Towards the Use of Artificial Intelligence on the Edge in Space Systems: Challenges and Opportunities
Gianluca Furano, Gabriele Meoni, Aubrey Dunne, David Moloney, Véronique Ferlet-Cavrois, Antonis Tavoularis, Jonathan Byrne, Léonie Buckley, Mihalis Psarakis, Kay‐Obbe Voss, Luca Fanucci
Abstract
The market for remote sensing space-based applications is fundamentally limited by up- and downlink bandwidth and onboard compute capability for space data handling systems. This article details how the compute capability on these platforms can be vastly increased by leveraging emerging commercial off-the-shelf (COTS) system-on-chip (SoC) technologies. The orders of magnitude increase in processing power can then be applied to consuming data at source rather than on the ground allowing the deployment of value-added applications in space, which consume a tiny fraction of the downlink bandwidth that would be otherwise required. The proposed solution has the potential to revolutionize Earth observation (EO) and other remote sensing applications, reducing the time and cost to deploy new added value services to space by a great extent compared with the state of the art. This article also reports the first results in radiation tolerance and power/performance of these COTS SoCs for space-based applications and maps the trajectory toward low Earth orbit trials and the complete life-cycle for space-based artificial intelligence classifiers on orbital platforms and spacecraft.