DeepPower: Fast and scalable energy assessment of mobile sensing applications: Poster abstract

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dc.contributor.authorKim, Wonjungko
dc.contributor.authorChang, Youngjaeko
dc.contributor.authorSong, Junehwako
dc.date.accessioned2023-08-24T03:02:13Z-
dc.date.available2023-08-24T03:02:13Z-
dc.date.created2023-07-06-
dc.date.issued2020-11-
dc.identifier.citation18th ACM Conference on Embedded Networked Sensor Systems, SenSys 2020, pp.673 - 674-
dc.identifier.urihttp://hdl.handle.net/10203/311782-
dc.description.abstractEnergy-efficiency is a key performance metric of mobile sensing applications. However, assessment of energy-efficiency is greatly limited in practice. The main difficulty is that it requires assessment of power consumption in various user's real-life situation in the long term. This poster presents DeepPower, a system for assessing energy-efficiency of mobile sensing applications in fast and scalable manner. DeepPower introduces a sensor trace-based power use prediction technique, which significantly reduces the cost of assessing power consumption compared to existing power emulation techniques. Our experiments with three mobile sensing applications and five 1-hour-long sensor traces show that DeepPower can predict hardware usage of 1-hour-long sensor traces in less than a second, achieving average error rate of 4.6%.-
dc.languageEnglish-
dc.publisherAssociation for Computing Machinery, Inc-
dc.titleDeepPower: Fast and scalable energy assessment of mobile sensing applications: Poster abstract-
dc.typeConference-
dc.identifier.scopusid2-s2.0-85097555731-
dc.type.rimsCONF-
dc.citation.beginningpage673-
dc.citation.endingpage674-
dc.citation.publicationname18th ACM Conference on Embedded Networked Sensor Systems, SenSys 2020-
dc.identifier.conferencecountryJA-
dc.identifier.conferencelocationVirtual-
dc.identifier.doi10.1145/3384419.3430463-
dc.contributor.localauthorSong, Junehwa-
dc.contributor.nonIdAuthorKim, Wonjung-
dc.contributor.nonIdAuthorChang, Youngjae-
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CS-Conference Papers(학술회의논문)
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