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How to Measure Energy Consumption in Machine Learning Algorithms
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.ORCID iD: 0000-0003-4973-9255
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.ORCID iD: 0000-0002-0535-1761
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.ORCID iD: 0000-0001-9947-1088
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.ORCID iD: 0000-0002-3118-5058
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2018 (English)In: Green Data Mining, International Workshop on Energy Efficient Data Mining and Knowledge Discovery: ECMLPKDD 2018: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases Workshops. Lecture Notes in Computer Science. Springer, Cham, 2018Conference paper, Published paper (Refereed)
Abstract [en]

Machine learning algorithms are responsible for a significant amount of computations. These computations are increasing with the advancements in different machine learning fields. For example, fields such as deep learning require algorithms to run during weeks consuming vast amounts of energy. While there is a trend in optimizing machine learning algorithms for performance and energy consumption, still there is little knowledge on how to estimate an algorithm’s energy consumption. Currently, a straightforward cross-platform approach to estimate energy consumption for different types of algorithms does not exist. For that reason, well-known researchers in computer architecture have published extensive works on approaches to estimate the energy consumption. This study presents a survey of methods to estimate energy consumption, and maps them to specific machine learning scenarios. Finally, we illustrate our mapping suggestions with a case study, where we measure energy consumption in a big data stream mining scenario. Our ultimate goal is to bridge the current gap that exists to estimate energy consumption in machine learning scenarios.

Place, publisher, year, edition, pages
2018.
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:bth-17209OAI: oai:DiVA.org:bth-17209DiVA, id: diva2:1260112
Conference
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Dublin
Funder
Knowledge Foundation, 20140032Available from: 2018-11-01 Created: 2018-11-01 Last updated: 2018-11-01Bibliographically approved

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garciamartin-measure-energy-ml(291 kB)46 downloads
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Authority records BETA

García Martín, EvaLavesson, NiklasGrahn, HåkanCasalicchio, EmilianoBoeva, Veselka

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1617181920212219 of 28
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