MEC辅助XR设备中多任务DNN推理的能量优化：一种Lyapunov引导的强化学习方法
Energy Optimization of Multi-task DNN Inference in MEC-assisted XR Devices: A Lyapunov-Guided Reinforcement Learning Approach
AI总结：
针对MEC辅助XR设备的多任务DNN推理能耗问题，提出双时间尺度模型划分与资源分配双层优化，并利用LyaPPO实现显著节能。
摘要：
扩展现实（XR）融合虚拟世界与现实世界，是未来网络的关键应用。尽管AI进步增强了XR能力，但也给轻量级XR设备带来了巨大的计算与能量挑战。本文针对多任务DNN推理建立了分布式队列模型，以解决资源竞争和队列耦合问题。为应对XR设备高能耗与资源有限所带来的挑战，我们设计了一种用于模型划分和资源分配的双时间尺度联合优化策略，并将其表述为一个双层优化问题。该策略旨在在保证队列稳定性并遵守计算与通信资源约束的同时，最小化XR设备的总能耗。为求解该问题，我们设计了一种Lyapunov引导的Proximal Policy Optimization（近端策略优化）算法，命名为LyaPPO。数值结果表明，LyaPPO算法优于各基线方法，在不同资源容量下实现24.79%至46.14%的节能。具体而言，与基线算法相比，所提算法将XR设备的能耗降低了24.29%至56.62%。
英文摘要：
Extended reality (XR), blending virtual and real worlds, is a key application of future networks. While AI advancements enhance XR capabilities, they also impose significant computational and energy challenges on lightweight XR devices. In this paper, we developed a distributed queue model for multi-task DNN inference, addressing issues of resource competition and queue coupling. In response to the challenges posed by the high energy consumption and limited resources of XR devices, we designed a dual time-scale joint optimization strategy for model partitioning and resource allocation, formulated as a bi-level optimization problem. This strategy aims to minimize the total energy consumption of XR devices while ensuring queue stability and adhering to computational and communication resource constraints. To tackle this problem, we devised a Lyapunov-guided Proximal Policy Optimization algorithm, named LyaPPO. Numerical results demonstrate that the LyaPPO algorithm outperforms the baselines, achieving energy conservation of 24.79% to 46.14% under varying resource capacities. Specifically, the proposed algorithm reduces the energy consumption of XR devices by 24.29% to 56.62% compared to baseline algorithms.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
