Fine-Grained Elastic Partitioning for Distributed DNN Towards Mobile Web AR Services in the 5G Era

Web-based Deep Neural Networks (DNNs) enhance the ability of object recognition and has attracted considerable attention in mobile Web AR and other services. However, neither performing the DNN inference on mobile Web browsers locally nor offloading computations to the cloud can strike a balance between accuracy and efficiency; generally, rude methods are often accompanied by unsatisfactory accuracy. Collaborative approaches seem to fill this gap by coordinating the distributed hierarchical computing resources, especially in the 5G era, but it still faces challenges in the current solutions, such as the lack of (1) full use of 5G resources for the one point DNN computation partitioning schemes; (2) fine-grained branching mechanism; (3) efficient partitioning method; and (4) multi-objective optimization. To this end, we present the fine-grained elastic computation partitioning mechanism for distributed DNN in 5G networks. First, we elaborate two collaborative scenarios. Second, we study the DNN branching mechanism at layer granularity. Next, we propose a DNN computation partitioning algorithm based on deep reinforcement learning. Finally, we develop a mobile Web AR application as a proof of concept. The experiments were conducted in an actually deployed 5G trial network, and the results show the superiority of this collaborative approach. The common theme is, under the premise that Quality of Service (QoS) is satisfied, to balance multiple interests by orchestrating computations across heterogeneous computing platforms.

Automated summary

Web-based Deep Neural Networks (DNNs) have significant applications in mobile Web AR and other services. However, achieving a balance between accuracy and efficiency remains challenging when performing DNN inference on mobile Web browsers or offloading computations to the cloud. Collaborative approaches can coordinate distributed computing resources, but current solutions still face challenges. This paper proposes a fine-grained elastic computation partitioning mechanism for distributed DNN in 5G networks and demonstrates its superiority through experiments.