 0808[工学-电气工程] 08[工学] 081104[工学-模式识别与智能系统] 080203[工学-机械设计及理论] 0802[工学-机械工程] 0811[工学-控制科学与工程] 

基　　金：supported by the Research Fund for International Young Scientists of the National Natural Science Foundation of China(61550110248) the Research on Fundamental Theory of Shared Intelligent Street Lamp for New Scene Service(H04W200495) Sichuan Science and Technology Program(2019YFG0190) the Research on Sino-Tibetan Multi-source Information Acquisition,Fusion,Data Mining and its Application(H04W170186). 

主　　题：edge detection figure of merit(FOM) memristor crossbar synaptic circuit memristor crossbar-based cellular neural network(MCM-CNN) 

摘      要：Memristor with memory properties can be applied to connection points(synapses)between cells in a cellular neural network(CNN).This paper highlights memristor crossbar-based multilayer CNN(MCM-CNN)and its application to edge detection.An MCM-CNN is designed by adopting a memristor crossbar composed of a pair of memristors.MCM-CNN based on the memristor crossbar with changeable weight is suitable for edge detection of a binary image and a color image considering its characteristics of programmablization and compactation.Figure of merit(FOM)is introduced to evaluate the proposed structure and several traditional edge detection operators for edge detection results.Experiment results show that the FOM of MCM-CNN is three times more than that of the traditional edge detection operators.