DNN-Based Task Partitioning and Offloading in Edge-Cloud Collaboration Within Electric Vehicles

Abstract

The rapid development of vehicular networks has led to widespread adoption of various electric vehicle (EV) applications, often necessitating the deployment of large-scale deep neural networks (DNNs). However, constrained computational and energy resources pose challenges for executing computationally intensive DNN tasks exclusively within EVs. To address this issue, one potential solution is to utilize edge or cloud computing resources for collaborative computation, typically implemented through DNN partitioning and task offloading. Therefore, we propose a novel approach in this paper, named TOCC, to execute EV-generated DNN tasks with edge-cloud collaboration. Specifically, we first construct a performance prediction model, which can accurately predict the performance of different layers in a DNN. Then, we define the joint optimization problem of minimizing processing delay and energy consumption as a Markov Decision Process (MDP). Finally, we employ Deep Reinforcement Learning (DRL) to design a strategy that enables EVs to make optimal decisions for DNN task partitioning and offloading. The experimental results demonstrate that TOCC surpasses existing approaches in terms of both processing delay and energy consumption, and is applicable to various DNN types.