Performance Evaluation of Neuromorphic Hardware for Onboard Satellite Communication Applications

Spiking neural networks (SNNs) implemented on neuromorphic processors (NPs) can enhance the energy efficiency of deployments of artificial intelligence (AI) for specific workloads. As such, NPs represent an interesting opportunity for implementing AI tasks power-limited onboard satellite communication spacecraft. In this article, we disseminate the findings of a recently completed study which targeted the comparison, in terms of performance and power-consumption, of different satellite communication use cases implemented on standard AI accelerators and on NPs. In particular, the article describes three prominent use cases, namely payload resource optimization, onboard interference detection and classification, and dynamic receive beamforming; and compares the performance of conventional convolutional neural networks (CNNs) implemented on Xilinx's VCK5000 Versal development card, and SNNs on Intel's neuromorphic chip Loihi 2.

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Publication Details

Journal
IEEE Wireless Communications
Published
2024-12-01
DOI
https://doi.org/10.1109/mwc.016.2300560
Citations
6
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
1.25

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article

Performance Evaluation of Neuromorphic Hardware for Onboard Satellite Communication Applications

Symeon Chatzinotas, Osvaldo Simeone, Eva Lagunas, Saed Daoud et al.
6 citations
IEEE Wireless Communications
Advanced Memory and Neural Computing
1.25
article

Performance Evaluation of Neuromorphic Hardware for Onboard Satellite Communication Applications

Symeon Chatzinotas, Osvaldo Simeone, Eva Lagunas, Saed Daoud, Nicolas Skatchkovsky, Jorge Querol, Bipin Rajendran, Wallace A. Martins, Geoffrey Eappen, Flor G. Ortiz
article en
6 citations

Abstract

Spiking neural networks (SNNs) implemented on neuromorphic processors (NPs) can enhance the energy efficiency of deployments of artificial intelligence (AI) for specific workloads. As such, NPs represent an interesting opportunity for implementing AI tasks power-limited onboard satellite communication spacecraft. In this article, we disseminate the findings of a recently completed study which targeted the comparison, in terms of performance and power-consumption, of different satellite communication use cases implemented on standard AI accelerators and on NPs. In particular, the article describes three prominent use cases, namely payload resource optimization, onboard interference detection and classification, and dynamic receive beamforming; and compares the performance of conventional convolutional neural networks (CNNs) implemented on Xilinx's VCK5000 Versal development card, and SNNs on Intel's neuromorphic chip Loihi 2.

IEEE Wireless CommunicationsVol. 31(6)
École Nationale de l’Aviation Civile (FR), King's College London (GB), University of Luxembourg (LU), Institut Superieur de l'Aeronautique et de l'Espace (ISAE-SUPAERO) (FR), The Francis Crick Institute (GB)
European Space Agency, Fonds National de la Recherche Luxembourg, Engineering and Physical Sciences Research Council
Openalex Percentile: Top 19%
Advanced Memory and Neural Computing
1.25
DOISourceOpenAlex
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