The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Spiking Neural Networks—Part III: Neuromorphic Communications
Open accessAbstract
Synergies between wireless communications and artificial intelligence are increasingly motivating research at the intersection of the two fields. On the one hand, the presence of more and more wirelessly connected devices, each with its own data, is driving efforts to export advances in machine learning (ML) from high performance computing facilities, where information is stored and processed in a single location, to distributed, privacy-minded, processing at the end user. On the other hand, ML can address algorithm and model deficits in the optimization of communication protocols. However, implementing ML models for learning and inference on battery-powered devices that are connected via bandwidth-constrained channels remains challenging. This letter explores two ways in which Spiking Neural Networks (SNNs) can help address these open problems. First, we discuss federated learning for the distributed training of SNNs, and then describe the integration of neuromorphic sensing, SNNs, and impulse radio technologies for low-power remote inference.
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Inspired by dynamic spiking neurons in the human brain, the energy consumption and latency of edge AI can be significantly reduced by processing the learning tasks in an event-driven manner.
“Inspired by the dynamic spiking neurons in the human brain, the energy consumption and latency of edge AI can be significantly reduced by processing the learning tasks in an event-driven manner [62], [63].”
From Edge artificial intelligence for 6g: vision, enabling technologies, and applications (2022) · cites this paper as [63]
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Communication-Efficient Learning of Deep Networks from Decentralized Data
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. Y. (2016) · arXiv · doi:10.48550/arxiv.1602.05629 · Cited by 14,215
An Introduction to Deep Learning for the Physical Layer
O'Shea, T., & Hoydis, J. (2017) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2017.2758370 · Cited by 2,980
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Edge artificial intelligence for 6g: vision, enabling technologies, and applications
Letaief, K. B., Shi, Y., Lu, J., & Lu, J. (2022) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2021.3126076 · Cited by 829
Metadata
- Authors
- Skatchkovsky, N., Jang, H., & Simeone, O.
- Affiliations
- King's College London (GB)
- Venue
- IEEE Communications Letters
- Published
- 2021-06
- Volume
- 25
- Issue
- 6
- Pages
- 1746-1750
- ISSN
- 1089-7798 (ISSN-L)
- 1558-2558
- 2373-7891
- OpenAlex
- W3096058467
- Cited by
- 14
- References
- 2
- Citations by year
- 2026: 2 · 2024: 4 · 2023: 3 · 2022: 3 · 2021: 2
- FWCI
- 0.88
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Communications Letters (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Research Portal (King's College London) (submittedVersion)
- arXiv (Cornell University)
- Funders
- European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Programme (725731)
- King’s College London
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Keywords
- spiking neural networks, federated learning, neuromorphic sensing, impulse radio, edge computing, distributed learning, battery-powered devices, communication protocol optimization
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