# GPU-Accelerated CNN Inference for Onboard DQN-Based Routing in Dynamic LEO Satellite Networks
## 2 Related Work  
Table 1 provides a brief overview of related studies, categorized based on whether they consider LEO satellite network environments, reinforcement learning, parallelization, or onboard environments. Ref. [10] applies the Dijkstra algorithm for designing dynamic  
routing algorithms for LEO satellites. The Dijkstra algorithm is a method for finding the shortest path from one node to all other nodes, and it can be applied to satellite networks for setting up communication paths between satellites. In this approach, the link database is periodically updated to compute the optimal path. The computed path information is then recorded in the routing table, and a routing algorithm is proposed that can quickly respond to changes in the link status. Ref. [11] focuses on predicting congestion states and distributing loads through link state analysis to optimize routing paths. To achieve this, it combines the GRouting algorithm with GNNs (Graph Neural Networks) and DRL (Deep Reinforcement Learning). A GNN is a deep learning model that learns the structural characteristics of each node and the relationships between nodes based on graph-structured data. DRL, using the learned network representation, dynamically selects the optimal routing paths between satellites. DRL identifies paths that minimize transmission delay while efficiently utilizing network resources. Refs. [12; 13; 14] apply reinforcement learning to routing algorithms, where the agent learns the optimal policy by interacting with the environment to maximize future cumulative rewards. These methods, based on immediate rewards, are being studied to quickly find optimal paths in highly dynamic LEO satellite networks. Compared to the DQN, the Dueling DQN employed in this paper demonstrates superior performance in complex environments, such as dynamic network environments. By separating the state-value function and the action-advantage function to calculate the Q-value, Dueling DQN achieves greater efficiency and stability in inference compared to DQN. This is particularly effective in environments where differences between actions are minimal, or state changes occur frequently. In LEO satellite onboard systems, where computational resources are limited and network topology often changes dynamically, Dueling DQN reduces the computational burden of insignificant actions, supporting rapid and reliable inference. This enhances the efficiency of communication path selection and resource management. While this approach overcomes the limitations of existing algorithms and proposes an efficient solution, it requires considerable computational resources. Considering the real-time inference process in LEO satellites, parallelization research is essential to address the constraints of limited computational resources. Refs. [15; 16] parallelize LEO satellite network routing algorithms using reinforcement learning on FPGAs to overcome onboard computational constraints. The FPGA's programmable logic (PL) optimizes hardware for computationally intensive tasks and accelerates parallel processing by leveraging techniques like pipelining. While FPGAs have the advantage of efficiently optimizing specific tasks, they may be less favorable in terms of scalability. Additionally, the complexity of hardware design and high initial costs are significant drawbacks. Ref. [17] utilizes GPU parallel processing capabilities to accelerate reinforcement learning algorithms. GPUs, with their thousands of cores, are well-suited for processing large volumes of data simultaneously, making them highly effective for parallel computation. The ability of GPUs to efficiently handle operations like matrix multiplications in reinforcement learning algorithms accelerates model training and inference. Furthermore, GPUs support high-performance software libraries and frameworks (e.g., CUDA and cuDNN), which significantly enhance development efficiency. Therefore,