EdgeRL: Reinforcement Learning-driven Deep Learning Model Inference Optimization at Edge

Balancing mutually diverging performance metrics, such as, processing latency, outcome accuracy, and end device energy consumption is a challenging undertaking for deep learning model inference in ad-hoc edge environments. In this paper, we propose EdgeRL framework that seeks to strike such balance by using an Advantage Actor-Critic (A2C) Reinforcement Learning (RL) approach that can choose optimal run-time DNN inference parameters and aligns the performance metrics based on the application requirements. Using real world deep learning model and a hardware testbed, we evaluate the benefits of EdgeRL framework in terms of end device energy savings, inference accuracy improvement, and end-to-end inference latency reduction. Deep learning models, particularly deep neural networks (DNN), are becoming increasingly important for mission-critical applications, such as public safety, tactical scenarios, search and rescue, and emergency triage, most of which are often edgenative. Unlike traditional edge that are typically part of the network infrastructure, a new paradigm of ad-hoc deployments of edge computing environments are currently being adopted by public safety agencies and armed forces [1]-[3] to support mission-critical use cases.