基于机器学习的处理器电源管理方法研究

Research on Processor Power Management Method Based on Machine Learning

深入分析处理器电源管理方法后,将机器学习的思想应用到电源管理方法中。以树莓派4开发板为实验平台,比较传统动态调频调压技术与基于随机森林的动态调频调压技术在功耗和温度方面的性能表现。实验结果显示,基于随机森林的技术在不同负载下相较于传统技术表现出更为优越的功耗和温度控制特性。该技术在实验中取得显著的效果,为处理器电源管理领域的性能优化提供了新的视角和方法。

After analyzing the processor power management method in depth,the idea of machine learning is applied to the power management method.The Raspberry Pi 4 development board is used as an experimental platform to compare the performance of the traditional dynamic frequency and voltage regulation technology and the random forest-based dynamic frequency and voltage regulation technology in terms of power consumption and temperature.The experimental results show that the random forest-based technique exhibits superior power consumption and temperature control characteristics compared to the traditional technique under different loads.The technique achieves significant results in the experiments and provides new perspectives and methods for performance optimization in the field of processor power management.