mlsanityData to 14 Oct 2026

Evaluating Four FPGA-accelerated Space Use Cases based on Neural Network Algorithms for On-board Inference

arXiv 2603.14091 ↗First posted 14 Mar 2026cs.AR cs.LGcited by 0

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Abstract (arXiv)

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× higher inference rate than the embedded ARM CPU baseline, while custom HLS designs reach up to 5.4× speedup and add support for operators (e.g., sigmoids, 3D layers) absent in the DPU. For these implementations, measured MPSoC inference power spans 1.5-6.75 W, reducing energy per inference versus CPU execution in all use cases. These results show that NN FPGA acceleration can enable onboard filtering, compression, and event detection, easing downlink pressure in future missions.

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References · 11

  1. 2016FINN: A Framework for Fast, Scalable Binarized Neural Network Inferencecited by 150
  2. 2020Satellite Communications in the New Space Era: A Survey and Future Challengescited by 36
  3. 2019A Machine Learning Dataset Prepared From the NASA Solar Dynamics Observatory Missioncited by 26
  4. 2023Accurate, Low-latency, Efficient SAR Automatic Target Recognition on FPGAcited by 10
  5. 2019Automated classification of plasma regions using 3D particle energy distributionscited by 6
  6. 2025Efficient FPGA-accelerated Convolutional Neural Networks for Cloud Detection on CubeSatscited by 5
  7. 2024An Energy-Efficient Artefact Detection Accelerator on FPGAs for Hyper-Spectral Satellite Imagerycited by 4
  8. 2023Resource-constrained FPGA Design for Satellite Component Feature Extractioncited by 3
  9. 2024AI in Space for Scientific Missions: Strategies for Minimizing Neural-Network Model Uploadcited by 3
  10. 2025FPGA-Based Neural Network Accelerators for Space Applications: A Surveycited by 2
  11. 2025Towards Employing FPGA and ASIP Acceleration to Enable Onboard AI/ML in Space Applicationscited by 2

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