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Energy Efficiency in Machine Learning: A position paper
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.ORCID iD: 0000-0003-4973-9255
2017 (English)In: 30th Annual Workshop of the Swedish Artificial Intelligence Society SAIS 2017, May 15–16, 2017, Karlskrona, Sweden / [ed] Niklas Lavesson, Linköping: Linköping University Electronic Press, 2017, Vol. 137, 68-72 p.Conference paper, Published paper (Refereed)
Abstract [en]

Machine learning algorithms are usually evaluated and developed in terms of predictive performance. Since these types of algorithms often run on large-scale data centers, they account for a significant share of the energy consumed in many countries. This position paper argues for the reasons why developing energy efficient machine learning algorithms is of great importance.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2017. Vol. 137, 68-72 p.
Series
Linköping Electronic Conference Proceedings, ISSN 1650-3740 ; 137
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:bth-15533ISBN: 978-91-7685-496-9 OAI: oai:DiVA.org:bth-15533DiVA: diva2:1159323
Conference
30th Annual Workshop of the Swedish Artificial Intelligence Society SAIS, Karlskrona
Funder
Knowledge Foundation, 20140032
Available from: 2017-11-22 Created: 2017-11-22 Last updated: 2017-11-27Bibliographically approved

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fulltext(195 kB)69 downloads
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García Martín, Eva

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
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