Activity-difference training of deep neural networks using memristor crossbars
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445922 · Cited by 6,988 · see citing works
Hopfield, J. J. (1984) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.81.10.3088 · Cited by 6,918 · cited 2× · see citing works
“This layout enables a symmetric and zero-diagonal Hopfield weight matrix, which are required conditions for energy minimization, without which the system may produce non-monotonic dynamics 41,42 .”
BackgroundA learning algorithm for Boltzmann machines
Ackley, D. H., Hinton, G. E., & Sejnowski, T. J. (1985) · Cognitive Science · doi:10.1207/s15516709cog0901_7 · Cited by 3,402 · see citing works
Multivariate stochastic approximation using a simultaneous perturbation gradient approximation
Spall, J. C. (1992) · IEEE Transactions on Automatic Control · doi:10.1109/9.119632 · Cited by 2,283 · see citing works
Equivalent-accuracy accelerated neural-network training using analogue memory
Ambrogio, S., Narayanan, P., Tsai, H., Shelby, R. M., Boybat, I., di Nolfo, C., Sidler, S., Giordano, M., Bodini, M., Farinha, N. C. P., et al. (2018) · Nature · doi:10.1038/s41586-018-0180-5 · Cited by 1,151 · see citing works
Efficient and self-adaptive in-situ learning in multilayer memristor neural networks
Li, C., Belkin, D., Li, Y., Yan, P., Hu, M., Ge, N., Jiang, H., Montgomery, E., Lin, P., Wang, Z., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-04484-2 · Cited by 931 · see citing works
Self-citationIn situ training of feed-forward and recurrent convolutional memristor networks
Wang, Z., Li, C., Lin, P., Rao, M., Nie, Y., Song, W., Qiu, Q., Li, Y., Yan, P., Strachan, J. P., et al. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0089-1 · Cited by 307 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Lee, Y., Rhee, H., Kim, G., Cheong, W. H., Kim, D. H., Song, H., Kay, S. N., Lee, J., & Kim, K. M. (2025) · Nature Communications · doi:10.1038/s41467-025-59589-2 · Cited by 19 · see citing works
- Authors
- Yi, S.-I., Kendall, J. D., Williams, R. S., & Kumar, S.
- Affiliations
- Texas A&M University (US)
- Sandia National Laboratories California (US)
- Corresponding author
- Kumar, Suhas
- Venue
- Nature Electronics
- Published
- 2022-11-21
- ISSN
- 2520-1131 (ISSN-L)
- OpenAlex
- W4309760588
- Cited by
- 75
- References
- 8
- Citations by year
- 2026: 7 · 2025: 21 · 2024: 33 · 2023: 14
- FWCI
- 4.59
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Nature Electronics (publishedVersion)
- Funders
- U.S. Department of Energy (DE-NA0003525)
- United States Department of Defense | United States Air Force | AFMC | Air Force Office of Scientific Research (AFOSR-FA9550-19-0213)
- Texas A and M University
- 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 › Engineering › Electrical and Electronic Engineering › Ferroelectric and Negative Capacitance Devices
- Keywords
- memristor crossbar, memristor arrays, deep neural network training, neuromorphic hardware, in-memory computing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
