Real-Time Spacecraft Pose Estimation Using Mixed-Precision Quantized Neural Network on COTS Reconfigurable MPSoC
<|reference_start|>Real-Time Spacecraft Pose Estimation Using Mixed-Precision Quantized Neural Network on COTS Reconfigurable MPSoC: This article presents a pioneering approach to real-time spacecraft pose estimation, utilizing a mixed-precision quantized neural network implemented on the FPGA components of a commercially available Xilinx MPSoC, renowned for its suitability in space applications. Our co-design methodology includes a novel evaluation technique for assessing the layer-wise neural network sensitivity to quantization, facilitating an optimal balance between accuracy, latency, and FPGA resource utilization. Utilizing the FINN library, we developed a bespoke FPGA dataflow accelerator that integrates on-chip weights and activation functions to minimize latency and energy consumption. Our implementation is 7.7 times faster and 19.5 times more energy-efficient than the best-reported values in the existing spacecraft pose estimation literature. Furthermore, our contribution includes the first real-time, open-source implementation of such algorithms, marking a significant advancement in making efficient spacecraft pose estimation algorithms widely accessible. The source code is available at https://github.com/possoj/FPGA-SpacePose.<|reference_end|>
@article{posso2024real-time,
    title={Real-Time Spacecraft Pose Estimation Using Mixed-Precision Quantized
  Neural Network on COTS Reconfigurable MPSoC},
    author={Julien Posso, Guy Bois, Yvon Savaria},
    journal={arXiv preprint arXiv:2407.06170},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.06170},
    primaryClass={cs.CV cs.AI cs.AR}
}