MEC辅助增强现实系统中基于视频的AI推理任务的联合优化
Joint Optimization of Video-based AI Inference Tasks in MEC-assisted Augmented Reality Systems
摘要：
人工智能（AI）算法的高计算复杂性和能耗阻碍了其在增强现实（AR）系统中的应用。然而，移动边缘计算（MEC）使解决这个问题成为可能。本文考虑在MEC系统中完成基于视频的人工智能推理任务的场景。我们制定了一个混合整数非线性规划问题（MINLP），以减少推理延迟，能源消耗和提高识别精度。通过推导和实验，给出了推理复杂度模型和准确度模型的简化表达式。然后，通过使用交替优化迭代地解决问题。具体地，通过假设卸载决策被给出，问题被解耦为两个子问题，即，本地完成推理任务的设备集和卸载任务的设备集的资源分配问题。针对卸载决策优化问题，提出了一种基于启发式算法的卸载决策优化算法。为了进一步降低复杂度，我们提出了一种基于交替方向乘法器（ADMM）的分布式算法。基于ADMM的算法具有低的计算复杂性，该计算复杂性随器件数量线性增长。数值实验表明了算法的有效性。文中还分析了时延、能耗和精度之间的权衡关系。
英文摘要：
The high computational complexity and energy consumption of artificial intelligence (AI) algorithms hinder their application in augmented reality (AR) systems. However, mobile edge computing (MEC) makes it possible to solve this problem. This paper considers the scene of completing video-based AI inference tasks in the MEC system. We formulate a mixed-integer nonlinear programming problem (MINLP) to reduce inference delays, energy consumption and to improve recognition accuracy. We give a simplified expression of the inference complexity model and accuracy model through derivation and experimentation. The problem is then solved iteratively by using alternating optimization. Specifically, by assuming that the offloading decision is given, the problem is decoupled into two sub-problems, i.e., the resource allocation problem for the devices set that completes the inference tasks locally, and that for the devices set that offloads tasks. For the problem of offloading decision optimization, we propose a Channel-Aware heuristic algorithm. To further reduce the complexity, we propose an alternating direction method of multipliers (ADMM) based distributed algorithm. The ADMM-based algorithm has a low computational complexity that grows linearly with the number of devices. Numerical experiments show the effectiveness of proposed algorithms. The trade-off relationship between delay, energy consumption, and accuracy is also analyzed.
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。
