The high computational complexity and high energy consumption of artificial intelligence (AI) algorithms hinder their application in augmented reality (AR) systems. This paper considers the scene of completing video-based AI inference tasks in the mobile edge computing (MEC) system. We use multiply-and-accumulate operations (MACs) for problem analysis and optimize delay and energy consumption under accuracy constraints. To solve this problem, we first assume that offloading policy is known and decouple the problem into two subproblems. After solving these two subproblems, we propose an iterative-based scheduling algorithm to obtain the optimal offloading policy. We also experimentally discuss the relationship between delay, energy consumption, and inference accuracy.
翻译:人工智能(AI)算法的高度计算复杂性和高能消耗阻碍了其在扩大现实系统的应用。本文审议了在移动边缘计算系统中完成基于视频的AI推论任务的场景。我们使用乘积操作进行问题分析,并在精确度限制下优化延迟和能源消耗。为了解决这个问题,我们首先假设,卸载政策是众所周知的,并将问题分为两个子问题。在解决了这两个子问题之后,我们提出了基于迭接的排期算法,以获得最佳的卸载政策。我们还试验性地讨论了延迟、能源消耗和推论准确性之间的关系。