Desislavov, R.; Martínez-Plumed, F.; Hernández-Orallo, J. (2023). Trends in AI inference energy consumption: Beyond the performance-vs-parameter laws of deep learning. Sustainable Computing: Informatics and Systems. 38:1-17. https://doi.org/10.1016/j.suscom.2023.100857
Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/204447
Título:
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Trends in AI inference energy consumption: Beyond the performance-vs-parameter laws of deep learning
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Autor:
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Desislavov, Radosvet
Martínez-Plumed, Fernando
Hernández-Orallo, José
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Entidad UPV:
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Universitat Politècnica de València. Escola Tècnica Superior d'Enginyeria Informàtica
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Fecha difusión:
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Resumen:
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[EN] The progress of some AI paradigms such as deep learning is said to be linked to an exponential growth in the number of parameters. There are many studies corroborating these trends, but does this translate into an ...[+]
[EN] The progress of some AI paradigms such as deep learning is said to be linked to an exponential growth in the number of parameters. There are many studies corroborating these trends, but does this translate into an exponential increase in energy consumption? In order to answer this question we focus on inference costs rather than training costs, as the former account for most of the computing effort, solely because of the multiplicative factors. Also, apart from algorithmic innovations, we account for more specific and powerful hardware (leading to higher FLOPS) that is usually accompanied with important energy efficiency optimisations. We also move the focus from the first implementation of a breakthrough paper towards the consolidated version of the techniques one or two year later. Under this distinctive and comprehensive perspective, we analyse relevant models in the areas of computer vision and natural language processing: for a sustained increase in performance we see a much softer growth in energy consumption than previously anticipated. The only caveat is, yet again, the multiplicative factor, as future AI increases penetration and becomes more pervasive.
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Palabras clave:
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Artificial Intelligence
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Deep learning
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Inference
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Energy consumption
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Performance analysis
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Performance evaluation
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AI progress
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Derechos de uso:
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Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
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Fuente:
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Sustainable Computing: Informatics and Systems. (issn:
2210-5379
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DOI:
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10.1016/j.suscom.2023.100857
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Editorial:
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Elsevier
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Versión del editor:
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https://doi.org/10.1016/j.suscom.2023.100857
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Código del Proyecto:
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info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-122830OB-C42/ES/METODOS FORMALES ESCALABLES PARA APLICACIONES REALES/
...[+]
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-122830OB-C42/ES/METODOS FORMALES ESCALABLES PARA APLICACIONES REALES/
info:eu-repo/grantAgreement/EC/H2020/952215/EU/Integrating Reasoning, Learning and Optimization/
info:eu-repo/grantAgreement/GVA//PROMETEO%2F2019%2F098//DEEPTRUST/
info:eu-repo/grantAgreement/GVA//INNEST%2F2021%2F317/
info:eu-repo/grantAgreement/FLI//RFP2-152/
info:eu-repo/grantAgreement/DOD//HR00112120007/
info:eu-repo/grantAgreement/MIT//COST-OMIZE/
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Agradecimientos:
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We thank the reviewers for their insightful remarks, which have helped improve the paper significantly. This work has been partially supported by the MIT-Spain-INDITEX Sustainability Seed Fund under project COST-OMIZE, the ...[+]
We thank the reviewers for their insightful remarks, which have helped improve the paper significantly. This work has been partially supported by the MIT-Spain-INDITEX Sustainability Seed Fund under project COST-OMIZE, the grant PID2021-122830OB-C42 funded by MCIN/AEI/10.13039/501100011033 and "ERDF A way of making Europe", Generalitat Valenciana under INNEST/2021/317 and PROMETEO/2019/098, EU's Horizon 2020 research and innovation programme under grant agreement No. 952215 (TAILOR) , the Future of Life Institute, FLI, under grant RFP2-152, US DARPA HR00112120007 (RECoG-AI).
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Tipo:
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Artículo
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