Energy-Efficient DNN Partitioning and Offloading for Task Completion Rate Maximization in Multiuser Edge Intelligence

Abstract

Deep Neural Network (DNN) has become an essential technology for edge intelligence. Due to significant resource and energy requirements for large-scale DNNs’ inference, executing them directly on energy-constrained Internet of Things (IoT) devices is impractical. DNN partitioning provides a feasible solution for this problem by offloading some DNN layers to execute on the edge server. However, the resources of edge servers are also typically limited. An energy-constrained and resource-constrained optimization problem is generated in such a realistic environment. Motivated by this, we investigate an optimization problem of DNN partitioning and offloading in a multiuser resource-constrained environment, which is considered an intractable Mixed-Integer Nonlinear Problem (MINLP). We decompose the problem into two subproblems and propose an Energy-Efficient DNN Partitioning and Offloading (EEDPO) strategy to solve it in polynomial time based on the minimum cut/maximum flow theorem and dynamic programming. Finally, we test the impact of energy constraint, DNN type, and device number on the performance of EEDPO. Simulation results on realistic DNN models demonstrate that the proposed strategy can significantly improve the DNN inference task completion rate compared to other methods.