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Simon Vellas

Publications and source records attributed to Simon Vellas.

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Towards Employing FPGA and ASIP Acceleration to Enable Onboard AI/ML in Space Applications

The success of AI/ML in terrestrial applications and the commercialization of space are now paving the way for the advent of AI/ML in satellites. However, the limited processing power of classical onboard processors drives the community towards extending the use of FPGAs in space with both rad-hard and Commercial-Off-The-Shelf devices. The increased performance of FPGAs can be complemented with VPU or TPU ASIP co-processors to further facilitate high-level AI development and in-flight reconfiguration. Thus, selecting the most suitable devices and designing the most efficient avionics architecture becomes crucial for the success of novel space missions. The current work presents industrial trends, comparative studies with in-house benchmarking, as well as architectural designs utilizing FPGAs and AI accelerators towards enabling AI/ML in future space missions.

cs.AR

Evaluation of Resource-Efficient Crater Detectors on Embedded Systems

Real-time analysis of Martian craters is crucial for mission-critical operations, including safe landings and geological exploration. This work leverages the latest breakthroughs for on-the-edge crater detection aboard spacecraft. We rigorously benchmark several YOLO networks using a Mars craters dataset, analyzing their performance on embedded systems with a focus on optimization for low-power devices. We optimize this process for a new wave of cost-effective, commercial-off-the-shelf-based smaller satellites. Implementations on diverse platforms, including Google Coral Edge TPU, AMD Versal SoC VCK190, Nvidia Jetson Nano and Jetson AGX Orin, undergo a detailed trade-off analysis. Our findings identify optimal network-device pairings, enhancing the feasibility of crater detection on resource-constrained hardware and setting a new precedent for efficient and resilient extraterrestrial imaging. Code at: https://github.com/billpsomas/mars_crater_detection.

cs.CV