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Estimation of energy consumption in machine learning
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
University of Manchester, GBR.
University of Manchester, GBR.
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.ORCID-id: 0000-0001-9947-1088
2019 (Engelska)Ingår i: Journal of Parallel and Distributed Computing, ISSN 0743-7315, E-ISSN 1096-0848, Vol. 134, s. 75-88Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

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.

Ort, förlag, år, upplaga, sidor
Academic Press, 2019. Vol. 134, s. 75-88
Nyckelord [en]
Deep learning, Energy consumption, Green AI, High performance computing, Machine learning
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
URN: urn:nbn:se:bth-18650DOI: 10.1016/j.jpdc.2019.07.007ISI: 000489358200007OAI: oai:DiVA.org:bth-18650DiVA, id: diva2:1350558
Anmärkning

Funding text

Eva 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/.

Tillgänglig från: 2019-09-11 Skapad: 2019-09-11 Senast uppdaterad: 2019-12-03Bibliografiskt granskad
Ingår i avhandling
1. Energy Efficiency in Machine Learning: Approaches to Sustainable Data Stream Mining
Öppna denna publikation i ny flik eller fönster >>Energy Efficiency in Machine Learning: Approaches to Sustainable Data Stream Mining
2020 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
Abstract [en]

Energy efficiency in machine learning explores how to build machine learning algorithms and models with low computational and power requirements. Although energy consumption is starting to gain interest in the field of machine learning, still the majority of solutions focus on obtaining the highest predictive accuracy, without a clear focus on sustainability.

This thesis explores green machine learning, which builds on green computing and computer architecture to design sustainable and energy efficient machine learning algorithms. In particular, we investigate how to design machine learning algorithms that automatically learn from streaming data in an energy efficient manner.

We first illustrate how energy can be measured in the context of machine learning, in the form of a literature review and a procedure to create theoretical energy models. We use this knowledge to analyze the energy footprint of Hoeffding trees, presenting an energy model that maps the number of computations and memory accesses to the main functionalities of the algorithm. We also analyze the hardware events correlated to the execution of the algorithm, their functions and their hyper parameters.

The final contribution of the thesis is showcased by two novel extensions of Hoeffding tree algorithms, the Hoeffding tree with nmin adaptation and the Green Accelerated Hoeffding Tree. These solutions are able to reduce their energy consumption by twenty and thirty percent, with minimal effect on accuracy. This is achieved by setting an individual splitting criteria for each branch of the decision tree, spending more energy on the fast growing branches and saving energy on the rest.

This thesis shows the importance of evaluating energy consumption when designing machine learning algorithms, proving that we can design more energy efficient algorithms and still achieve competitive accuracy results.

Ort, förlag, år, upplaga, sidor
Karlskrona: Blekinge Tekniska Högskola, 2020. s. 267
Serie
Blekinge Institute of Technology Doctoral Dissertation Series, ISSN 1653-2090 ; 2
Nyckelord
machine learning, energy efficiency, data stream mining, green machine learning, edge computing
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
urn:nbn:se:bth-18986 (URN)978-91-7295-396-3 (ISBN)
Disputation
2020-01-31, Blekinge Institute of Technology, Karlskrona, 09:59 (Engelska)
Opponent
Handledare
Forskningsfinansiär
KK-stiftelsen, 20140032
Tillgänglig från: 2019-12-03 Skapad: 2019-12-03 Senast uppdaterad: 2019-12-04Bibliografiskt granskad

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