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García Martín, Eva
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Publications (10 of 12) Show all publications
García Martín, E., Rodrigues, C. F., Riley, G. & Grahn, H. (2019). Estimation of energy consumption in machine learning. Journal of Parallel and Distributed Computing, 134, 75-88
Open this publication in new window or tab >>Estimation of energy consumption in machine learning
2019 (English)In: Journal of Parallel and Distributed Computing, ISSN 0743-7315, E-ISSN 1096-0848, Vol. 134, p. 75-88Article in journal (Refereed) 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.

Place, publisher, year, edition, pages
Academic Press, 2019
Keywords
Deep learning, Energy consumption, Green AI, High performance computing, Machine learning
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-18650 (URN)10.1016/j.jpdc.2019.07.007 (DOI)000489358200007 ()
Note

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/.

Available from: 2019-09-11 Created: 2019-09-11 Last updated: 2019-10-31Bibliographically approved
Lundberg, L., Lennerstad, H., Boeva, V. & García Martín, E. (2019). Handling non-linear relations in support vector machines through hyperplane folding. In: ACM International Conference Proceeding Series: . Paper presented at 11th International Conference on Machine Learning and Computing, ICMLC 2019; Zhuhai; China; 22 February 2019 through 24 February (pp. 137-141). Association for Computing Machinery
Open this publication in new window or tab >>Handling non-linear relations in support vector machines through hyperplane folding
2019 (English)In: ACM International Conference Proceeding Series, Association for Computing Machinery , 2019, p. 137-141Conference paper, Published paper (Refereed)
Abstract [en]

We present a new method, called hyperplane folding, that increases the margin in Support Vector Machines (SVMs). Based on the location of the support vectors, the method splits the dataset into two parts, rotates one part of the dataset and then merges the two parts again. This procedure increases the margin as long as the margin is smaller than half of the shortest distance between any pair of data points from the two different classes. We provide an algorithm for the general case with n-dimensional data points. A small experiment with three folding iterations on 3-dimensional data points with non-linear relations shows that the margin does indeed increase and that the accuracy improves with a larger margin. The method can use any standard SVM implementation plus some basic manipulation of the data points, i.e., splitting, rotating and merging. Hyperplane folding also increases the interpretability of the data. © 2019 Association for Computing Machinery.

Place, publisher, year, edition, pages
Association for Computing Machinery, 2019
Keywords
Hyperplane folding, Hyperplane hinging, Non-linear relations, Piecewise linear classification, Support vector machines, Geometry, Piecewise linear techniques, Vectors, Different class, Interpretability, Nonlinear relations, Piecewise linear, Support vector, Support vector machine (SVMs)
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-18039 (URN)10.1145/3318299.3318319 (DOI)000477981500023 ()2-s2.0-85066460409 (Scopus ID)
Conference
11th International Conference on Machine Learning and Computing, ICMLC 2019; Zhuhai; China; 22 February 2019 through 24 February
Available from: 2019-06-14 Created: 2019-06-14 Last updated: 2019-09-10Bibliographically approved
García Martín, E., Lavesson, N., Grahn, H., Casalicchio, E. & Boeva, V. (2019). How to Measure Energy Consumption in Machine Learning Algorithms. In: 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. Paper presented at 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 (pp. 243-255). , 11329
Open this publication in new window or tab >>How to Measure Energy Consumption in Machine Learning Algorithms
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2019 (English)In: 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, p. 243-255Conference 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.

Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 11329
Keywords
Computer architecture, Energy efficiency, Green computing, Machine learning
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-17209 (URN)10.1007/978-3-030-13453-2_20 (DOI)9783030134525 (ISBN)
Conference
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
Funder
Knowledge Foundation, 20140032
Available from: 2018-11-01 Created: 2018-11-01 Last updated: 2019-04-18Bibliographically approved
García Martín, E., Lavesson, N., Grahn, H., Casalicchio, E. & Boeva, V. (2018). Hoeffding Trees with nmin adaptation. In: The 5th IEEE International Conference on Data Science and Advanced Analytics (DSAA 2018): . Paper presented at 5th IEEE International Conference on Data Science and Advanced Analytics (IEEE DSAA), 1–4 October 2018, Turin (pp. 70-79). IEEE
Open this publication in new window or tab >>Hoeffding Trees with nmin adaptation
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2018 (English)In: The 5th IEEE International Conference on Data Science and Advanced Analytics (DSAA 2018), IEEE, 2018, p. 70-79Conference paper, Published paper (Refereed)
Abstract [en]

Machine learning software accounts for a significant amount of energy consumed in data centers. These algorithms are usually optimized towards predictive performance, i.e. accuracy, and scalability. This is the case of data stream mining algorithms. Although these algorithms are adaptive to the incoming data, they have fixed parameters from the beginning of the execution. We have observed that having fixed parameters lead to unnecessary computations, thus making the algorithm energy inefficient.In this paper we present the nmin adaptation method for Hoeffding trees. This method adapts the value of the nmin pa- rameter, which significantly affects the energy consumption of the algorithm. The method reduces unnecessary computations and memory accesses, thus reducing the energy, while the accuracy is only marginally affected. We experimentally compared VFDT (Very Fast Decision Tree, the first Hoeffding tree algorithm) and CVFDT (Concept-adapting VFDT) with the VFDT-nmin (VFDT with nmin adaptation). The results show that VFDT-nmin consumes up to 27% less energy than the standard VFDT, and up to 92% less energy than CVFDT, trading off a few percent of accuracy in a few datasets.

Place, publisher, year, edition, pages
IEEE, 2018
Series
Proceedings of the International Conference on Data Science and Advanced Analytics, ISSN 2472-1573
Keywords
data stream mining; green artificial intelligence; energy efficiency; hoeffding trees; energy aware machine learning
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-17207 (URN)10.1109/DSAA.2018.00017 (DOI)000459238600008 ()978-1-5386-5090-5 (ISBN)
Conference
5th IEEE International Conference on Data Science and Advanced Analytics (IEEE DSAA), 1–4 October 2018, Turin
Funder
Knowledge Foundation, 20140032
Available from: 2018-11-01 Created: 2018-11-01 Last updated: 2019-04-05Bibliographically approved
García Martín, E., Lavesson, N. & Grahn, H. (2017). Energy Efficiency Analysis of the Very Fast Decision Tree Algorithm. In: Rokia Missaoui, Talel Abdessalem, Matthieu Latapy (Ed.), Trends in Social Network Analysis: Information Propagation, User Behavior Modeling, Forecasting, and Vulnerability Assessment (pp. 229-252). Cham, Switzerland: Springer
Open this publication in new window or tab >>Energy Efficiency Analysis of the Very Fast Decision Tree Algorithm
2017 (English)In: Trends in Social Network Analysis: Information Propagation, User Behavior Modeling, Forecasting, and Vulnerability Assessment / [ed] Rokia Missaoui, Talel Abdessalem, Matthieu Latapy, Cham, Switzerland: Springer, 2017, p. 229-252Chapter in book (Refereed)
Abstract [en]

Data mining algorithms are usually designed to optimize a trade-off between predictive accuracy and computational efficiency. This paper introduces energy consumption and energy efficiency as important factors to consider during data mining algorithm analysis and evaluation. We conducted an experiment to illustrate how energy consumption and accuracy are affected when varying the parameters of the Very Fast Decision Tree (VFDT) algorithm. These results are compared with a theoretical analysis on the algorithm, indicating that energy consumption is affected by the parameters design and that it can be reduced significantly while maintaining accuracy.

