Evaluating Four FPGA-accelerated Space Use Cases based on Neural Network Algorithms for On-board Inference
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. Vitis AI achieves up to 34.16$\times$ higher inference rate than the embedded ARM CPU baseline, while custom HLS designs reach up to 5.4$\times$ speedup and add support for operators (e.g., sigmoids, 3D layers) absent in the DPU. For these implementations, measured MPSoC infe...
| Authors | Pedro Antunes, Muhammad Ihsan Al Hafiz, Jonah Ekelund et al. |
|---|---|
| Primary category | cs.AR |
| Published | 14 March 2026 |
| Last updated at source | 14 March 2026 |
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