Joint Optimization of Data Transfer and Co-Execution for DNN in Edge Computing

Deep learning plays an increasingly important role in human life. However, resource-constrained IoT devices are still inefficient in performing deep neural network (DNN) inference. Existing works have attempted to improve the performance by leveraging edge computing that partitions DNN and offloads a part of workloads to the edge server. However, most of them focus on scheduling workloads among different devices, ignoring the network costs. Thus, user experience easily suffers from inferior conditions such as network congestion. To address this issue, we jointly consider network conditions and computing capabilities of the IoT device and edge server, and then propose FastCoDNN, a co-execution framework that enables high-performance DNN inference. Specifically, under inferior conditions, we conduct redundant calculation instead of data synchronization to reduce network costs. Then, we orchestrate redundant calculations and data synchronizations among the local and edge, and flexibly adjust them according to new network conditions. We propose a new algorithm based on dynamic programming to achieve this adjustment. Experimental results show that FastCoDNN achieves fewer network costs and much performance improvement compared with existing methods.