Performance Evaluation of Neuromorphic Hardware for Onboard Satellite Communication Applications
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arXiv
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| Auteurs principaux: | , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866917566264377344 |
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| author | Lagunas, Eva Ortiz, Flor Eappen, Geoffrey Daoud, Saed Martins, Wallace Alves Querol, Jorge Chatzinotas, Symeon Skatchkovsky, Nicolas Rajendran, Bipin Simeone, Osvaldo |
| author_facet | Lagunas, Eva Ortiz, Flor Eappen, Geoffrey Daoud, Saed Martins, Wallace Alves Querol, Jorge Chatzinotas, Symeon Skatchkovsky, Nicolas Rajendran, Bipin Simeone, Osvaldo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_06911 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Performance Evaluation of Neuromorphic Hardware for Onboard Satellite Communication Applications Lagunas, Eva Ortiz, Flor Eappen, Geoffrey Daoud, Saed Martins, Wallace Alves Querol, Jorge Chatzinotas, Symeon Skatchkovsky, Nicolas Rajendran, Bipin Simeone, Osvaldo Signal Processing 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. |
| title | Performance Evaluation of Neuromorphic Hardware for Onboard Satellite Communication Applications |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2401.06911 |