The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.
Physics for neuromorphic computing
Open accessAbstract
Neuromorphic computing takes inspiration from the brain to create energy-efficient hardware for information processing, capable of highly sophisticated tasks. Systems built with standard electronics achieve gains in speed and energy by mimicking the distributed topology of the brain. Scaling-up such systems and improving their energy usage, speed and performance by several orders of magnitude requires a revolution in hardware. We discuss how including more physics in the algorithms and nanoscale materials used for data processing could have a major impact in the field of neuromorphic computing. We review striking results that leverage physics to enhance the computing capabilities of artificial neural networks, using resistive switching materials, photonics, spintronics and other technologies. We discuss the paths that could lead these approaches to maturity, towards low-power, miniaturized chips that could infer and learn in real time. Neuromorphic computing takes inspiration from the brain to create energy-efficient hardware for information processing, capable of highly sophisticated tasks. Including more physics in the algorithms and nanoscale materials used for computing could have a major impact in this field.
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Cites
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Cited by
Pham, P. V., Bodepudi, S. C., Shehzad, K., Liu, Y., Xu, Y., Yu, B., & Duan, X. (2022) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00735 · Cited by 708
Liu, K., Zhang, T., Dang, B., Bao, L., Xu, L., Cheng, C., Yang, Z., Huang, R., & Yang, Y. (2022) · Nature Electronics · doi:10.1038/s41928-022-00847-2 · Cited by 498
Advances in Magnetics Roadmap on Spin-Wave Computing
Chumak, A. V., Kabos, P., Wu, M., Abert, C., Adelmann, C., Adeyeye, A. O., Akerman, J., Aliev, F. G., Anane, A., Awad, A., et al. (2022) · IEEE Transactions on Magnetics · doi:10.1109/tmag.2022.3149664 · Cited by 477
Emerging dynamic memristors for neuromorphic reservoir computing
Cao, J., Zhang, X., Cheng, H., Qiu, J., Liu, X., Wang, M., & Liu, Q. (2022) · Nanoscale · doi:10.1039/d1nr06680c · Cited by 134
Echo state graph neural networks with analogue random resistive memory arrays
Wang, S., Li, Y., Wang, D., Zhang, W., Chen, X., Dong, D., Wang, S., Zhang, X., Lin, P., Gallicchio, C., et al. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00609-5 · Cited by 84
Metadata
- Authors
- Marković, D., Mizrahi, A., Querlioz, D., & Grollier, J.
- Venue
- Nature Reviews Physics
- Published
- 2020-07-28
- Volume
- 2
- Issue
- 9
- Pages
- 499-510
- ISSN
- 2522-5820 (ISSN-L)
- OpenAlex
- W3010886459
- Cited by
- 1,176
- References
- 45
- FWCI
- 4.16
- Type
- journal article
- Language
- en
- Open access
- green
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