This magazine article introduces computation uncertainty of edge computing to the wireless communication society. The simple and intuitive principle called AI model diversity is proposed as the cornerstone for resolving uncertainty at the edge. With the design approaches and research directions provided in the article, we hope to see new opportunities in wireless communications research grow and bloom.
Model Diversity Network (MoDNet) architecture for edge computing that exploits AI model diversity
Abstract: Due to the edge's position between the cloud and the users, and the recent surge of deep neural network (DNN) applications, edge computing brings about uncertainties that must be understood separately. Particularly, the edge users' locally specific requirements that change depending on time and location cause a phenomenon called dataset shift, defined as the difference between the training and test datasets' representations. It renders many of the state-of-the-art approaches for resolving uncertainty insufficient. Instead of finding ways around it, we exploit such phenomenon by utilizing a new principle: AI model diversity, which is achieved when the user is allowed to opportunistically choose from multiple AI models. To utilize AI model diversity, we propose Model Diversity Network (MoDNet), and provide design guidelines and future directions for efficient learning driven communication schemes.
Authors: Sejin Seo, Sang Won Choi, Sujin Kook, Seong-Lyun Kim, and Seung-Woo Ko, “Understanding Uncertainty of Edge Computing: New Principle and Design Approach,” submitted to IEEE Communications Magazine.
preprint available: https://arxiv.org/abs/2006.01032