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.Comment: 6 pages, 7 figures, accepted by Globecom202