Onboard Processing in Satellite Communications Using AI Accelerators

Author:

Ortiz Flor1ORCID,Monzon Baeza Victor1ORCID,Garces-Socarras Luis M.1ORCID,Vásquez-Peralvo Juan A.1ORCID,Gonzalez Jorge L.1ORCID,Fontanesi Gianluca1ORCID,Lagunas Eva1ORCID,Querol Jorge1ORCID,Chatzinotas Symeon1ORCID

Affiliation:

1. Interdisciplinary Centre for Security Reliability and Trust (SnT), University of Luxembourg, L-1855 Luxembourg, Luxembourg

Abstract

Satellite communication (SatCom) systems operations centers currently require high human intervention, which leads to increased operational expenditure (OPEX) and implicit latency in human action that causes degradation in the quality of service (QoS). Consequently, new SatCom systems leverage artificial intelligence and machine learning (AI/ML) to provide higher levels of autonomy and control. Onboard processing for advanced AI/ML algorithms, especially deep learning algorithms, requires an improvement of several magnitudes in computing power compared to what is available with legacy, radiation-tolerant, space-grade processors in space vehicles today. The next generation of onboard AI/ML space processors will likely include a diverse landscape of heterogeneous systems. This manuscript identifies the key requirements for onboard AI/ML processing, defines a reference architecture, evaluates different use case scenarios, and assesses the hardware landscape for current and next-generation space AI processors.

Funder

European Space Agency

Luxembourg National Research Fund

Publisher

MDPI AG

Subject

Aerospace Engineering

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