FRAMEWORK FOR IMPLEMENTING A MACHINE LEARNING WORKFLOW IN DYNAMIC VOLTAGE AND FREQUENCY SCALING (DVFS) FOR IMPROVED POWER AND THERMAL MANAGEMENT

The ongoing advancement of technology calls for continuous enhancement of energy efficiency in microprocessors. One of the fundamental techniques employed for such improvements is Dynamic Voltage and Frequency Scaling (DVFS), which strategically adjusts power levels based on varying workloads. However, finding the optimal balance for these adjustments presents a complex challenge that needs to be addressed. This paper proposes a novel approach that utilizes Machine Learning (ML) models to enhance the DVFS design and simulation framework. The proposed framework incorporates three critical modules- microarchitectural simulation, ML model-based prediction, and Deep Reinforcement Learning (DRL) based DVFS control—of ering an intelligent management mechanism that efficiently trades of among dif erent optimization targets. The paper discusses various ML models and their effectiveness in predicting workload patterns, enabling precise estimation of power and thermal dissipation