Fast model inference and training on-board of Satellites

• URL: http://arxiv.org/abs/2307.08700v1
• Date: Mon, 17 Jul 2023 17:59:09 GMT
• Title: Fast model inference and training on-board of Satellites
• Authors: V\'it R\r{u}\v{z}i\v{c}ka, Gonzalo Mateo-Garc\'ia, Chris Bridges, Chris Brunskill, Cormac Purcell, Nicolas Long\'ep\'e, Andrew Markham
• Abstract summary: This study deploys a lightweight foundational model called RaVAEn on D-Orbit's ION SCV004 satellite. RaVAEn generates compressed latent vectors from small image tiles, enabling several downstream tasks.
• Score: 16.93335252280199
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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