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 communicati...

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Bibliographic Details
Published in:IEEE wireless communications 2024-12, Vol.31 (6), p.78-84
Main Authors: Lagunas, Eva, Ortiz, Flor G., Eappen, Geoffrey, Daoud, Saed, Martins, Wallace A., Querol, Jorge, Chatzinotas, Symeon, Skatchkovsky, Nicolas, Rajendran, Bipin, Simeone, Osvaldo
Format: Article
Language:English
Subjects:
AI accelerators
Artificial intelligence
Artificial neural networks
Beamforming
Hardware
Neuromorphic computing
Neuromorphics
Payloads
Performance evaluation
Power management
Program processors
Satellite communications
Satellites
Spiking neural networks
Training
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Summary: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.
ISSN:1536-1284
1558-0687
DOI:10.1109/MWC.016.2300560