Energy Efficiency of Training Neural Network Architectures: An Empirical Study Xu, Yinlena Martínez-Fernández, Silverio Martinez, Matias Franch, Xavier 2022-12-27T18:55:13Z 2022-12-27T18:55:13Z 2023-01-03
dc.description.abstract The evaluation of Deep Learning (DL) models has traditionally focused on criteria such as accuracy, F1 score, and related measures. The increasing availability of high computational power environments allows the creation of deeper and more complex models. However, the computations needed to train such models entail a large carbon footprint. In this work, we study the relations between DL model architectures and their environmental impact in terms of energy consumed and CO2 emissions produced during training by means of an empirical study using Deep Convolutional Neural Networks. Concretely, we study: (i) the impact of the architecture and the location where the computations are hosted on the energy consumption and emissions produced; (ii) the trade-off between accuracy and energy efficiency; and (iii) the difference on the method of measurement of the energy consumed using software-based and hardware-based tools.
dc.format.extent 10
dc.identifier.isbn 978-0-9981331-6-4
dc.language.iso eng
dc.relation.ispartof Proceedings of the 56th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.subject Analytics and Decision Support for Green IS and Sustainability Applications
dc.subject deep learning
dc.subject energy efficiency
dc.subject green ai
dc.subject image recognition
dc.subject neural network
dc.title Energy Efficiency of Training Neural Network Architectures: An Empirical Study
dc.type.dcmi text
prism.startingpage 781
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