{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T17:02:04Z","timestamp":1771261324806,"version":"3.50.1"},"publisher-location":"Cham","reference-count":36,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030134525","type":"print"},{"value":"9783030134532","type":"electronic"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-13453-2_20","type":"book-chapter","created":{"date-parts":[[2019,2,15]],"date-time":"2019-02-15T06:33:53Z","timestamp":1550212433000},"page":"243-255","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["How to Measure Energy Consumption in Machine Learning Algorithms"],"prefix":"10.1007","author":[{"given":"Eva","family":"Garc\u00eda-Mart\u00edn","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Niklas","family":"Lavesson","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"H\u00e5kan","family":"Grahn","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Emiliano","family":"Casalicchio","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Veselka","family":"Boeva","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,2,16]]},"reference":[{"key":"20_CR1","unstructured":"Bellosa, F., Weissel, A., Waitz, M., Kellner, S.: Event-driven energy accounting for dynamic thermal management. In: Proceedings of the Workshop on Compilers and Operating Systems for Low Power, COLP 2003, vol. 22 (2003)"},{"key":"20_CR2","doi-asserted-by":"crossref","unstructured":"Bertran, R., Gonzalez, M., Martorell, X., Navarro, N., Ayguade, E.: Decomposable and responsive power models for multicore processors using performance counters. In: Proceedings of the 24th ACM International Conference on Supercomputing, pp. 147\u2013158. ACM (2010)","DOI":"10.1145\/1810085.1810108"},{"key":"20_CR3","doi-asserted-by":"crossref","unstructured":"Bifet, A., Gavalda, R.: Learning from time-changing data with adaptive windowing. In: Proceedings of the 2007 SIAM International Conference on Data Mining, pp. 443\u2013448. SIAM (2007)","DOI":"10.1137\/1.9781611972771.42"},{"key":"20_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1007\/978-3-642-03915-7_22","volume-title":"Advances in Intelligent Data Analysis VIII","author":"A Bifet","year":"2009","unstructured":"Bifet, A., Gavald\u00e0, R.: Adaptive learning from evolving data streams. In: Adams, N.M., Robardet, C., Siebes, A., Boulicaut, J.-F. (eds.) IDA 2009. LNCS, vol. 5772, pp. 249\u2013260. Springer, Heidelberg (2009). https:\/\/doi.org\/10.1007\/978-3-642-03915-7_22"},{"key":"20_CR5","doi-asserted-by":"crossref","unstructured":"Brooks, D., Tiwari, V., Martonosi, M.: Wattch: a framework for architectural-level power analysis and optimizations, vol. 28. ACM (2000)","DOI":"10.1145\/342001.339657"},{"issue":"3","key":"20_CR6","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1145\/268806.268810","volume":"25","author":"D Burger","year":"1997","unstructured":"Burger, D., Austin, T.M.: The simplescalar tool set, version 2.0. ACM SIGARCH Comput. Archit. News 25(3), 13\u201325 (1997)","journal-title":"ACM SIGARCH Comput. Archit. News"},{"issue":"1","key":"20_CR7","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1109\/JSSC.2016.2616357","volume":"52","author":"YH Chen","year":"2017","unstructured":"Chen, Y.H., Krishna, T., Emer, J.S., Sze, V.: Eyeriss: an energy-efficient reconfigurable accelerator for deep convolutional neural networks. IEEE J. Solid-State Circuits 52(1), 127\u2013138 (2017)","journal-title":"IEEE J. Solid-State Circuits"},{"key":"20_CR8","doi-asserted-by":"crossref","unstructured":"David, H., Gorbatov, E., Hanebutte, U.R., Khanna, R., Le, C.: RAPL: memory power estimation and capping. In: 2010 ACM\/IEEE International Symposium on Low-Power Electronics and Design (ISLPED), pp. 189\u2013194. IEEE (2010)","DOI":"10.1145\/1840845.1840883"},{"key":"20_CR9","unstructured":"Devices, E.E.: Watts up pro (2009)"},{"key":"20_CR10","doi-asserted-by":"crossref","unstructured":"Domingos, P., Hulten, G.: Mining high-speed data streams. In: Proceedings of 6th SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 71\u201380 (2000)","DOI":"10.1145\/347090.347107"},{"key":"20_CR11","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781139051224","volume-title":"Parallel Computer Organization and Design","author":"M Dubois","year":"2012","unstructured":"Dubois, M., Annavaram, M., Stenstr\u00f6m, P.: Parallel Computer Organization and Design. Cambridge University Press, Cambridge (2012)"},{"key":"20_CR12","unstructured":"Economou, D., Rivoire, S., Kozyrakis, C., Ranganathan, P.: