A Dynamic Deep Neural Network Design for Efficient Workload Allocation in Edge Computing

Unreliable communication channels and limited computing resources at the edge end are two primary constraints of battery-powered movable devices, such as autonomous robots and unmanned aerial vehicles (UAVs). The impact is especially severe for those performing deep neural network (DNN) computations. With increasing demand for accuracy, the trend in modern DNN designs is the use of cascaded modularized layers. Implementing a deep network at the edge increases computational workloads and resource occupancy, leading to an increase in battery drain. Using a shallow network and offloading workloads to backbone servers, however, incur significant latency overheads caused by unstable communication channels. Hence, dynamic DNN design techniques for efficient workload allocation are urgently required to manage the amount of workload transmissions while achieving the required accuracy. In this paper, we explore the use of authentic operation (AO) unit and dynamic network structure to enhance DNNs. The AO unit defines a set of stochastic threshold values for different DNN output classes and determines at runtime if an input has to be transferred to backbone servers for further analysis. The dynamic network structure adjusts its depth according to channel availability. Experiments have been comprehensively performed on several well-known DNN models and datasets. Our results show that, on an average, the proposed techniques are able to reduce the amount of transmissions by up to 17% compared to previous methods under the same accuracy requirement.