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.

该研究提出了一种创新的实时航天器姿态估计方法，利用基于商业可用的Xilinx MPSoC的FPGA组件上实现的混合精度量化神经网络。通过一种新颖的评估技术来评估神经网络在量化上的层级敏感性，实现精度、延迟和FPGA资源利用之间的最佳平衡。利用FINN库，开发了一种定制的FPGA数据流加速器，将芯片上的权重和激活函数集成，以最小化延迟和能源消耗。与现有航天器姿态估计文献中最佳报告值相比，我们的实现速度提高了7.7倍，能效提高了19.5倍。此外，我们的贡献还包括首个实时开源实现的算法，标志着有效的航天器姿态估计算法的重大进展。源代码可在此链接获得。

基于商用重新配置的MPSoC的混合精度量化神经网络的实时航天器姿态估计