Estimation of energy consumption in machine learning

↓ Direkt till sidans innehåll
↓ Direkt till sidans sekundära innehåll (sidomenyn)

Sökning: L773:0743 7315 OR L773:1096 0848 > Estimation of energ...

LIBRIS Formathandbok (Information om MARC21)

Fältnamn	Indikatorer	Metadata
000		06358naa a2200421 4500
001		oai:DiVA.org:bth-18650
003		SwePub
008		190911s2019 \| \|\|\|\|\|\|\|\|\|\|\|000 \|\|eng\|
024	7	a https://urn.kb.se/resolve?urn=urn:nbn:se:bth-186502 URI
024	7	a https://doi.org/10.1016/j.jpdc.2019.07.0072 DOI
040		a (SwePub)bth
041		a engb eng
042		9 SwePub
072	7	a ref2 swepub-contenttype
072	7	a art2 swepub-publicationtype
100	1	a García Martín, Evau Blekinge Tekniska Högskola,Institutionen för datavetenskap4 aut0 (Swepub:bth)egx
245	1 0	a Estimation of energy consumption in machine learning
264	1	b Academic Press,c 2019
338		a electronic2 rdacarrier
500		a open accessFunding textEva García-Martín and Håkan Grahn work under the research project “Scalable resource-efficient systems for big data analytics” funded by the Knowledge Foundation (grant: 20140032 ) in Sweden. Crefeda Faviola Rodrigues and Graham Riley are funded under the European FP7-INFRASTRUCTURES-2012-1 call (grant: 312979 ) and part-funded by ARM Ltd., UK under a Ph.D. Studentship Agreement. Eva Garcia-Martin is a Ph.D. student in Machine Learning at Blekinge Institute of Technology, in Sweden. She is working under the project Scalable resource- efficient systems for big data analytics funded by the Knowledge Foundation, advised by Niklas Lavesson and Håkan Grahn. The main focus of her thesis is on making machine learning algorithms more energy efficient. In particular, she has studied the energy consumption patterns of streaming algorithms, and then proposed new algorithm extensions that reduce their energy consumption. Personal website: https://egarciamartin.github.io/. Crefeda Faviola Rodrigues is a Ph.D. student in Advanced Processor Technology (APT) group at The University of Manchester and she is supervised by Mr. Graham Riley and Dr. Mikel Lujan. Her research is part funded by ARM and IS-ENES2 Project. Her research topic is “Efficient execution of Convolutional Neural Networks on low power heterogeneous systems”. The main focus of her thesis is to enable energy efficiency in deep learning algorithms such as Convolutional Neural Networks or ConvNets on embedded platforms like the Jetson TX1 and Snapdragon 820. Personal website: https://personalpages.manchester.ac.uk/staff/crefeda.rodrigues/. Graham Riley is a Lecturer in the School of Computer Science at the University of Manchester and hold a part-time position in the Scientific Computing Department (SCD) at STFC, Daresbury. His research is application-driven and much of his research has been undertaken in collaboration with computational scientists in application areas such as Earth System Modeling (including the U.K. Met Office) and, previously, computational chemistry and biology. His aim is to apply his experience in high performance computing and software engineering for (principally) scientific computing to new application domains. He is also interested in techniques and tools to support flexible coupled modeling in scientific computing and in performance modeling techniques for large-scale heterogeneous HPC systems, where energy efficiency is increasingly key. Personal website: http://www.manchester.ac.uk/research/graham.riley/. Håkan Grahn is professor of computer engineering since 2007. He received a M.Sc. degree in Computer Science and Engineering in 1990 and a Ph.D. degree in Computer Engineering in 1995, both from Lund University. His main interests are computer architecture, multicore systems, GPU computing, parallel programming, image processing, and machine learning/data mining. He has published more than 100 papers on these subjects. During 1999–2002 he was head of department for the Dept. of software engineering and computer science, and during 2011–2013, he was Dean of research at Blekinge Institute of Technology. Currently he is project leader for BigData@BTH – “Scalable resource-efficient systems for big data analytics”, a research profile funded by the Knowledge foundation during 2014–2020. Personal website: https://www.bth.se/eng/staff/hakan-grahn-hgr/.
520		a 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.
650	7	a NATURVETENSKAPx Data- och informationsvetenskapx Datavetenskap0 (SwePub)102012 hsv//swe
650	7	a NATURAL SCIENCESx Computer and Information Sciencesx Computer Sciences0 (SwePub)102012 hsv//eng
653		a Deep learning
653		a Energy consumption
653		a Green AI
653		a High performance computing
653		a Machine learning
700	1	a Rodrigues, Crefeda Faviolau University of Manchester, GBR4 aut
700	1	a Riley, Grahamu University of Manchester, GBR4 aut
700	1	a Grahn, Håkanu Blekinge Tekniska Högskola,Institutionen för datavetenskap4 aut0 (Swepub:bth)hgr
710	2	a Blekinge Tekniska Högskolab Institutionen för datavetenskap4 org
773	0	t Journal of Parallel and Distributed Computingd : Academic Pressg 134, s. 75-88q 134<75-88x 0743-7315x 1096-0848
856	4	u https://doi.org/10.1016/j.jpdc.2019.07.007y Fulltext
856	4	u https://bth.diva-portal.org/smash/get/diva2:1350558/FULLTEXT01.pdfx primaryx Raw objecty fulltext:print
856	4	u https://doi.org/10.1016/j.jpdc.2019.07.007
856	4 8	u https://urn.kb.se/resolve?urn=urn:nbn:se:bth-18650
856	4 8	u https://doi.org/10.1016/j.jpdc.2019.07.007

Hitta via bibliotek

Journal of Parallel and Distributed Computing (Sök värdpublikationen i LIBRIS)

Till lärosätets databas

Hitta mer i SwePub

Av författaren/redakt...: García Martín, E ...; Rodrigues, Crefe ...; Riley, Graham; Grahn, Håkan

Om ämnet

NATURVETENSKAP: NATURVETENSKAP; och Data och informa ...; och Datavetenskap

Artiklar i publikationen: Journal of Paral ...

Av lärosätet: Blekinge Tekniska Högskola

Sök utanför SwePub

Sök vidare i:: Google; Google Book Search; Google Scholar

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.

LIBRIS.kb.se