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dc.contributor.author | Chen, Longbin | es_ES |
dc.contributor.author | Qiu, Meikang | es_ES |
dc.contributor.author | Dai, Wenyun | es_ES |
dc.contributor.author | Hassan Mohamed, Houcine | es_ES |
dc.date.accessioned | 2020-07-15T03:32:10Z | |
dc.date.available | 2020-07-15T03:32:10Z | |
dc.date.issued | 2017-07 | es_ES |
dc.identifier.issn | 0141-9331 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/147999 | |
dc.description.abstract | [EN] The developments of wearable devices such as Body Sensor Networks (BSNs) have greatly improved the capability of tele-health industry. Large amount of data will be collected from every local BSN in real-time. These data is processed by embedded systems including smart phones and tablets. After that, the data will be transferred to distributed storage systems for further processing. Traditional on-chip SRAMs cause critical power leakage issues and occupy relatively large chip areas. Therefore, hybrid memories, which combine volatile memories with non-volatile memories, are widely adopted in reducing the latency and energy cost on multi-core systems. However, most of the current works are about static data allocation for hybrid memories. Those mechanisms cannot achieve better data placement in real-time. Hence, we propose online data allocation for hybrid memories on embedded tele-health systems. In this paper, we present dynamic programming and heuristic approaches. Considering the difference between profiled data access and actual data access, the proposed algorithms use a feedback mechanism to improve the accuracy of data allocation during runtime. Experimental results demonstrate that, compared to greedy approaches, the proposed algorithms achieve 20%-40% performance improvement based on different benchmarks. (C) 2016 Elsevier B.V. All rights reserved. | es_ES |
dc.description.sponsorship | This work is supported by NSF CNS-1457506 and NSF CNS-1359557. | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation.ispartof | Microprocessors and Microsystems | es_ES |
dc.rights | Reserva de todos los derechos | es_ES |
dc.subject | Hybrid memory | es_ES |
dc.subject | Non-volatile memory | es_ES |
dc.subject | Tele-health | es_ES |
dc.subject | Data allocation | es_ES |
dc.subject | Dynamic programming | es_ES |
dc.subject | Heuristic approach | es_ES |
dc.subject.classification | ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES | es_ES |
dc.title | Novel online data allocation for hybrid memories on tele-health systems | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1016/j.micpro.2016.08.003 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/NSF//1457506/US/EAGER: Towards Low-Latency Low-Power Heterogeneous Memory Access/ | |
dc.relation.projectID | info:eu-repo/grantAgreement/NSF//1359557/US/EAGER: Towards Low-Latency Low-Power Heterogeneous Memory Access/ | |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Informática de Sistemas y Computadores - Departament d'Informàtica de Sistemes i Computadors | es_ES |
dc.description.bibliographicCitation | Chen, L.; Qiu, M.; Dai, W.; Hassan Mohamed, H. (2017). Novel online data allocation for hybrid memories on tele-health systems. Microprocessors and Microsystems. 52:391-400. https://doi.org/10.1016/j.micpro.2016.08.003 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.1016/j.micpro.2016.08.003 | es_ES |
dc.description.upvformatpinicio | 391 | es_ES |
dc.description.upvformatpfin | 400 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 52 | es_ES |
dc.relation.pasarela | S\353285 | es_ES |
dc.contributor.funder | National Science Foundation, EEUU | es_ES |