An edge-agent system on a COTS ARM SoC repeatedly completed two fixed FAIR1M detection workflows 20/20 times, with detector time only about 2.5% to 2.9% of full-agent latency.
Fast model inference and training on-board of Satellites
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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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SAT-Edge-Agent: Hardware-in-the-Loop Edge-Agent Orchestration for Onboard Satellite Intelligence
An edge-agent system on a COTS ARM SoC repeatedly completed two fixed FAIR1M detection workflows 20/20 times, with detector time only about 2.5% to 2.9% of full-agent latency.