  Pose estimation of an uncooperative space resident object is a key asset
towards autonomy in close proximity operations. In this context monocular
cameras are a valuable solution because of their low system requirements.
However, the associated image processing algorithms are either too
computationally expensive for real time on-board implementation, or not enough
accurate. In this paper we propose a pose estimation software exploiting neural
network architectures which can be scaled to different accuracy-latency
trade-offs. We designed our pipeline to be compatible with Edge Tensor
Processing Units to show how low power machine learning accelerators could
enable Artificial Intelligence exploitation in space. The neural networks were
tested both on the benchmark Spacecraft Pose Estimation Dataset, and on the
purposely developed Cosmo Photorealistic Dataset, which depicts a COSMO-SkyMed
satellite in a variety of random poses and steerable solar panels orientations.
The lightest version of our architecture achieves state-of-the-art accuracy on
both datasets but at a fraction of networks complexity, running at 7.7 frames
per second on a Coral Dev Board Mini consuming just 2.2W.