Place, publisher, year, edition, pages
Cham, Switzerland: Springer, 2017
Series
Lectures Notes in Social Networks, ISSN 2190-5428
Keywords
Energy efficiency, Green computing, Very Fast Decision Tree, Big Data
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-15489 (URN)10.1007/978-3-319-53420-6_10 (DOI)978-3-319-53419-0 (ISBN)978-3-319-53420-6 (ISBN)
Funder
Knowledge Foundation, 20140032
Available from: 2017-11-14 Created: 2017-11-14 Last updated: 2018-02-02Bibliographically approved
García Martín, E. (2017). Energy Efficiency in Machine Learning: A position paper. In: Niklas Lavesson (Ed.), 30th Annual Workshop of the Swedish Artificial Intelligence Society SAIS 2017, May 15–16, 2017, Karlskrona, Sweden: . Paper presented at 30th Annual Workshop of the Swedish Artificial Intelligence Society SAIS, Karlskrona (pp. 68-72). Linköping: Linköping University Electronic Press, 137
Open this publication in new window or tab >>Energy Efficiency in Machine Learning: A position paper
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, p. 68-72Conference 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
Series
Linköping Electronic Conference Proceedings, ISSN 1650-3740 ; 137
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-15533 (URN)978-91-7685-496-9 (ISBN)
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: 2018-01-13Bibliographically approved
García Martín, E., Lavesson, N. & Grahn, H. (2017). Identification of Energy Hotspots: A Case Study of the Very Fast Decision Tree. In: Au M., Castiglione A., Choo KK., Palmieri F., Li KC. (Ed.), GPC 2017: Green, Pervasive, and Cloud Computing: . Paper presented at International Conference on Green, Pervasive and Cloud Computing (GPC), Cetara, Amalfi Coast, Italy (pp. 267-281). Cham, Switzerland: Springer, 10232
Open this publication in new window or tab >>Identification of Energy Hotspots: A Case Study of the Very Fast Decision Tree
2017 (English)In: GPC 2017: Green, Pervasive, and Cloud Computing / [ed] Au M., Castiglione A., Choo KK., Palmieri F., Li KC., Cham, Switzerland: Springer, 2017, Vol. 10232, p. 267-281Conference paper, Published paper (Refereed)
Abstract [en]

Large-scale data centers account for a significant share of the energy consumption in many countries. Machine learning technology requires intensive workloads and thus drives requirements for lots of power and cooling capacity in data centers. It is time to explore green machine learning. The aim of this paper is to profile a machine learning algorithm with respect to its energy consumption and to determine the causes behind this consumption. The first scalable machine learning algorithm able to handle large volumes of streaming data is the Very Fast Decision Tree (VFDT), which outputs competitive results in comparison to algorithms that analyze data from static datasets. Our objectives are to: (i) establish a methodology that profiles the energy consumption of decision trees at the function level, (ii) apply this methodology in an experiment to obtain the energy consumption of the VFDT, (iii) conduct a fine-grained analysis of the functions that consume most of the energy, providing an understanding of that consumption, (iv) analyze how different parameter settings can significantly reduce the energy consumption. The results show that by addressing the most energy intensive part of the VFDT, the energy consumption can be reduced up to a 74.3%.

Place, publisher, year, edition, pages
Cham, Switzerland: Springer, 2017
Series
Lecture Notes in Computer Science, ISSN 0302-9743
Keywords
Machine learning, Big data, Very Fast Decision Tree, Green machine learning, Data mining, Data stream mining
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-15490 (URN)10.1007/978-3-319-57186-7_21 (DOI)000426275000021 ()978-3-319-57185-0 (ISBN)978-3-319-57186-7 (ISBN)
Conference
International Conference on Green, Pervasive and Cloud Computing (GPC), Cetara, Amalfi Coast, Italy
Funder
Knowledge Foundation, 20140032
Available from: 2017-11-14 Created: 2017-11-14 Last updated: 2018-03-23Bibliographically approved
García Martín, E. & Lavesson, N. (2017). Is it ethical to avoid error analysis?. In: : . Paper presented at 2017 Workshop on Fairness, Accountability, and Transparency in Machine Learning (FAT/ML 2017), Halifax, Canada. arXiv
Open this publication in new window or tab >>Is it ethical to avoid error analysis?
2017 (English)Conference paper, Poster (with or without abstract) (Refereed)
Abstract [en]

Machine learning algorithms tend to create more accurate models with the availability of large datasets. In some cases, highly accurate models can hide the presence of bias in the data. There are several studies published that tackle the development of discriminatory-aware machine learning algorithms. We center on the further evaluation of machine learning models by doing error analysis, to understand under what conditions the model is not working as expected. We focus on the ethical implications of avoiding error analysis, from a falsification of results and discrimination perspective. Finally, we show different ways to approach error analysis in non-interpretable machine learning algorithms such as deep learning.

