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...