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dc.contributor.advisorKim, Hyesoon
dc.contributor.authorMagudilu Vijayaraj, Thejasvi Magudilu
dc.date.accessioned2013-09-20T13:25:48Z
dc.date.available2013-09-20T13:25:48Z
dc.date.created2013-08
dc.date.issued2013-05-20
dc.date.submittedAugust 2013
dc.identifier.urihttp://hdl.handle.net/1853/49054
dc.description.abstractPower is one of the today’s major constraints for both hardware and software design. Thus the need to understand the statistics and distribution of power consumption from a hardware and software perspective is high. Power models satisfy this requirement to a certain extent, by estimating the power consumption for a subset of applications, or by providing a detailed power consumption distribution of a system. Till date, many power models have been proposed for the desktop and mobile platforms. However, most of these models were created based on power measurements performed on the entire system when different microbenchmarks stressing different blocks of the system were run. Then the measured power and the profiled information of the subsystem stressing benchmarks were used to create a regression analysis based model. Here, the power/energy prediction accuracy of the models created in this way, depend on both the method and accuracy of the power measurements and the type of regression used in generating the model. This work tries to eliminate the dependency of the accuracy of the power models on the type of regression analysis used, by performing power measurements at a subsystem granularity. When the power measurement of a single subsystem is obtained while stressing it, one can know the exact power it is consuming, instead of obtaining the power consumption of the entire system - without knowing the power consumption of the subsystem of interest - and depending on the regression analysis to provide the answer. Here we propose a generic method that can be used to create power models of individual subsystems of mobile platforms, and validate the method by presenting an empirical power model of the OMAP4460 based Pandaboard-ES, created using the proposed method. The created model has an average percentage of energy prediction error of just around -2.7% for the entire Pandaboard-ES system.
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherGeorgia Institute of Technology
dc.subjectEmpirical power model
dc.subjectOMAP4460
dc.subjectPandaboard
dc.subject.lcshPower electronics
dc.subject.lcshEnergy consumption
dc.titleAn empirical power model of a low power mobile platform
dc.typeThesis
dc.description.degreeM.S.
dc.contributor.departmentElectrical and Computer Engineering
thesis.degree.levelMasters
dc.contributor.committeeMemberMukhopadhyay, Saibal
dc.contributor.committeeMemberYalamanchili, Sudhakar
dc.date.updated2013-09-20T13:25:48Z


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