SpaceExit: Enabling Efficient Adaptive Computing in Space with Early Exits

Advances in satellite technology and reduced launch costs have led to a proliferation of Earth observation (EO) satellites in low-Earth orbit (LEO). These satellites generate massive high-resolution imagery, creating a significant downlink bottleneck due to limited satellite-to-ground communication bandwidth. While orbit edge computing (OEC) can reduce data volume, existing static approaches fail to adapt to the varying complexity of satellite imagery, resulting in limited system performance and inefficient resource utilization.

We therefore propose SpaceExit, an integrated system for efficient adaptive computing on satellites. SpaceExit introduces three key components: (1) a geospatial-contextual adaptive detector that leverages both visual semantics and geospatial context to adjust processing complexity for each image, (2) a complexity-driven adaptive task scheduler that partitions images into tiles and allocates inference tasks across onboard devices based on content complexity and device capabilities, and (3) a satellite resource adaptive controller that ensures safe and efficient execution under changing conditions. Evaluations of diverse satellite settings and hardware platforms demonstrate that SpaceExit increases the performance by 5.2%-37.6% compared with the SoTA design.