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

doi:10.23919/JSEE.2022.000127

Journal of Systems Engineering and Electronics ›› 2023, Vol. 34 ›› Issue (3): 641-649.doi: 10.23919/JSEE.2022.000127

• ELECTRONICS TECHNOLOGY • Previous Articles

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

Yongbin YU(), Haowen TANG(), Xiao FENG(), Xiangxiang WANG, Hang HUANG

¹ School of Information and software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China

Received:2021-05-15 Accepted:2022-07-07 Online:2023-06-15 Published:2023-06-30
Contact: Yongbin YU E-mail:ybyu@uestc.edu.cn;thw_yx@163.com;fengxiaocd@gmail.com
About author:
YU Yongbin was born in 1975. He received his Ph.D. degree from the University of Electronic Science and Technology of China (UESTC), Chengdu, in 2008. He visited the University of Michigan-Ann Arbor in 2013, and the University of California-Santa Barbara in 2016. He won the first prize of science and technology award of Tibet Autonomous Region in 2018. He worked as a guest deputy director in the Department of Big Data Industry, Sichuan Provincial Economic and Information Commission in 2018. Currently, he is an associate professor in the School of Information and Software Engineering, UESTC. His research interests include memristor-based neural network, swarm intelligence, natural language processing, and big data. E-mail: ybyu@uestc.edu.cn

TANG Haowen was born in 1994. He received his M.S. degree from the School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, in 2020. His current research interests include memristive neural networks, memristive cellular neural networks, and memristive binary neural network and its application. E-mail: thw_yx@163.com

FENG Xiao was born in 1995. He received his M.E. degree in integrated design engineering from Keio Univerisity, Yokohama, Japan, in 2020. His research interests include evolutionary algorithm, neural network, memristor neural network, neural architecture search, and control system. E-mail: fengxiaocd@gmail.com

WANG Xiangxiang was born in 1993. He is currently pursuing his Ph.D. degree in the School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China. His current research interests include coupled memristive neural networks, impulsive memristive neural networks, and switched systems. E-mail: wxxlongtime@gmail.com

HUANG Hang was born in 1993. He received his M.S. degree from the School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, in 2020. His current research interests include field programmable gate array (FPGA), neural networks, and memristor nerueal networks. E-mail: tohang@gmail.com
Supported by:
This work was 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), and the Research on Sino-Tibetan Multi-source Information Acquisition, Fusion, Data Mining and its Application (H04W170186).

Abstract

Abstract:

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.

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

Yongbin YU, Haowen TANG, Xiao FENG, Xiangxiang WANG, Hang HUANG. Design of multilayer cellular neural network based on memristor crossbar and its application to edge detection[J]. Journal of Systems Engineering and Electronics, 2023, 34(3): 641-649.

Figures/Tables 13

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References 35

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Algorithm	Main advantages or disadvantages
Canny	Anti noise interference/edges are marked only once
Gaussian	Accurate edge positioning/noise sensitivity
Log	Noise sensitivity
Prewitt	Anti noise interference
Roberts	Low positioning accuracy
Sobel	Anti noise interference/low precision
MCM-CNN	Variable template/hardware implementation

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[2]	An Liu, Donghua Zhou, Lixin Chen, and Maoyin Chen. Saliency detection and edge feature matching approach for crater extraction [J]. Systems Engineering and Electronics, 2015, 26(6): 1291-1300.
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[4]	Ma Yu, Gu Xiaodong & Wang Yuanyuan. Feature fusion method for edge detection of color images [J]. Journal of Systems Engineering and Electronics, 2009, 20(2): 394-399.
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Design of multilayer cellular neural network based on memristor crossbar and its application to edge detection

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