Energy-Aware Satellite-Ground Co-Inference via Layer-Wise Processing Schedule Optimization

Recent advancements in Low Earth Orbit (LEO) satellites are facilitating the provision of Deep Neural Networks (DNNs)-inherent services to achieve ubiquitous coverage via satellite computing. However, the computational demands and energy consumption of DNN models pose significant challenges for satellite computing with limited power and computation resources. Based on the hierarchical characteristics of DNN models, we propose a satellite-ground co-inference strategy that executing certain layers on satellites and the remaining layers on ground servers. However, identifying the optimal layers for in-orbit processing with latency constraints is challenging due to the uncertain energy consumption across diverse models. To explore the correlation between energy consumption and layer types, we conduct comprehensive measurements on a hardware device commonly found in commercial LEO satellites and develop a layer-based energy consumption prediction model. Then, we formulate an optimization problem of minimizing the energy consumption on the satellite within the latency constraint as an integer nonlinear programming problem. Solving this problem is difficult due to combinatorial explosion in the discrete solution space. To address this, we propose an improved algorithm based on genetic algorithms. Using configurations from a real satellite, we conduct simulation experiments, concluding that our algorithm significantly improves energy savings by an average of 27 ×.