Edge Computing with Early Exiting for Adaptive Inference in Mobile Autonomous Systems

Early Exiting (EE) is an emerging computing paradigm where Deep Neural Networks (DNNs) are equipped with earlier classifiers, enabling trading-off accuracy with inference latency. EE can be effectively combined with edge computing, a paradigm that allows mobile nodes to offload complex tasks, such as the execution of DNNs, to servers at the edge of the network, thus reducing computing times and energy consumption at the mobile devices. The integration of such technologies is particularly attractive for the support of applications for connected and automated driving. In this paper, we consider a system that jointly leverages the benefits of EE and edge computing, and we model their complex interactions by means of a Markov Decision Process (MDP). We then formulate an optimization problem to select the inference strategy that maximizes the average task accuracy. Importantly, such an optimization problem has low complexity, as the optimal policy can be derived by mapping the MDP into a linear program. Our numerical results focus on a use case centered on automated vehicles connected with an edge server under varying channel and network conditions, and show that our solution achieves up to 11% higher accuracy compared to the optimal policy with no EE.