Abstract: With the improvement of on-orbit computing capability of low-orbit satellites and the surge in demand for artificial intelligence services such as target detection and satellite reconnaissance, deep neural network (DNN) has become the first choice for realizing intelligent services with its unique model structure and efficient learning. In order to solve the problems of limited resources and difficult communication caused by the satellite always moving at high speed, small size and isomerization, it has become an inevitable trend to realize edge calculation by low-orbit satellite and distributed task inference by DNN. Firstly, directed acyclic graph (DAG) is used to explore the structure of DNN model, and the distributed DNN inference problem in low orbit satellite network is studied. Then, a quantum evolution algorithm (QEA) based on excitation function and processing delay is designed to realize the optimal decision of sampling rate setting and task offloading. Finally, the simulation results show that the performance of quantum evolution algorithm based on excitation function and processing delay is better than that of traditional methods.

Key words: Distributed DNN Inference, LEO Satellite, Task Offloading