Toward Memristive Phase‐Change Neural Network with High‐Quality Ultra‐Effective Highly‐Self‐Adjustable Online Learning
Abstract Memristive hardware with reconfigurable conductance levels are leading candidates for achieving artificial neural networks (ANNs). However, owing to difficulties in device character design and circuit combination, the ability to perform complicated online‐learning tasks on a memristive netw...
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| Format: | Article |
| Language: | English |
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Wiley-VCH
2024-03-01
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| Series: | Advanced Physics Research |
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| Online Access: | https://doi.org/10.1002/apxr.202300085 |
| _version_ | 1797270001078501376 |
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| author | Kian‐Guan Lim Shao‐Xiang Go Chun‐Chia Tan Yu Jiang Kui Cai Tow‐Chong Chong Stephen R. Elliott Tae‐Hoon Lee Desmond K. Loke |
| author_facet | Kian‐Guan Lim Shao‐Xiang Go Chun‐Chia Tan Yu Jiang Kui Cai Tow‐Chong Chong Stephen R. Elliott Tae‐Hoon Lee Desmond K. Loke |
| author_sort | Kian‐Guan Lim |
| collection | DOAJ |
| description | Abstract Memristive hardware with reconfigurable conductance levels are leading candidates for achieving artificial neural networks (ANNs). However, owing to difficulties in device character design and circuit combination, the ability to perform complicated online‐learning tasks on a memristive network is not well understood. Here, tandem (T) material states are harnessed in a phase‐change memory (PCM) element, i.e., the primed‐amorphous state and the partial‐crystallized state, by utilizing an impetus‐and‐consequent pair pulse through a large degree of configurational ordering, and illustrate the development of an integrated system for achieving in‐memory computing and neural networks (NNs). A correct classification of 96.1% of 10,000 separate test images from the conventional Modified‐National‐Institute‐of‐Standards‐and‐Technology (MNIST) database in the tandem neural‐network (T‐NN) model is achieved, as well as image recognition for 28×28‐pixel pictures. The T‐NN configuration exhibits an in situ learning, with 50% of the elements stuck in the low‐conductance state, and at the same time, maintains an identification accuracy of ≈90%. The structural origin of the large degree of configurational‐ordering‐enhanced improvement in the extent of the conductance uniformity in the T‐based memristive element is revealed by theoretical studies. This work opens the door for attaining a widely relevant hardware system capable of performing artificial intelligence tasks with a large power‐time efficacy. |
| first_indexed | 2024-04-25T01:57:19Z |
| format | Article |
| id | doaj.art-4cd0785974f34a4c991d68526e6d8b76 |
| institution | Directory Open Access Journal |
| issn | 2751-1200 |
| language | English |
| last_indexed | 2024-04-25T01:57:19Z |
| publishDate | 2024-03-01 |
| publisher | Wiley-VCH |
| record_format | Article |
| series | Advanced Physics Research |
| spelling | doaj.art-4cd0785974f34a4c991d68526e6d8b762024-03-07T15:43:41ZengWiley-VCHAdvanced Physics Research2751-12002024-03-0133n/an/a10.1002/apxr.202300085Toward Memristive Phase‐Change Neural Network with High‐Quality Ultra‐Effective Highly‐Self‐Adjustable Online LearningKian‐Guan Lim0Shao‐Xiang Go1Chun‐Chia Tan2Yu Jiang3Kui Cai4Tow‐Chong Chong5Stephen R. Elliott6Tae‐Hoon Lee7Desmond K. Loke8Department of Science, Mathematics and Technology Singapore University of Technology and Design Singapore 487372 SingaporeDepartment of Science, Mathematics and Technology Singapore University of Technology and Design Singapore 487372 SingaporeDepartment of Science, Mathematics and Technology Singapore University of Technology and Design Singapore 487372 SingaporeDepartment of Science, Mathematics and Technology Singapore University of Technology and Design Singapore 487372 SingaporeDepartment of Science, Mathematics and Technology Singapore University of Technology and Design Singapore 487372 SingaporeDepartment of Science, Mathematics and Technology Singapore University of Technology and Design Singapore 487372 SingaporePhysical and Theoretical Chemistry Laboratory University of Oxford Oxford OX1 3QZ UKSchool of Materials Science and Engineering Kyungpook National University Daegu 41566 Republic of KoreaDepartment of Science, Mathematics and Technology Singapore University of Technology and Design Singapore 487372 SingaporeAbstract Memristive hardware with reconfigurable conductance levels are leading candidates for achieving artificial neural networks (ANNs). However, owing to difficulties in device character design and circuit combination, the ability to perform complicated online‐learning tasks on a memristive network is not well understood. Here, tandem (T) material states are harnessed in a phase‐change memory (PCM) element, i.e., the primed‐amorphous state and the partial‐crystallized state, by utilizing an impetus‐and‐consequent pair pulse through a large degree of configurational ordering, and illustrate the development of an integrated system for achieving in‐memory computing and neural networks (NNs). A correct classification of 96.1% of 10,000 separate test images from the conventional Modified‐National‐Institute‐of‐Standards‐and‐Technology (MNIST) database in the tandem neural‐network (T‐NN) model is achieved, as well as image recognition for 28×28‐pixel pictures. The T‐NN configuration exhibits an in situ learning, with 50% of the elements stuck in the low‐conductance state, and at the same time, maintains an identification accuracy of ≈90%. The structural origin of the large degree of configurational‐ordering‐enhanced improvement in the extent of the conductance uniformity in the T‐based memristive element is revealed by theoretical studies. This work opens the door for attaining a widely relevant hardware system capable of performing artificial intelligence tasks with a large power‐time efficacy.https://doi.org/10.1002/apxr.202300085artificial neural networkimage classificationin situ online learningmemristive devicesphase‐change‐memory materials |
| spellingShingle | Kian‐Guan Lim Shao‐Xiang Go Chun‐Chia Tan Yu Jiang Kui Cai Tow‐Chong Chong Stephen R. Elliott Tae‐Hoon Lee Desmond K. Loke Toward Memristive Phase‐Change Neural Network with High‐Quality Ultra‐Effective Highly‐Self‐Adjustable Online Learning Advanced Physics Research artificial neural network image classification in situ online learning memristive devices phase‐change‐memory materials |
| title | Toward Memristive Phase‐Change Neural Network with High‐Quality Ultra‐Effective Highly‐Self‐Adjustable Online Learning |
| title_full | Toward Memristive Phase‐Change Neural Network with High‐Quality Ultra‐Effective Highly‐Self‐Adjustable Online Learning |
| title_fullStr | Toward Memristive Phase‐Change Neural Network with High‐Quality Ultra‐Effective Highly‐Self‐Adjustable Online Learning |
| title_full_unstemmed | Toward Memristive Phase‐Change Neural Network with High‐Quality Ultra‐Effective Highly‐Self‐Adjustable Online Learning |
| title_short | Toward Memristive Phase‐Change Neural Network with High‐Quality Ultra‐Effective Highly‐Self‐Adjustable Online Learning |
| title_sort | toward memristive phase change neural network with high quality ultra effective highly self adjustable online learning |
| topic | artificial neural network image classification in situ online learning memristive devices phase‐change‐memory materials |
| url | https://doi.org/10.1002/apxr.202300085 |
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