[2307.08700] Fast model inference and training on-board of Satellites
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Computer Science > Artificial Intelligence

arXiv:2307.08700 (cs)
[Submitted on 17 Jul 2023]

Title:Fast model inference and training on-board of Satellites

Authors:Vít Růžička, Gonzalo Mateo-García, Chris Bridges, Chris Brunskill, Cormac Purcell, Nicolas Longépé, Andrew Markham
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Abstract:Artificial intelligence onboard satellites has the potential to reduce data transmission requirements, enable real-time decision-making and collaboration within constellations. This study deploys a lightweight foundational model called RaVAEn on D-Orbit's ION SCV004 satellite. RaVAEn is a variational auto-encoder (VAE) that generates compressed latent vectors from small image tiles, enabling several downstream tasks. In this work we demonstrate the reliable use of RaVAEn onboard a satellite, achieving an encoding time of 0.110s for tiles of a 4.8x4.8 km$^2$ area. In addition, we showcase fast few-shot training onboard a satellite using the latent representation of data. We compare the deployment of the model on the on-board CPU and on the available Myriad vision processing unit (VPU) accelerator. To our knowledge, this work shows for the first time the deployment of a multi-task model on-board a CubeSat and the on-board training of a machine learning model.
Comments: 4 pages, 4 figures, International Geoscience and Remote Sensing Symposium (IGARSS) 2023
Subjects: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2307.08700 [cs.AI]
  (or arXiv:2307.08700v1 [cs.AI] for this version)
  https://doi.org/10.48550/arXiv.2307.08700

Submission history

From: Vít Růžička [view email]
[v1] Mon, 17 Jul 2023 17:59:09 UTC (161 KB)
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