Performance Evaluation of Neuromorphic Hardware for Onboard Satellite Communication Applications
arxiv(2024)
摘要
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, NP represents an interesting opportunity
for implementing AI tasks on board power-limited 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 compare 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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