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The growing demand for optimal and low-power energy consumption paradigms forInternet of Things (IoT) devices has garnered significant attention due totheir cost-effectiveness, simplicity, and intelligibility. We propose anArtificial Intelligence (AI) hardware energy-efficient framework to achieveoptimal energy savings in heterogeneous computing through appropriate powerconsumption management. A deep reinforcement learning framework is employed,utilizing the Actor-Critic architecture to provide a simple and precise methodfor power saving. The results of the study demonstrate the proposed approach'ssuitability for different hardware configurations, achieving notable energyconsumption control while adhering to strict performance requirements. Theevaluation of the proposed power-saving framework shows that it is more stable,and has achieved more than 23% efficiency improvement, outperforming othermethods by more than 5%.

Multi-Objective Optimization Approach Using Deep Reinforcement Learning for Energy Efficiency in Heterogeneous Computing System