Place, publisher, year, edition, pages
arXiv, 2017
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-15534 (URN)
Conference
2017 Workshop on Fairness, Accountability, and Transparency in Machine Learning (FAT/ML 2017), Halifax, Canada
Funder
Knowledge Foundation, 20170032
Available from: 2017-11-22 Created: 2017-11-22 Last updated: 2018-01-13Bibliographically approved
Abghari, S., García Martín, E., Johansson, C., Lavesson, N. & Grahn, H. (2017). Trend analysis to automatically identify heat program changes. In: Energy Procedia: . Paper presented at 15th International Symposium on District Heating and Cooling (DHC2016), Seoul (pp. 407-415). Elsevier, 116
Open this publication in new window or tab >>Trend analysis to automatically identify heat program changes
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2017 (English)In: Energy Procedia, Elsevier, 2017, Vol. 116, p. 407-415Conference paper, Published paper (Refereed)
Abstract [en]

The aim of this study is to improve the monitoring and controlling of heating systems located at customer buildings through the use of a decision support system. To achieve this, the proposed system applies a two-step classifier to detect manual changes of the temperature of the heating system. We apply data from the Swedish company NODA, active in energy optimization and services for energy efficiency, to train and test the suggested system. The decision support system is evaluated through an experiment and the results are validated by experts at NODA. The results show that the decision support system can detect changes within three days after their occurrence and only by considering daily average measurements.

Place, publisher, year, edition, pages
Elsevier, 2017
Series
Energy Procedia, ISSN 1876-6102 ; 116
Keywords
District heating, Trend analysis, Change detection, Smart automated system
National Category
Computer Systems
Identifiers
urn:nbn:se:bth-12894 (URN)10.1016/j.egypro.2017.05.088 (DOI)000406743000039 ()
Conference
15th International Symposium on District Heating and Cooling (DHC2016), Seoul
Projects
BigData@BTH
Funder
Knowledge Foundation, 20140032
Note

Open access

Available from: 2016-09-26 Created: 2016-07-13 Last updated: 2018-10-12Bibliographically approved
Martin, E. G., Lavesson, N. & Doroud, M. (2016). Hashtags and followers: An experimental study of the online social network Twitter. SOCIAL NETWORK ANALYSIS AND MINING, 6(1), Article ID UNSP 12.
Open this publication in new window or tab >>Hashtags and followers: An experimental study of the online social network Twitter
2016 (English)In: SOCIAL NETWORK ANALYSIS AND MINING, ISSN 1869-5450, Vol. 6, no 1, article id UNSP 12Article in journal (Refereed) Published
Abstract [en]

We have conducted an analysis of data from 502,891 Twitter users and focused on investigating the potential correlation between hashtags and the increase of followers to determine whether the addition of hashtags to tweets produces new followers. We have designed an experiment with two groups of users: one tweeting with random hashtags and one tweeting without hashtags. The results showed that there is a correlation between hashtags and followers: on average, users tweeting with hashtags increased their followers by 2.88, while users tweeting without hashtags increased 0.88 followers. We present a simple, reproducible approach to extract and analyze Twitter user data for this and similar purposes.

Place, publisher, year, edition, pages
Springer, 2016
Keywords
Experimental study, Correlational analysis, Hashtags, Followers
National Category
Media and Communication Technology Other Computer and Information Science
Identifiers
urn:nbn:se:bth-13048 (URN)10.1007/s13278-016-0320-6 (DOI)000381220500012 ()
Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2018-01-14Bibliographically approved
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