Onboard Processing in Satellite Communications Using AI Accelerators
Flor Ortíz, Víctor Monzón Baeza, Luis Manuel Garcés-Socarrás, Juan A. Vásquez-Peralvo, Jorge Luis González Ríos, Gianluca Fontanesi, Eva Lagunas, Jorge Querol, Symeon Chatzinotas
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.