PREDICTING MICROPROCESSOR POWER CONSUMPTION BASED ON HARDWARE PERFORMANCE COUNTERS

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dc.contributor.authorPopov, Alexandr
dc.date.accessioned2024-06-21T06:22:50Z
dc.date.available2024-06-21T06:22:50Z
dc.date.issued2024-04-22
dc.description.abstractThis thesis presents a foundation for microprocessor power consumption estimation and prediction framework development for ARM-based devices with limited power resources using hardware performance counters. The study introduces a LSTM RNN model to estimate power consumption based on CPU HPC data without evaluation of other hardware events.. This method has a potential advantage for battery-powered embedded systems, where traditional power measurement tools have small efficiency. The research builds upon previous work in the field, highlighting the importance of energy-efficient designs in the growing IoT market. The proposed framework aims to enhance the battery life of portable devices, by helping developers to optimise the software and enabling devices with real-time power management. The model was trained on the dataset collected in idle, video recording, video streaming and audio recording scenarios and evaluated on RMSE performance. Results of the paper suggest that prediction performance of the RNN LSTM model are lacking, but the use of adaptive algorithms in a regression like evaluation have potential to be effective.en_US
dc.identifier.citationPopov, A. (2024). Predicting Microprocessor Power Consumption Based on Hardware Performance Counters. Nazarbayev University School of Engineering and Digital Sciencesen_US
dc.identifier.urihttp://nur.nu.edu.kz/handle/123456789/7927
dc.language.isoenen_US
dc.publisherNazarbayev University School of Engineering and Digital Sciencesen_US
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.subjectType of access: Restricteden_US
dc.subjectMicroprocessor Power Consumptionen_US
dc.subjectHardware Performance Countersen_US
dc.subjectMachine Learningen_US
dc.subjectARM Cortex-A53en_US
dc.subjectPower Prediction Frameworken_US
dc.subjectLSTMen_US
dc.subjectRecurrent Neural Networksen_US
dc.titlePREDICTING MICROPROCESSOR POWER CONSUMPTION BASED ON HARDWARE PERFORMANCE COUNTERSen_US
dc.typeMaster's thesisen_US
workflow.import.sourcescience

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