Opportunities for neuromorphic computing algorithms and applications
Open accessAbstract
Neuromorphic computing technologies will be important for the future of computing, but much of the work in neuromorphic computing has focused on hardware development. Here, we review recent results in neuromorphic computing algorithms and applications. We highlight characteristics of neuromorphic computing technologies that make them attractive for the future of computing and we discuss opportunities for future development of algorithms and applications on these systems.
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The correlation between AI advances and brain-inspired hardware publications suggests that demand for more efficient hardware capable of executing large-scale neural network models has increased.
“First, it suggests that with the advances in AI, and with more and more applications using intelligent data processing [149], the demand for more efficient hardware capable of executing large-scale neural network models has increased.”
From Brain-inspired computing systems: a systematic literature review (2024) · cites this paper as [149]
Cites
Nanoscale Memristor Device as Synapse in Neuromorphic Systems
Jo, S. H., Chang, T., Ebong, I., Bhadviya, B. B., Mazumder, P., & Lu, W. (2010) · Nano Letters · doi:10.1021/nl904092h · Cited by 4,174 · cited 1× · see citing works
“One popular approach in the literature is using memristors as the fundamental device to have resistive memory to collocate processing and memory 31,32 , but other types of devices have also been used to implement neuromorphic computers, including optoelectronic devices 10 .”
MethodNeuromorphic electronic systems
Mead, C. (1990) · Proceedings of the IEEE · doi:10.1109/5.58356 · Cited by 2,368 · see citing works
Nessler, B., Pfeiffer, M., Buesing, L., & Maass, W. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003037 · Cited by 281 · see citing works
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Rech, P. (2024) · IEEE Transactions on Nuclear Science · doi:10.1109/tns.2024.3349956 · Cited by 75 · see citing works
Brain-inspired computing systems: a systematic literature review
Zolfagharinejad, M., Alegre-Ibarra, U., Chen, T., Kinge, S., & van der Wiel, W. G. (2024) · The European Physical Journal B · doi:10.1140/epjb/s10051-024-00703-6 · Cited by 39 · see citing works
Kim, M., Kim, D.-E., Wang, Y., Lee, D., Lim, D.-H., Choi, H., Kymissis, I., Yang, J. J., Suh, J., Lee, H.-S., et al. (2024) · Applied Materials Today · doi:10.1016/j.apmt.2024.102204 · Cited by 24 · see citing works
- Authors
- Schuman, C. D., Kulkarni, S. R., Parsa, M., Mitchell, J. P., Date, P., & Kay, B.
- Affiliations
- Oak Ridge National Laboratory (US)
- Corresponding author
- Schuman, Catherine D.
- ORCID
- Venue
- Nature Computational Science
- Published
- 2022-01-31
- Volume
- 2
- Issue
- 1
- Pages
- 10-19
- ISSN
- 2662-8457 (ISSN-L)
- OpenAlex
- W4210357113
- Cited by
- 1,117
- References
- 3
- Citations by year
- 2026: 219 · 2025: 361 · 2024: 313 · 2023: 178 · 2022: 46
- FWCI
- 14.63
- Type
- journal article
- Language
- en
- Update notices
- correction (2022-03-11)
- Open access
- bronze
- doi.org
- Licence
- Locations
- Nature Computational Science (publishedVersion)
- PubMed (publishedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Funders
- U.S. Department of Energy
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Reservoir Computing
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Keywords
- neuromorphic computing, neuromorphic algorithms, neuromorphic hardware, artificial neural networks
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