Energy Efficiency of Training Neural Network Architectures: An Empirical Study

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dc.contributor.authorXu, Yinlena
dc.contributor.authorMartínez-Fernández, Silverio
dc.contributor.authorMartinez, Matias
dc.contributor.authorFranch, Xavier
dc.date.accessioned2022-12-27T18:55:13Z
dc.date.available2022-12-27T18:55:13Z
dc.date.issued2023-01-03
dc.description.abstractThe 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.extent10
dc.identifier.doi10.24251/HICSS.2023.098
dc.identifier.isbn978-0-9981331-6-4
dc.identifier.urihttps://hdl.handle.net/10125/102727
dc.language.isoeng
dc.relation.ispartofProceedings of the 56th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectAnalytics and Decision Support for Green IS and Sustainability Applications
dc.subjectdeep learning
dc.subjectenergy efficiency
dc.subjectgreen ai
dc.subjectimage recognition
dc.subjectneural network
dc.titleEnergy Efficiency of Training Neural Network Architectures: An Empirical Study
dc.type.dcmitext
prism.startingpage781

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