Memristor's characteristics: From non-ideal to ideal

doi:10.1088/1674-1056/ac7548

中国物理B ›› 2023, Vol. 32 ›› Issue (2): 28401-028401.doi: 10.1088/1674-1056/ac7548

Memristor's characteristics: From non-ideal to ideal

Fan Sun(孙帆), Jing Su(粟静), Jie Li(李杰), Shukai Duan(段书凯), and Xiaofang Hu(胡小方)^†

College of Artificial Intelligence, Southwest University, Chongqing 400715, China

收稿日期:2022-05-02 修回日期:2022-05-23 接受日期:2022-06-02 出版日期:2023-01-10 发布日期:2023-01-10
通讯作者: Xiaofang Hu E-mail:huxf@swu.edu.cn
基金资助:
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).

Memristor's characteristics: From non-ideal to ideal

Fan Sun(孙帆), Jing Su(粟静), Jie Li(李杰), Shukai Duan(段书凯), and Xiaofang Hu(胡小方)^†

College of Artificial Intelligence, Southwest University, Chongqing 400715, China

Received:2022-05-02 Revised:2022-05-23 Accepted:2022-06-02 Online:2023-01-10 Published:2023-01-10
Contact: Xiaofang Hu E-mail:huxf@swu.edu.cn
Supported by:
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).

摘要/Abstract

摘要： 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.

关键词: memristor, non-ideal characteristic, genetic algorithm, path planning

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.

Key words: memristor, non-ideal characteristic, genetic algorithm, path planning

中图分类号: (Neural networks)

84.35.+i

84.37.+q (Measurements in electric variables (including voltage, current, resistance, capacitance, inductance, impedance, and admittance, etc.)) 87.19.lv (Learning and memory)

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[J]. 中国物理B, 2023, 32(2): 28401-028401.

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[J]. Chin. Phys. B, 2023, 32(2): 28401-028401.

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Memristor's characteristics: From non-ideal to ideal

Memristor's characteristics: From non-ideal to ideal

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