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Numrering	Referens	Omslagsbild	Hitta
1.	García Martín, Eva, et al. (författare) Estimation of energy consumption in machine learning 2019 Ingår i: Journal of Parallel and Distributed Computing. - : Academic Press. - 0743-7315 .- 1096-0848. ; 134, s. 75-88 Tidskriftsartikel (refereegranskat)abstract Energy consumption has been widely studied in the computer architecture field for decades. While the adoption of energy as a metric in machine learning is emerging, the majority of research is still primarily focused on obtaining high levels of accuracy without any computational constraint. We believe that one of the reasons for this lack of interest is due to their lack of familiarity with approaches to evaluate energy consumption. To address this challenge, we present a review of the different approaches to estimate energy consumption in general and machine learning applications in particular. Our goal is to provide useful guidelines to the machine learning community giving them the fundamental knowledge to use and build specific energy estimation methods for machine learning algorithms. We also present the latest software tools that give energy estimation values, together with two use cases that enhance the study of energy consumption in machine learning.

García Martín, Eva, et al. (författare)
Estimation of energy consumption in machine learning
2019
Ingår i: Journal of Parallel and Distributed Computing. - : Academic Press. - 0743-7315 .- 1096-0848. ; 134, s. 75-88
Tidskriftsartikel (refereegranskat)abstract
- Energy consumption has been widely studied in the computer architecture field for decades. While the adoption of energy as a metric in machine learning is emerging, the majority of research is still primarily focused on obtaining high levels of accuracy without any computational constraint. We believe that one of the reasons for this lack of interest is due to their lack of familiarity with approaches to evaluate energy consumption. To address this challenge, we present a review of the different approaches to estimate energy consumption in general and machine learning applications in particular. Our goal is to provide useful guidelines to the machine learning community giving them the fundamental knowledge to use and build specific energy estimation methods for machine learning algorithms. We also present the latest software tools that give energy estimation values, together with two use cases that enhance the study of energy consumption in machine learning.

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