Neuromorphic devices assisted by machine learning algorithms
Neuromorphic computing extends beyond sequential processing modalities and outperforms traditional von Neumann architectures in implementing more complicated tasks, e.g., pattern processing, image recognition, and decision making. It features parallel interconnected neural networks, high fault toler...
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| Format: | Article |
| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | International Journal of Extreme Manufacturing |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/2631-7990/adba1e |
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| _version_ | 1849762437964759040 |
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| author | Ziwei Huo Qijun Sun Jinran Yu Yichen Wei Yifei Wang Jeong Ho Cho Zhong Lin Wang |
| author_facet | Ziwei Huo Qijun Sun Jinran Yu Yichen Wei Yifei Wang Jeong Ho Cho Zhong Lin Wang |
| author_sort | Ziwei Huo |
| collection | DOAJ |
| description | Neuromorphic computing extends beyond sequential processing modalities and outperforms traditional von Neumann architectures in implementing more complicated tasks, e.g., pattern processing, image recognition, and decision making. It features parallel interconnected neural networks, high fault tolerance, robustness, autonomous learning capability, and ultralow energy dissipation. The algorithms of artificial neural network (ANN) have also been widely used because of their facile self-organization and self-learning capabilities, which mimic those of the human brain. To some extent, ANN reflects several basic functions of the human brain and can be efficiently integrated into neuromorphic devices to perform neuromorphic computations. This review highlights recent advances in neuromorphic devices assisted by machine learning algorithms. First, the basic structure of simple neuron models inspired by biological neurons and the information processing in simple neural networks are particularly discussed. Second, the fabrication and research progress of neuromorphic devices are presented regarding to materials and structures. Furthermore, the fabrication of neuromorphic devices, including stand-alone neuromorphic devices, neuromorphic device arrays, and integrated neuromorphic systems, is discussed and demonstrated with reference to some respective studies. The applications of neuromorphic devices assisted by machine learning algorithms in different fields are categorized and investigated. Finally, perspectives, suggestions, and potential solutions to the current challenges of neuromorphic devices are provided. |
| format | Article |
| id | doaj-art-b544030062d84659bde2b6d2fb593caf |
| institution | DOAJ |
| issn | 2631-7990 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | International Journal of Extreme Manufacturing |
| spelling | doaj-art-b544030062d84659bde2b6d2fb593caf2025-08-20T03:05:44ZengIOP PublishingInternational Journal of Extreme Manufacturing2631-79902025-01-017404200710.1088/2631-7990/adba1eNeuromorphic devices assisted by machine learning algorithmsZiwei Huo0Qijun Sun1https://orcid.org/0000-0003-2130-7389Jinran Yu2Yichen Wei3Yifei Wang4Jeong Ho Cho5https://orcid.org/0000-0002-1030-9920Zhong Lin Wang6Beijing Institute of Nanoenergy and Nanosystems , Chinese Academy of Sciences, Beijing 101400, People’s Republic of China; School of Nanoscience and Engineering, University of Chinese Academy of Sciences , Beijing 100049, People’s Republic of ChinaBeijing Institute of Nanoenergy and Nanosystems , Chinese Academy of Sciences, Beijing 101400, People’s Republic of China; Center on Nanoenergy Research, School of Physical Science and Technology, Guangxi University , Nanning 530004, People’s Republic of China; Shandong Zhongke Naneng Energy Technology Co. , Ltd, Dongying 7061, People’s Republic of ChinaBeijing Institute of Nanoenergy and Nanosystems , Chinese Academy of Sciences, Beijing 101400, People’s Republic of ChinaBeijing Institute of Nanoenergy and Nanosystems , Chinese Academy of Sciences, Beijing 101400, People’s Republic of China; Center on Nanoenergy Research, School of Physical Science and Technology, Guangxi University , Nanning 530004, People’s Republic of ChinaBeijing Institute of Nanoenergy and Nanosystems , Chinese Academy of Sciences, Beijing 101400, People’s Republic of China; School of Nanoscience and Engineering, University of Chinese Academy of Sciences , Beijing 100049, People’s Republic of ChinaDepartment of Chemical and Biomolecular Engineering, Yonsei University , Seoul 03722, Republic of KoreaBeijing Institute of Nanoenergy and Nanosystems , Chinese Academy of Sciences, Beijing 101400, People’s Republic of China; Georgia Institute of Technology , Atlanta, GA 30332, United States of AmericaNeuromorphic computing extends beyond sequential processing modalities and outperforms traditional von Neumann architectures in implementing more complicated tasks, e.g., pattern processing, image recognition, and decision making. It features parallel interconnected neural networks, high fault tolerance, robustness, autonomous learning capability, and ultralow energy dissipation. The algorithms of artificial neural network (ANN) have also been widely used because of their facile self-organization and self-learning capabilities, which mimic those of the human brain. To some extent, ANN reflects several basic functions of the human brain and can be efficiently integrated into neuromorphic devices to perform neuromorphic computations. This review highlights recent advances in neuromorphic devices assisted by machine learning algorithms. First, the basic structure of simple neuron models inspired by biological neurons and the information processing in simple neural networks are particularly discussed. Second, the fabrication and research progress of neuromorphic devices are presented regarding to materials and structures. Furthermore, the fabrication of neuromorphic devices, including stand-alone neuromorphic devices, neuromorphic device arrays, and integrated neuromorphic systems, is discussed and demonstrated with reference to some respective studies. The applications of neuromorphic devices assisted by machine learning algorithms in different fields are categorized and investigated. Finally, perspectives, suggestions, and potential solutions to the current challenges of neuromorphic devices are provided.https://doi.org/10.1088/2631-7990/adba1eneuromorphic devicesmachine learning algorithmsartificial synapsesmemristorsfield-effect transistors |
| spellingShingle | Ziwei Huo Qijun Sun Jinran Yu Yichen Wei Yifei Wang Jeong Ho Cho Zhong Lin Wang Neuromorphic devices assisted by machine learning algorithms International Journal of Extreme Manufacturing neuromorphic devices machine learning algorithms artificial synapses memristors field-effect transistors |
| title | Neuromorphic devices assisted by machine learning algorithms |
| title_full | Neuromorphic devices assisted by machine learning algorithms |
| title_fullStr | Neuromorphic devices assisted by machine learning algorithms |
| title_full_unstemmed | Neuromorphic devices assisted by machine learning algorithms |
| title_short | Neuromorphic devices assisted by machine learning algorithms |
| title_sort | neuromorphic devices assisted by machine learning algorithms |
| topic | neuromorphic devices machine learning algorithms artificial synapses memristors field-effect transistors |
| url | https://doi.org/10.1088/2631-7990/adba1e |
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