论文标题
AR应用的准确性限制下DNN推断延迟和能量的联合优化
Joint Optimization of DNN Inference Delay and Energy under Accuracy Constraints for AR Applications
论文作者
论文摘要
人工智能（AI）算法的高计算复杂性和高能消耗阻碍了它们在增强现实（AR）系统中的应用。本文考虑了在移动边缘计算（MEC）系统中完成基于视频的AI推理任务的场景。我们使用多重和积累的操作（MAC）进行问题分析，并在准确性约束下优化延迟和能源消耗。为了解决这个问题，我们首先假设已知卸载策略并将问题分解为两个子问题。解决这两个子问题后，我们提出了一种基于迭代的调度算法，以获得最佳的卸载策略。我们还实验讨论了延迟，能耗和推理准确性之间的关系。
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.
