Abstract Memristor has been widely studied in the field of neuromorphic computing and is considered to be a strong candidate to break the von Neumann bottleneck. However, the non-ideal characteristics of memristor seriously limit its practical application. There are two sides to everything, and memristors are no exception. The non-ideal characteristics of memristors may become ideal in some applications. Genetic algorithm (GA) is a method to search for the optimal solution by simulating the process of biological evolution. It is widely used in the fields of machine learning, combinatorial optimization, and signal processing. In this paper, we simulate the biological evolutionary behavior in GA by using the non-ideal characteristics of memristors, based on which we design peripheral circuits and path planning algorithms based on memristor networks. The experimental results show that the non-ideal characteristics of memristor can well simulate the biological evolution behavior in GA.
Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 61976246 and U20A20227), the Natural Science Foundation of Chongqing, China (Grant No. cstc2020jcyj-msxm X0385), and the National Key R&D Program of China (Grant Nos. 2018YFB130660 and 2018YFB1306604).
Corresponding Authors:
Xiaofang Hu
E-mail: huxf@swu.edu.cn
Cite this article:
Fan Sun(孙帆), Jing Su(粟静), Jie Li(李杰), Shukai Duan(段书凯), and Xiaofang Hu(胡小方) Memristor's characteristics: From non-ideal to ideal 2023 Chin. Phys. B 32 028401
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