AppealNet: An Efﬁcient and Highly-Accurate Edge/Cloud Collaborative Architecture for DNN Inference
Hosted in Virtual Platform
Design of Cyber-physical Systems and IoT
DescriptionThis 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 cost of the edge/cloud system. Experimental results show up to more than 40\% energy savings compared to baselines.