Optimizing the Learning Accuracy in Mobile Augmented Reality Systems with CNN

Abstract

With the combination of deep learning and mobile edge computing, the accuracy of the computer vision task in mobile augmented reality (AR) applications can be significantly improved along with the enhancement on the end-to-end latency and energy efficiency. However, no architecture-based delay model for convolutional neural networks (CNNs) has been proposed in edge computing. In this paper, we first develop a new delay model to characterize the relation between the processing delay and the input image size of general CNN models. Then, we formulate a non-convex optimization problem to maximize the learning accuracy under the communication and computation resource constraints. By problem transformation, the optimal resource allocation policy is derived in closed-form and low-complexity search algorithm is also developed. Finally, test results validate the applicability of the delay model and demonstrate the learning accuracy improvement of the proposed algorithm.