Full-system power analysis and modeling for server environments. In: International Symposium on Computer Architecture-IEEE (2006)"},{"key":"20_CR13","unstructured":"Gilberto, C., Margaret, M.: Power prediction for intel XScale processors using performance monitoring unit events power prediction for intel XScale processors using performance monitoring unit events. In: ISLPED, vol. 5, pp. 8\u201310 (2005)"},{"key":"20_CR14","doi-asserted-by":"crossref","unstructured":"Goel, B., McKee, S.A.: A methodology for modeling dynamic and static power consumption for multicore processors. In: IPDPS, pp. 273\u2013282 (2016)","DOI":"10.1109\/IPDPS.2016.118"},{"key":"20_CR15","doi-asserted-by":"crossref","unstructured":"Goel, B., McKee, S.A., Gioiosa, R., Singh, K., Bhadauria, M., Cesati, M.: Portable, scalable, per-core power estimation for intelligent resource management. In: 2010 International Green Computing Conference, pp. 135\u2013146. IEEE (2010)","DOI":"10.1109\/GREENCOMP.2010.5598313"},{"key":"20_CR16","doi-asserted-by":"crossref","unstructured":"Goel, B., McKee, S.A., Sj\u00e4lander, M.: Techniques to measure, model, and manage power. In: Advances in Computers, vol. 87, pp. 7\u201354. Elsevier (2012)","DOI":"10.1016\/B978-0-12-396528-8.00002-X"},{"key":"20_CR17","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"20_CR18","volume-title":"Computer Architecture: A Quantitative Approach","author":"JL Hennessy","year":"2011","unstructured":"Hennessy, J.L., Patterson, D.A.: Computer Architecture: A Quantitative Approach. Elsevier, Amsterdam (2011)"},{"key":"20_CR19","doi-asserted-by":"crossref","unstructured":"Joseph, R., Martonosi, M.: Run-time power estimation in high performance microprocessors. In: Proceedings of the 2001 International Symposium on Low Power Electronics and Design, pp. 135\u2013140. ACM (2001)","DOI":"10.1145\/383082.383119"},{"issue":"3","key":"20_CR20","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1109\/MAHC.2010.28","volume":"33","author":"J Koomey","year":"2011","unstructured":"Koomey, J., Berard, S., Sanchez, M., Wong, H.: Implications of historical trends in the electrical efficiency of computing. IEEE Ann. Hist. Comput. 33(3), 46\u201354 (2011)","journal-title":"IEEE Ann. Hist. Comput."},{"key":"20_CR21","doi-asserted-by":"crossref","unstructured":"Lee, B.C., Brooks, D.M.: Accurate and efficient regression modeling for microarchitectural performance and power prediction. In: ACM SIGOPS Operating Systems Review, vol. 40, pp. 185\u2013194. ACM (2006)","DOI":"10.1145\/1168917.1168881"},{"key":"20_CR22","doi-asserted-by":"crossref","unstructured":"Li, S., Ahn, J.H., Strong, R.D., Brockman, J.B., Tullsen, D.M., Jouppi, N.P.: McPAT: an integrated power, area, and timing modeling framework for multicore and manycore architectures. In: 2009 42nd Annual IEEE\/ACM International Symposium on Microarchitecture. MICRO-42, pp. 469\u2013480. IEEE (2009)","DOI":"10.1145\/1669112.1669172"},{"key":"20_CR23","doi-asserted-by":"crossref","unstructured":"Mazouz, A., Wong, D.C., Kuck, D., Jalby, W.: An incremental methodology for energy measurement and modeling. In: Proceedings of the 8th ACM\/SPEC on International Conference on Performance Engineering, pp. 15\u201326. ACM (2017)","DOI":"10.1145\/3030207.3030224"},{"key":"20_CR24","unstructured":"Montiel, J., Read, J., Bifet, A., Abdessalem, T.: Scikit-multiflow: a multi-output streaming framework. CoRR abs\/1807.04662 (2018). https:\/\/github.com\/scikit-multiflow\/scikit-multiflow"},{"key":"20_CR25","unstructured":"Mucci, P.J., Browne, S., Deane, C., Ho, G.: PAPI: a portable interface to hardware performance counters. In: Proceedings of the Department of Defense HPCMP Users Group Conference, vol. 710 (1999)"},{"issue":"3","key":"20_CR26","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1145\/3078811","volume":"50","author":"K O\u2019brien","year":"2017","unstructured":"O\u2019brien, K., Pietri, I., Reddy, R., Lastovetsky, A., Sakellariou, R.: A survey of power and energy predictive models in HPC systems and applications. ACM Comput. Surv. (CSUR) 50(3), 37 (2017)","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"20_CR27","doi-asserted-by":"crossref","unstructured":"Rajamani, K., Hanson, H., Rubio, J., Ghiasi, S., Rawson, F.: Application-aware power management. In: 2006 IEEE International Symposium on Workload Characterization, pp. 39\u201348. IEEE (2006)","DOI":"10.1109\/IISWC.2006.302728"},{"issue":"2","key":"20_CR28","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1109\/MM.2012.12","volume":"32","author":"E Rotem","year":"2012","unstructured":"Rotem, E., Naveh, A., Ananthakrishnan, A., Weissmann, E., Rajwan, D.: Power-management architecture of the intel microarchitecture code-named sandy bridge. IEEE Micro 32(2), 20\u201327 (2012)","journal-title":"IEEE Micro"},{"issue":"9","key":"20_CR29","doi-asserted-by":"publisher","first-page":"998","DOI":"10.1109\/TCAD.2002.801105","volume":"21","author":"M Sami","year":"2002","unstructured":"Sami, M., Sciuto, D., Silvano, C., Zaccaria, V.: An instruction-level energy model for embedded vliw architectures. IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst. 21(9), 998\u20131010 (2002)","journal-title":"IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst."},{"key":"20_CR30","doi-asserted-by":"crossref","unstructured":"Shao, Y.S., Brooks, D.: Energy characterization and instruction-level energy model of Intel\u2019s Xeon Phi processor. In: Proceedings of the 2013 International Symposium on Low Power Electronics and Design, pp. 389\u2013394. IEEE Press (2013)","DOI":"10.1109\/ISLPED.2013.6629328"},{"issue":"2","key":"20_CR31","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1145\/1577129.1577137","volume":"37","author":"K Singh","year":"2009","unstructured":"Singh, K., Bhadauria, M., McKee, S.A.: Real time power estimation and thread scheduling via performance counters. ACM SIGARCH Comput. Archit. News 37(2), 46\u201355 (2009)","journal-title":"ACM SIGARCH Comput. Archit. News"},{"key":"20_CR32","doi-asserted-by":"crossref","unstructured":"Spiliopoulos, V., Sembrant, A., Kaxiras, S.: Power-sleuth: a tool for investigating your program\u2019s power behavior. In: 2012 IEEE 20th International Symposium on Modeling, Analysis & Simulation of Computer and Telecommunication Systems (MASCOTS), pp. 241\u2013250. IEEE (2012)","DOI":"10.1109\/MASCOTS.2012.36"},{"key":"20_CR33","doi-asserted-by":"crossref","unstructured":"Street, W.N., Kim, Y.: A streaming ensemble algorithm (SEA) for large-scale classification. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 377\u2013382. ACM (2001)","DOI":"10.1145\/502512.502568"},{"key":"20_CR34","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1007\/978-1-4613-1453-0_9","volume-title":"Technologies for Wireless Computing","author":"V Tiwari","year":"1996","unstructured":"Tiwari, V., Malik, S., Wolfe, A., Lee, M.T.C.: Instruction level power analysis and optimization of software. In: Chandrakasan, A.P., Brodersen, R.W. (eds.) Technologies for Wireless Computing, pp. 139\u2013154. Springer, Boston (1996). https:\/\/doi.org\/10.1007\/978-1-4613-1453-0_9"},{"key":"20_CR35","volume-title":"CMOS VLSI Design: A Circuits and Systems Perspective","author":"N Weste","year":"2010","unstructured":"Weste, N., Harris, D.: CMOS VLSI Design: A Circuits and Systems Perspective, 4th edn. Addison-Wesley, USA (2010). ISBN 0321547748, 9780321547743","edition":"4"},{"key":"20_CR36","doi-asserted-by":"crossref","unstructured":"Ye, W., Vijaykrishnan, N., Kandemir, M., Irwin, M.J.: The design and use of simplepower: a cycle-accurate energy estimation tool. In: Proceedings of the 37th Annual Design Automation Conference, pp. 340\u2013345. ACM (2000)","DOI":"10.1145\/337292.337436"}],"container-title":["Lecture Notes in Computer Science","ECML PKDD 2018 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-13453-2_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,15]],"date-time":"2024-02-15T01:04:43Z","timestamp":1707959083000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-13453-2_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030134525","9783030134532"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-13453-2_20","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"16 February 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Dublin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ireland","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 September 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 September 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ecmlpkdd2018.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"535","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"131","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"17","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"24% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}