AI RESEARCH
Evaluating Four FPGA-accelerated Space Use Cases based on Neural Network Algorithms for On-board Inference
arXiv CS.LG
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ArXi:2603.14091v1 Announce Type: cross Space missions increasingly deploy high-fidelity sensors that produce data volumes exceeding onboard buffering and downlink capacity. This work evaluates FPGA acceleration of neural networks (NNs) across four space use cases on the AMD ZCU104 board. We use Vitis AI (AMD DPU) and Vitis HLS to implement inference, quantify throughput and energy, and expose toolchain and architectural constraints relevant to deployment.