Energy efficiency of training neural network architectures: an empirical study
Document typeConference report
PublisherUniversity Of Hawaii
Rights accessOpen Access
Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution-NonCommercial-NoDerivs 4.0 International
ProjectDESARROLLO, OPERATIVA Y GOBERNANZA DE DATOS PARA SISTEMAS SOFTWARE BASADOS EN APRENDIZAJE AUTOMATICO (AEI-PID2020-117191RB-I00)
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.
CitationXu, Y. [et al.]. Energy efficiency of training neural network architectures: an empirical study. A: Hawaii International Conference on System Sciences. "Proceedings of the 56th Annual Hawaii International Conference on System Sciences: January 3-6, 2023, Hyatt Regency Maui". Honolulu, HI: University Of Hawaii, 2023, p. 781-790. ISBN 978-0-9981331-6-4.
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