Enabling Space-Grade AI/ML with RISC-V: A Fully European Stack for Autonomous Missions
The convergence of open RISC-V architectures, real-time hypervisors, and efficient embedded AI frameworks marks a transformative moment in critical space system design.
By Pablo Ghiglino (Klepsydra), Jan Reinhard (Sysgo) and Jean-Didier Noir (Sysgo)
EETimes Europe | August 5, 2025
As modern space missions evolve in complexity, the role of software onboard spacecraft is undergoing a dramatic transformation. Spacecraft are no longer limited to basic telemetry and remote control. Today, onboard computing must support autonomous decision-making, intelligent data reduction, and rapid responses to unforeseen conditions—all while operating under strict constraints on power, size, and reliability. Artificial intelligence and machine learning (AI/ML) are the technologies driving this shift, but implementing them in the harsh, resource-constrained environment of space demands a new breed of embedded computing.
At the center of this evolution lies a fully European initiative combining open-source hardware, certified real-time software, and efficient AI frameworks. It’s a future built on RISC-V processors, safety-critical hypervisors, and edge-optimized AI engines—all integrated into a secure and flexible technology stack designed for space. This article explores how this fully European ecosystem is shaping the future of space autonomy.
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