Work

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

Year: 2024

Type: conference-paper

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.

Source: International Conference on Network and Service Management (CNSM)

DOI: 10.23919/cnsm62983.2024.10814523

Authors: Motahare Mounesan, Xiaojie Zhang, Saptarshi Debroy

Institutions: City University of New York, Hunan First Normal University

Language: English

Topic: IoT and Edge/Fog Computing

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: green

Funder: National Science Foundation