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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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Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892
Self-citationStrukov, D. B., Snider, G. S., Stewart, D. R., & Williams, R. S. (2008) · Nature · doi:10.1038/nature06932 · Cited by 11,828
Self-citationSpeech recognition with deep recurrent neural networks
Graves, A., Mohamed, A.-R., & Hinton, G. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6638947 · Cited by 8,893
Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
Shi, X., Chen, Z., Wang, H., Yeung, D.-Y., Wong, W.-K., & Woo, W.-C. (2015) · arXiv · doi:10.48550/arxiv.1506.04214 · Cited by 7,709
Receptive fields and functional architecture of monkey striate cortex
Hubel, D. H., & Wiesel, T. N. (1968) · The Journal of Physiology · doi:10.1113/jphysiol.1968.sp008455 · Cited by 6,685
Backpropagation through time: what it does and how to do it
Werbos, P. J. (1990) · Proceedings of the IEEE · doi:10.1109/5.58337 · Cited by 4,879
In-Datacenter Performance Analysis of a Tensor Processing Unit
Jouppi, N. P., Young, C., Patil, N., Patterson, D., Agrawal, G., Bajwa, R., Bates, S., Bhatia, S., Boden, N., Borchers, A., et al. (2017) · Proceedings of the 44th Annual International Symposium on Computer Architecture · doi:10.1145/3079856.3080246 · Cited by 4,537
Research works printed but not linked: 2
- Simonyan, K. & Zisserman, A. Very deep convolutional networks for large-scale image recognition. Preprint at https://arxiv.org/abs/1409.1556 (2014). · arXiv
- Tieleman, T. & Hinton, G. Lecture 6.5-rmsprop: divide the gradient by a running average of its recent magnitude. COURSERA: Neural networks for machine learning 4, 26–31 (2012).
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Overview of Memristor-Based Neural Network Design and Applications
Ye, L., Gao, Z., Fu, J., Ren, W., Yang, C., Wen, J., Wan, X., Ren, Q., Gu, S., Liu, X., et al. (2022) · Frontiers in Physics · doi:10.3389/fphy.2022.839243 · Cited by 40
From memristive devices to neuromorphic systems
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Self-citationTime Complexity of In-Memory Matrix-Vector Multiplication
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Zheng, Y., Wu, R., Ren, Y., Bao, R., Liu, J., Ma, Y., Wang, M., & Cheng, Y. (2024) · Laser & Photonics Review · doi:10.1002/lpor.202400565 · Cited by 38
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Training neural networks with end-to-end optical backpropagation
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Wang, Z., Zheng, Q., Kang, J., Yu, Z., Zhong, G., Ling, Y., Bao, L., Bao, S., Bai, G., Zheng, S., et al. (2020) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2020.3014566 · Cited by 32
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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