Spiking Neural Networks - Part III: Neuromorphic Communications

Nicolas Skatchkovsky, Hyeryung Jang, Osvaldo Simeone

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number9317803
Pages (from-to)1746-1750
Number of pages5
JournalIEEE Communications Letters
Volume25
Issue number6
DOIs
StatePublished - Jun 2021
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Modeling and Simulation
  • Computer Science Applications
  • Electrical and Electronic Engineering

Keywords

  • Neuromorphic computing
  • spiking neural networks (SNNs)

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