Abstract
AbstractWhen the software in embedded system runs, the current may change dynamically in wide ranges. It is difficult to accurately measure the energy consumption of software in embedded system. Some existing methods can provide the functions that measuring the currents of different regions by using different ranges through adjusting shunt resistor or amplification. However, these methods are hard to give the value of current accurately during range switching, and bring some measuring errors. To address this problem, this paper designs hardware and software schemes to measure the current of software in embedded system by using small, medium and large ranges simultaneously. Further, a two-stage calibration method based on machine learning is proposed. And a Measurement System for Energy consumption of software in Embedded system (MSee) is presented. The experimental results show that both the average relative errors of MSee in small, medium and large ranges and the average relative errors of MSee in transitional neighborhood of ranges are better than those existing methods of range switching.KeywordsEnergy consumptionCalibrationEmbedded systemMeasurementMachine learningMulti-range
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