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dc.contributor.authorFernández-Vega, Iván
dc.contributor.authorQuislant-del-Barrio, Ricardo 
dc.contributor.authorGonzález-Navarro, Sonia 
dc.contributor.authorGutiérrez-Carrasco, Eladio Damián 
dc.contributor.authorPlata-González, Óscar Guillermo 
dc.date.accessioned2022-08-30T11:18:44Z
dc.date.available2022-08-30T11:18:44Z
dc.date.issued2022-09
dc.identifier.citationIvan Fernandez, Ricardo Quislant, Sonia Gonzalez-Navarro, Eladio Gutierrez, Oscar Plata, TraTSA: A Transprecision Framework for Efficient Time Series Analysis, Journal of Computational Science, Volume 63, 2022, 101784, ISSN 1877-7503, https://doi.org/10.1016/j.jocs.2022.101784es_ES
dc.identifier.urihttps://hdl.handle.net/10630/24830
dc.description.abstractTime series analysis (TSA) comprises methods for extracting information in domains as diverse as medicine, seismology, speech recognition and economics. Matrix Profile (MP) is the state-of-the-art TSA technique, which provides the most similar neighbor to each subsequence of the time series. However, this computation requires a huge amount of floating-point (FP) operations, which are a major contributor ( 50%) to the energy consumption in modern computing platforms. In this sense, Transprecision Computing has recently emerged as a promising approach to improve energy efficiency and performance by using fewer bits in FP operations while providing accurate results. In this work, we present TraTSA, the first transprecision framework for efficient time series analysis based on MP. TraTSA allows the user to deploy a high-performance and energy-efficient computing solution with the exact precision required by the TSA application. To this end, we first propose implementations of TraTSA for both commodity CPU and FPGA platforms. Second, we propose an accuracy metric to compare the results with the double-precision MP. Third, we study MP’s accuracy when using a transprecision approach. Finally, our evaluation shows that, while obtaining results accurate enough, the FPGA transprecision MP (i) is 22.75 faster than a 72-core server, and (ii) the energy consumption is up to 3.3 lower than the double-precision executions.es_ES
dc.description.sponsorshipThis work has been supported by the Government of Spain under project PID2019-105396RB-I00, and Junta de Andalucia under projects P18-FR-3433 and UMA18-FEDERJA-197. Funding for open access charge: Universidad de Málaga / CBUA.es_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectAnálisis de series temporaleses_ES
dc.subject.otherTime series analysises_ES
dc.subject.otherTransprecision Computinges_ES
dc.subject.otherFloating-Point Unites_ES
dc.subject.otherFPGAes_ES
dc.subject.otherParallel architectureses_ES
dc.titleTraTSA: A Transprecision Framework for Efficient Time Series Analysises_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.centroE.T.S.I. Informáticaes_ES
dc.identifier.doihttps://doi.org/10.1016/j.jocs.2022.101784
dc.rights.ccAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.ccAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones_ES


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