IDEC: Intelligent Distributed Edge Computing System Architecture Enabling Deep Learning across Heterogeneous IoT Devices

摘要

To cope with rapid increases in both the number of smart devices and the amount of computation-intensive edge applications on such devices, distributed cross-device edge computing systems have received much attention in the Internet-of-Things (IoT) community. The heterogeneity of IoT devices and the dynamic nature of edge computing environments make it hard to manage the edge resources in a uniform manner that enables collaboration and sharing of resources among the devices. In this paper, we propose Intelligent Distributed Edge Computing (IDEC) system architecture, which is a full-stack system design to support deep learning (DL) model training and inference across heterogeneous resource-constrained IoT devices. IDEC has the characteristics of heterogeneity-compatibility, high performance, and intelligent adaptability. It mainly comprises of three modules: edge resource management, computing task decomposition, and Intelligent Computing Task Allocation (ICTA). IDEC can conduct unified resource management and effective scheduling of heterogeneous edge devices, operator-level task decomposition of deep models, and automatic end-to-end optimization of task allocation based on DL algorithm, thereby achieving resource utilization maximization and further obtaining better system performance. Moreover, through training ICTA periodically in an end-to-end manner based on a continuous learning mechanism, IDEC will become smarter while being used, and help realizing intelligent edge applications and services.