Deep Reinforcement Learning for Energy-Efficient on the Heterogeneous Computing Architecture

TL;DR

This paper presents a deep reinforcement learning framework using Actor-Critic architecture to optimize energy consumption in heterogeneous computing architectures, particularly for IoT devices, achieving significant efficiency improvements.

Contribution

It introduces a novel AI-based energy management framework specifically designed for heterogeneous hardware, outperforming existing methods in energy efficiency.

Findings

Achieved over 34.6% energy efficiency improvement.

Outperformed other methods by more than 16%.

Demonstrated stability across various hardware configurations.

Abstract

The growing demand for optimal and low-power energy consumption paradigms for IOT devices has garnered significant attention due to their cost-effectiveness, simplicity, and intelligibility. In this article, an AI hardware energy-efficient framework to achieve optimal energy savings in heterogeneous computing through appropriate power consumption management is proposed. The deep reinforcement learning framework is employed, utilising the Actor-Critic architecture to provide a simple and precise method for power saving. The results of the study demonstrate the proposed approach's suitability for different hardware configurations, achieving notable energy consumption control while adhering to strict performance requirements. The evaluation of the proposed power-saving framework shows that it is more stable, and has achieved more than 34.6% efficiency improvement, outperforming other methods by more than 16%.