A Qualifiable GPU Sharing Approach for AI Workloads in Critical Systems

Graphics Processing Units (GPUs) are increasingly considered for adoption in safety-critical systems and especially space, in order to enable high performance on-board processing, particularly needed for Artificial Intelligence (AI) acceleration. While use of GPUs has been demonstrated in qualifiable real-time operating systems, GPU sharing has not been explored so far. In this work we describe we describe the design, implementation and evaluation of such GPU sharing approach, which is portable among different operating systems. The proposed solution has been implemented and demonstrated with two real-world AI applications for space missions. The results demonstrate significant performance improvements and efficient AI utilization. By addressing the timing constraints and enabling GPU utilization, this approach opens up new possibilities for efficient and reliable AI-powered space systems.