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

Motahare Mounesan; Xiaojie Zhang; Saptarshi Debroy

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arXiv · 2410.12221

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

Motahare Mounesan·Xiaojie Zhang·Saptarshi Debroy

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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.

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BibTeX RIS

Motahare Mounesan, Xiaojie Zhang, Saptarshi Debroy. 2024-10-16. EdgeRL: Reinforcement Learning-driven Deep Learning Model Inference Optimization at Edge. https://arxiv.org/abs/2410.12221

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