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In situ training of feed-forward and recurrent convolutional memristor networks
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“Although our device is not ideal yet, the estimated accuracy is comparable to other hardware‐based simulation performances ranging from 70% to 94%.[ 41 , 42 , 43 , 44 , 45 ]”
From Retention Secured Nonlinear and Self-Rectifying Analog Charge Trap Memristor for Energy-Efficient Neuromorphic Hardware (2023) · cites this paper as [41]
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Self-citation
Metadata
- Authors
- Wang, Z., Li, C., Lin, P., Rao, M., Nie, Y., Song, W., Qiu, Q., Li, Y., Yan, P., Strachan, J. P., Ge, N., McDonald, N., Wu, Q., Hu, M., Wu, H., Williams, R. S., Xia, Q., & Yang, J. J.
- Affiliations
- University of Massachusetts Amherst (US)
- Hewlett-Packard (United States) (US)
- Syracuse University (US)
- United States Air Force Research Laboratory (US)
- Binghamton University (US)
- Tsinghua University (CN)
- Texas A&M University (US)
- Corresponding author
- Xia, Qiangfei
- Yang, J. Joshua
- ORCID
- Venue
- Nature Machine Intelligence
- Published
- 2019-09-09
- Volume
- 1
- Issue
- 9
- Pages
- 434-442
- ISSN
- 2522-5839 (ISSN-L)
- OpenAlex
- W2972686555
- Cited by
- 307
- References
- 53
- Citations by year
- 2026: 16 · 2025: 39 · 2024: 46 · 2023: 59 · 2022: 50 · 2021: 59 · 2020: 35 · 2019: 3
- FWCI
- 13.21
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Nature Machine Intelligence (publishedVersion)
- Funders
- National Natural Science Foundation of China (61674092)
- United States Department of Defense | United States Air Force | AFMC | Air Force Research Laboratory (FA8750-18-2-0122)
- United States Department of Defense | Defense Advanced Research Projects Agency (D17PC00304)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Reservoir Computing
- Keywords
- memristor neural networks, in situ training, convolutional neural networks, recurrent neural networks, convolutional long short-term memory, parameter sharing, energy-efficient hardware, edge AI, MNIST classification, parameter reduction
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