Integrating AI-based capabilities into satellites improves spacecraft autonomy, but poses considerable obstacles in designing the hardware and software ecosystem.
The orbit-dependent generation of power with solar cells, the limited thermal dissipation and weight present significant challenges in designing a compute platform capable of edge inference, forcing the trade-off between high-performance for complex AI models and strict power/area budget.
Moreover, AI models must share the same resources of traditional algorithm that are executed onboard concurrently with the inference, such as avionics, attitude orbit control, data handling and signal processing.
However, benefits of onboard processing comprise secure and private computation, decreased data uplink/downlink demands, autonomous detection and resolution of anomalies, enabling autonomous spacecraft operation.
Therefore, it is necessary to adapt a hardware-aware codesign approach in designing software components to implement energy-efficient and secure edge inference without decreasing the performance of traditional applications.
The ISOLDE space demonstrator comprises several RISC-V cores and accelerators, and its hardware architecture and software ecosystem are presented, with a particular focus on the interactions of several open-source and open-hardware IPs developed by various academic and industry European partners.