Power Prediction of Mobile Processors based on Statistical Analysis of Performance Monitoring Events

In mobile systems, energy efficiency is critical to extend battery life. Therefore, power consumption should be taken into account to develop software in addition to performance. Efficient software design in power and performance is possible if accurate power prediction is accomplished during the execution of software. In this paper, power estimation model is developed using statistical analysis. The proposed model analyzes processor behavior quantitatively using the data of performance monitoring events and power consumption collected by executing various benchmark programs. And then representative hardware events on power consumption are selected using hierarchical clustering. The power prediction model is established by regression analysis in which the selected events are independent variables and power is a response variable. The proposed model is applied to a PXA320 mobile processor based on Intel XScale architecture and shows average estimation error within 4% of the actual measured power consumption of the processor. Keyword: power prediction, mobile processor, performance monitoring events, regression analysis, hierarchical clustering Initial Performance Monitoring Events of a PXA320 processor In order to predict power for PXA320, ten performance monitoring events are selected as initial events (Table 1). The events are collected every 10ms. Table 1. Initial Performance Monitoring Events