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arXiv:2610.33061v1 Announce Type: new Abstract: SNNs are attracting interest due to their potential for energy efficiency and event-driven processing. However, neuromorphic hardware is not yet mature enough to enable embedded deployment of complex spiking network architectures. In this work, we investigate a spiking version of the MoveNet architecture tailored for autonomous spacecraft operation, aiming to evaluate the deployability of this complex architecture compared to the non-spiking version for embedded, fully event-based solutions in space applications. Using the RAPTOR dataset, we compare the performance and operational cost of the proposed S-MoveNet-Lightning architecture with its standard version, MoveNet-Lightning. Our results show balanced advantages in computational resources and architectural settings for spiking U-Net-like architectures. An evaluation of the performance and hardware footprint of the proposed Spiking Neural Network (SNN) approach for pose estimation shows that, on average, 2.2 times fewer multiplication and accumulation operations are required compared to the non-spiking solution deployed on FPGA platforms with the AMD Deep Learning Processor Unit (DPU). These results provide valuable insights into the feasibility and potential of SNNs in complex aerospace applications and highlight their role in balancing efficiency and performance, paving the way for event-driven solutions in embedded space applications.
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