论文标题
跨建筑能力建模
Cross Architectural Power Modelling
论文作者
论文摘要
现有的电源建模研究重点是模型，而不是开发模型的过程。开发了一个可以部署在不同处理器上以高精度开发电源模型的自动化功率建模过程。为此，（i）选择与手臂和英特尔处理器上电力相关的计数器的自动硬件性能计数器选择方法，（ii）基于聚类的噪声过滤器，可以减少功率模型中的平均误差，（iii）两级功率模型，在提出和开发跨多个架构模型的现有电源模型时，都提出和开发了跨多个电源的挑战。关键结果是：（i）自动硬件性能反选择方法获得与文献中报道的手动方法相当的选择，（ii）噪声滤波器将功率模型中的平均误差降低了55％，并且（iii）两个阶段功率模型可以预测动态功率在手臂和英特尔流程中的误差少于8％，这是一种改善经典模型。
Existing power modelling research focuses on the model rather than the process for developing models. An automated power modelling process that can be deployed on different processors for developing power models with high accuracy is developed. For this, (i) an automated hardware performance counter selection method that selects counters best correlated to power on both ARM and Intel processors, (ii) a noise filter based on clustering that can reduce the mean error in power models, and (iii) a two stage power model that surmounts challenges in using existing power models across multiple architectures are proposed and developed. The key results are: (i) the automated hardware performance counter selection method achieves comparable selection to the manual method reported in the literature, (ii) the noise filter reduces the mean error in power models by up to 55%, and (iii) the two stage power model can predict dynamic power with less than 8% error on both ARM and Intel processors, which is an improvement over classic models.
