AppealNet: An Efficient and Highly-Accurate Edge/Cloud Collaborative Architecture for DNN Inference
This addresses the challenge of resource-constrained edge devices for applications like IoT or mobile computing, though it is incremental as it builds on existing collaborative architectures.
The paper tackled the problem of efficiently running deep learning tasks in edge/cloud collaborative systems by introducing AppealNet, which predicts whether inputs can be processed at the edge or need cloud assistance, resulting in up to 40% energy savings without accuracy loss.
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.