Overview of Memristor-Based Neural Network Design and Applications
Conventional von Newmann-based computers face severe challenges in the processing and storage of the large quantities of data being generated in the current era of “big data.” One of the most promising solutions to this issue is the development of an artificial neural network (ANN) that can process...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-07-01
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| Series: | Frontiers in Physics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphy.2022.839243/full |
| _version_ | 1811224571577106432 |
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| author | Longcheng Ye Zhixuan Gao Jinke Fu Wang Ren Cihui Yang Jing Wen Xiang Wan Qingying Ren Shipu Gu Xiaoyan Liu Xiaojuan Lian Xiaojuan Lian Lei Wang |
| author_facet | Longcheng Ye Zhixuan Gao Jinke Fu Wang Ren Cihui Yang Jing Wen Xiang Wan Qingying Ren Shipu Gu Xiaoyan Liu Xiaojuan Lian Xiaojuan Lian Lei Wang |
| author_sort | Longcheng Ye |
| collection | DOAJ |
| description | Conventional von Newmann-based computers face severe challenges in the processing and storage of the large quantities of data being generated in the current era of “big data.” One of the most promising solutions to this issue is the development of an artificial neural network (ANN) that can process and store data in a manner similar to that of the human brain. To extend the limits of Moore’s law, memristors, whose electrical and optical behaviors closely match the biological response of the human brain, have been implemented for ANNs in place of the traditional complementary metal-oxide-semiconductor (CMOS) components. Based on their different operation modes, we classify the memristor family into electronic, photonic, and optoelectronic memristors, and review their respective physical principles and state-of-the-art technologies. Subsequently, we discuss the design strategies, performance superiorities, and technical drawbacks of various memristors in relation to ANN applications, as well as the updated versions of ANN, such as deep neutral networks (DNNs) and spike neural networks (SNNs). This paper concludes by envisioning the potential approaches for overcoming the physical limitations of memristor-based neural networks and the outlook of memristor applications on emerging neural networks. |
| first_indexed | 2024-04-12T08:51:12Z |
| format | Article |
| id | doaj.art-071bcbf6d6904e2aa470af09e1c4aa0f |
| institution | Directory Open Access Journal |
| issn | 2296-424X |
| language | English |
| last_indexed | 2024-04-12T08:51:12Z |
| publishDate | 2022-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physics |
| spelling | doaj.art-071bcbf6d6904e2aa470af09e1c4aa0f2022-12-22T03:39:34ZengFrontiers Media S.A.Frontiers in Physics2296-424X2022-07-011010.3389/fphy.2022.839243839243Overview of Memristor-Based Neural Network Design and ApplicationsLongcheng Ye0Zhixuan Gao1Jinke Fu2Wang Ren3Cihui Yang4Jing Wen5Xiang Wan6Qingying Ren7Shipu Gu8Xiaoyan Liu9Xiaojuan Lian10Xiaojuan Lian11Lei Wang12College of Integrated Circuit Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing, ChinaCollege of Integrated Circuit Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing, ChinaCollege of Integrated Circuit Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing, ChinaShanghai Aerospace Electronic Technology Institute, Shanghai, ChinaSchool of Information Engineering, Nanchang Hangkong University, Nanchang, ChinaSchool of Information Engineering, Nanchang Hangkong University, Nanchang, ChinaCollege of Integrated Circuit Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing, ChinaElectrical and Electronic Laboratory Center, College of Electrical and Optical Engineering and College of Microelectronics, Nanjing University of Post and Telecommunications, Nanjing, ChinaCollege of Electronic and Optical Engineering, College of Microelectronics, Nanjing University of Posts and Telecommunications, Nanjing, ChinaCollege of Integrated Circuit Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing, ChinaCollege of Integrated Circuit Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing, ChinaNational and Local Joint Engineering Laboratory of RF Integration and Micro-Assembly Technology, Nanjing, ChinaCollege of Integrated Circuit Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing, ChinaConventional von Newmann-based computers face severe challenges in the processing and storage of the large quantities of data being generated in the current era of “big data.” One of the most promising solutions to this issue is the development of an artificial neural network (ANN) that can process and store data in a manner similar to that of the human brain. To extend the limits of Moore’s law, memristors, whose electrical and optical behaviors closely match the biological response of the human brain, have been implemented for ANNs in place of the traditional complementary metal-oxide-semiconductor (CMOS) components. Based on their different operation modes, we classify the memristor family into electronic, photonic, and optoelectronic memristors, and review their respective physical principles and state-of-the-art technologies. Subsequently, we discuss the design strategies, performance superiorities, and technical drawbacks of various memristors in relation to ANN applications, as well as the updated versions of ANN, such as deep neutral networks (DNNs) and spike neural networks (SNNs). This paper concludes by envisioning the potential approaches for overcoming the physical limitations of memristor-based neural networks and the outlook of memristor applications on emerging neural networks.https://www.frontiersin.org/articles/10.3389/fphy.2022.839243/fullartificial neural networkelectronic memristorphotonic memristoroptoelectronic memristoremerging neural networks |
| spellingShingle | Longcheng Ye Zhixuan Gao Jinke Fu Wang Ren Cihui Yang Jing Wen Xiang Wan Qingying Ren Shipu Gu Xiaoyan Liu Xiaojuan Lian Xiaojuan Lian Lei Wang Overview of Memristor-Based Neural Network Design and Applications Frontiers in Physics artificial neural network electronic memristor photonic memristor optoelectronic memristor emerging neural networks |
| title | Overview of Memristor-Based Neural Network Design and Applications |
| title_full | Overview of Memristor-Based Neural Network Design and Applications |
| title_fullStr | Overview of Memristor-Based Neural Network Design and Applications |
| title_full_unstemmed | Overview of Memristor-Based Neural Network Design and Applications |
| title_short | Overview of Memristor-Based Neural Network Design and Applications |
| title_sort | overview of memristor based neural network design and applications |
| topic | artificial neural network electronic memristor photonic memristor optoelectronic memristor emerging neural networks |
| url | https://www.frontiersin.org/articles/10.3389/fphy.2022.839243/full |
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