A Survey on Collaborative DNN Inference for Edge Intelligence

With the vigorous development of artificial intelligence (AI), intelligence applications based on deep neural networks (DNNs) have changed people′s lifestyles and production efficiency. However, the large amount of computation and data generated from the network edge becomes the major bottleneck, and the traditional cloud-based computing mode has been unable to meet the requirements of realtime processing tasks. To solve the above problems, by embedding AI model training and inference capabilities into the network edge, edge intelligence (EI) becomes a cutting-edge direction in the field of AI. Furthermore, collaborative DNN inference among the cloud, edge, and end devices provides a promising way to boost EI. Nevertheless, at present, EI oriented collaborative DNN inference is still in its early stage, lacking systematic classification and discussion of existing research efforts. Motivated by it, we have comprehensively investigated recent studies on EI-oriented collaborative DNN inference. In this paper, we first review the background and motivation of EI. Then, we classify four typical collaborative DNN inference paradigms for EI, and analyse their characteristics and key technologies. Finally, we summarize the current challenges of collaborative DNN inference, discuss future development trends and provide future research directions.