In recent years, Low Earth Orbit (LEO) satellites have witnessed rapid development, with inference based on Deep Neural Network (DNN) models emerging as the prevailing technology for remote sensing satellite image recognition. However, the substantial computation capability and energy demands of DNN models, coupled with the instability of the satellite-ground link, pose significant challenges, burdening satellites with limited power intake and hindering the timely completion of tasks. Existing approaches, such as transmitting all images to the ground for processing or executing DNN models on the satellite, is unable to effectively address this issue. By exploiting the internal hierarchical structure of DNNs and treating each layer as an independent subtask, we propose a satellite-ground collaborative computation partial offloading approach to address this challenge. We formulate the problem of minimizing the inference task execution time and onboard energy consumption through offloading as an integer linear programming (ILP) model. The complexity in solving the problem arises from the combinatorial explosion in the discrete solution space. To address this, we have designed an improved optimization algorithm based on branch and bound. Simulation results illustrate that, compared to the existing approaches, our algorithm improve the performance by 10%-18%

翻译：近年来，低地球轨道（LEO）卫星发展迅速，基于深度神经网络（DNN）模型的推理已成为遥感卫星图像识别的主流技术。然而，DNN模型对计算能力和能量的巨大需求，加之星地链路的不稳定性，给有限能源供给的卫星带来严峻挑战，阻碍了任务的及时完成。现有方法（如将所有图像传输至地面处理或在卫星上直接执行DNN模型）均无法有效解决此问题。通过利用DNN的内部层次结构，将每一层视为独立子任务，本文提出一种星地协同计算的部分卸载方法。我们将通过卸载操作最小化推理任务执行时间与星上能耗的问题建模为整数线性规划（ILP）模型。该问题的求解复杂性源于离散解空间的组合爆炸。为此，我们基于分支定界法设计了一种改进优化算法。仿真结果表明，与现有方法相比，本算法性能提升10%-18%。