Deep Neural Network Task Partitioning and Offloading for Mobile Edge Computing

Abstract

The surging Deep Neural Network (DNN) based applications are becoming increasingly popular in mobile computing. However, they impose significant challenges for mobile computing, as DNN tasks lead to much more computation complexity and data volume compared with traditional tasks. To alleviate this, mobile edge computing (MEC) provides a feasible approach through task partitioning and offloading. In this paper, we investigate a DNN based MEC scheme considering multiple mobile devices and one MEC server. To facilitate task partitioning, we first develop a processing delay prediction mechanism for typical DNN tasks. To achieve the minimal processing delay as well as to release the computing burden of mobile devices, a mixed integer linear programming (MILP) based DNN task partitioning and offloading mechanism is presented. Evaluations show that our mechanism can achieve up to 90.5% and 69.5% processing delay reduction compared with MEC server only and mobile device only schemes respectively.