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

TL;DR

EdgeRL is a reinforcement learning framework that optimizes deep learning inference at the edge by balancing latency, accuracy, and energy consumption, using real-world tests to demonstrate its effectiveness.

Contribution

It introduces a novel RL-based approach, EdgeRL, for dynamic optimization of DNN inference parameters tailored to application-specific performance trade-offs.

Findings

Significant energy savings on end devices.

Improved inference accuracy.

Reduced end-to-end latency.

Abstract

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.