  Satellite computing has emerged as a promising technology for next-generation
wireless networks. This innovative technology provides data processing
capabilities, which facilitates the widespread implementation of artificial
intelligence (AI)-based applications, especially for image processing tasks
involving deep neural network (DNN). With the limited computing resources of an
individual satellite, independently handling DNN tasks generated by diverse
user equipments (UEs) becomes a significant challenge. One viable solution is
dividing a DNN task into multiple subtasks and subsequently distributing them
across multiple satellites for collaborative computing. However, it is
challenging to partition DNN appropriately and allocate subtasks into suitable
satellites while ensuring load balancing. To this end, we propose a
collaborative satellite computing system designed to improve task processing
efficiency in satellite networks. Based on this system, a workload-balanced
adaptive task splitting scheme is developed to equitably distribute the
workload of DNN slices for collaborative inference, consequently enhancing the
utilization of satellite computing resources. Additionally, a self-adaptive
task offloading scheme based on a genetic algorithm (GA) is introduced to
determine optimal offloading decisions within dynamic network environments. The
numerical results illustrate that our proposal can outperform comparable
methods in terms of task completion rate, delay, and resource utilization.
