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

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.