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FPGA-based Inference Parallelization for Onboard RL-based Routing in Dynamic LEO Satellite Networks
김도형,이현철,원동식,한명훈 한국항공우주학회 2024 International Journal of Aeronautical and Space Sc Vol.25 No.3
This paper addresses the problem of onboard computer application of dynamic low-orbit satellite network routing algorithms. In low-orbit satellite networks, the satellite topology changes in real time, and satellite disconnection occurs frequently. The problem of routing algorithms for low-orbit satellites can be solved by reinforcement learning algorithms. However, the inference process based on deep reinforcement learning models suffers from excessive computation due to the operation of multiple convolutional layers. In this paper, we propose a method to accelerate convolutional layer operations by parallelizing them using heterogeneous processors. This approach is compared to the traditional single-processor-based convolutional operation method, commonly used in dynamic low-orbit satellite network routing algorithms. Our evaluation, conducted on an actual heterogeneous processor-based onboard computer, demonstrates that the proposed method not only matches the accuracy of the conventional single-processor-based approach, but also significantly reduces the execution time.
동적 저궤도 위성 네트워크에서 실시간 라우팅을 위한 FPGA 기반 컨볼루션층 추론 병렬화 기술
김대연,이헌철,원동식,한명훈 한국정보기술학회 2023 한국정보기술학회논문지 Vol.21 No.8
This paper addresses the real-time routing problem in Low Earth Orbit (LEO) satellite networks. Existing routing algorithms have been found to struggle to adapt effectively to dynamic satellite network environments. As such, this study proposes a reinforcement learning-based routing approach and implements it using a dueling deep Q-network model. However, the inference process on satellites faces challenges in meeting real-time requirements due to limited computational capabilities. To resolve this, we propose an approach that accelerates inference speed by parallelizing the convolutional layer's inference process. Experimental results show that our proposed method has reduced the computation time of the convolutional layer by 90.2% and the total algorithm execution time by 29.0% compared to the existing methods.
동적 저궤도 위성 네트워크를 위한 Dueling DQN 기반 라우팅 기법
김도형,이상현,이헌철,원동식,Dohyung Kim,Sanghyeon Lee,Heoncheol Lee,Dongshik Won 대한임베디드공학회 2023 대한임베디드공학회논문지 Vol.18 No.4
This paper deals with a routing algorithm which can find the best communication route to a desired point considering disconnected links in the LEO (low earth orbit) satellite networks. If the LEO satellite networks are dynamic, the number and distribution of the disconnected links are varying, which makes the routing problem challenging. To solve the problem, in this paper, we propose a routing method based on Dueling DQN which is one of the reinforcement learning algorithms. The proposed method was successfully conducted and verified by showing improved performance by reducing convergence times and converging more stably compared to other existing reinforcement learning-based routing algorithms.