Do Larger (More Accurate) Deep Neural Network Models Help in Edge-assisted Augmented Reality?
Jiayi Meng, Zhaoning Kong, Qiang Xu, Y. Charlie Hu · 2021
Edge-assisted Augmented Reality (AR) which offloads compute-intensive Deep Neural Network (DNN)-based AR tasks to edge servers faces an important design challenge: how to pick the DNN model out of many choices proposed for each AR task for offloading. For each AR task, e.g., depth estimation, many DNN-based models have been proposed over time that vary in accuracy and complexity. In general, more accurate models are also more complex; they are larger and have longer inference time. Thus choosing a larger model in offloading can provide higher accuracy for the offloaded frames but also incur longer turnaround time, during which the AR app has to reuse the estimation result from the last offloaded frame, which can lead to lower average accuracy.