Energy efficient solutions for computing and sensing
Thesis event information
Date and time of the thesis defence
Place of the thesis defence
Auditorium IT116, Linnanmaa, remote connection: https://oulu.zoom.us/j/69150544460
Topic of the dissertation
Energy efficient solutions for computing and sensing
Doctoral candidate
Master of Science (Tech) Mehdi Safarpour
Faculty and unit
University of Oulu Graduate School, Faculty of Information Technology and Electrical Engineering, Center for Machine Vision and Signal Analysis
Subject of study
Computer Science
Opponent
Professor Jari Nurmi, University of Tampere
Custos
Professor Olli Silvén, University of Oulu
Reducing energy consumption of electronics devices
There are many electronic devices around us, but many of them depend on batteries to function. Often battery life is an issue. Progress in the field of more advanced batteries is slow. In this study, we provide solutions that reduce the power consumption of common electronic components and extend battery life. Emerging technologies, such as the Internet of Things (IoT), Deep Neural Network (DNN) based machine learning, and 6th generation wireless communications, impose substantial performance and energy efficiency demands for implementations. In answer to the requirements, this thesis focuses on improving the efficiency of selected energy hungry system sections, from signal acquisition to computing.
Last updated: 1.3.2023