Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
How to Measure Energy Consumption in Machine Learning Algorithms
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.ORCID-id: 0000-0003-4973-9255
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.ORCID-id: 0000-0002-0535-1761
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.ORCID-id: 0000-0001-9947-1088
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.ORCID-id: 0000-0002-3118-5058
Vise andre og tillknytning
2019 (engelsk)Inngår i: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): ECMLPKDD 2018: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases Workshops. Lecture Notes in Computer Science. Springer, Cham, 2019, Vol. 11329, s. 243-255Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
2019. Vol. 11329, s. 243-255
Serie
Lecture Notes in Computer Science, ISSN 0302-9743 ; 11329
Emneord [en]
Computer architecture, Energy efficiency, Green computing, Machine learning
HSV kategori
Identifikatorer
URN: urn:nbn:se:bth-17209DOI: 10.1007/978-3-030-13453-2_20ISBN: 9783030134525 (tryckt)OAI: oai:DiVA.org:bth-17209DiVA, id: diva2:1260112
Konferanse
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2018; Dublin; Ireland; 10 September 2018 through 14 September 2018
Forskningsfinansiär
Knowledge Foundation, 20140032Tilgjengelig fra: 2018-11-01 Laget: 2018-11-01 Sist oppdatert: 2019-04-18bibliografisk kontrollert

Open Access i DiVA

garciamartin-measure-energy-ml(291 kB)143 nedlastinger
Filinformasjon
Fil FULLTEXT01.pdfFilstørrelse 291 kBChecksum SHA-512
596db4dbcc90f6f753a0401adc66ca29b8bce89ae927334a3935fa9dc927191b31ee2cddc122964d6f68bbd717931e255e33c4e3ca32e7a45369557fe850e938
Type fulltextMimetype application/pdf

Andre lenker

Forlagets fulltekst

Personposter BETA

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

Søk i DiVA

Av forfatter/redaktør
García Martín, EvaLavesson, NiklasGrahn, HåkanCasalicchio, EmilianoBoeva, Veselka
Av organisasjonen

Søk utenfor DiVA

GoogleGoogle Scholar
Totalt: 143 nedlastinger
Antall nedlastinger er summen av alle nedlastinger av alle fulltekster. Det kan for eksempel være tidligere versjoner som er ikke lenger tilgjengelige

doi
isbn
urn-nbn

Altmetric

doi
isbn
urn-nbn
Totalt: 473 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf