This paper presents AppealNet, a novel edge/cloud collaborative architecture that runs deep learning (DL) tasks more efficiently than state-of-the-art solutions. For a given input, AppealNet accurately predicts on-the-fly whether it can be successfully processed by the DL model deployed on the resource-constrained edge device, and if not, appeals to the more powerful DL model deployed at the cloud. This is achieved by employing a two-head neural network architecture that explicitly takes inference difficulty into consideration and optimizes the tradeoff between accuracy and computation/communication cost of the edge/cloud collaborative architecture. Experimental results on several image classification datasets show up to more than 40% energy savings compared to existing techniques without sacrificing accuracy.
@article{arxiv.2105.04104,
title = {AppealNet: An Efficient and Highly-Accurate Edge/Cloud Collaborative Architecture for DNN Inference},
author = {Min Li and Yu Li and Ye Tian and Li Jiang and Qiang Xu},
journal= {arXiv preprint arXiv:2105.04104},
year = {2021}
}