DNN Tasks Offloading and Bandwidth Optimization for Satellite-Terrestrial Collaborative Intelligence

Haochun Lei, Yuben Qu, Lei Zhang, Lingyuan Zhao, Yu Li, Jian Cheng, Guangxia Li, Qihui Wu

Abstract: Deep Neural Networks (DNNs) are now widely used in Low Earth Orbit (LEO) satellites, such as in remote sensing and environmental monitoring. DNN tasks are generally resource-intensive, while the resources of LEO satellites including computation and storage resources are usually limited, which implies directly running high-precision and complex DNNs on them is extremely challenging. A promising way is leveraging the layered structure of DNNs and executing DNN tasks collaboratively between satellites and ground, i.e., satellite-terrestrial collaborative inference. However, most existing works about satellite- terrestrial collaborative inference mainly focus on the optimization of DNN offloading strategy in terms of latency and energy minimization, without considering how to minimize the highly precious satellite communication resources in the collaboration. In this paper, we study how to jointly optimize the offloading decision and satellites' communication bandwidth, to achieve the minimization of weighted sum of latency, energy consumption, and communication bandwidth consumption. The aforementioned problem is a Mixed Integer Nonlinear Programming (MINLP) problem and hard to resolve. We design an alternating optimization algorithm combining branch-and-bound and gradient descent methods (AO-SA) to obtain an efficient solution. Extensive simulations validate the efficiency of the proposed algorithm: compared to existing satellite-terrestrial offloading algorithms, it improves the performance in terms of latency and energy consumption by up to 31 %, while saving the bandwidth resource of satellites by 28 % on average.

External IDs:dblp:conf/msn/LeiQZZLCL024