A 20-Year Retrospective on Power and Thermal Modeling and Management

As processor performance advances, increasing power densities and complex thermal behaviors threaten both energy efficiency and system reliability. This survey covers more than two decades of research on power and thermal modeling and management in modern processors. We start by comparing analytical...

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Detalles Bibliográficos
Autores: Atienza, David, Zhu, Kai, Huang, Darong, Costero Valero, Luis María
Tipo de recurso: artículo
Fecha de publicación:2025
País:España
Institución:Universidad Complutense de Madrid (UCM)
Repositorio:Docta Complutense
Idioma:inglés
OAI Identifier:oai:docta.ucm.es:20.500.14352/123770
Acceso en línea:https://hdl.handle.net/20.500.14352/123770
Access Level:acceso abierto
Palabra clave:Hardware
3304.06 Arquitectura de Ordenadores
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spelling A 20-Year Retrospective on Power and Thermal Modeling and ManagementAtienza, DavidZhu, KaiHuang, DarongCostero Valero, Luis MaríaHardware3304.06 Arquitectura de OrdenadoresAs processor performance advances, increasing power densities and complex thermal behaviors threaten both energy efficiency and system reliability. This survey covers more than two decades of research on power and thermal modeling and management in modern processors. We start by comparing analytical, regression-based, and neural network-based techniques for power estimation, then review thermal modeling methods, including finite element, finite difference, and data-driven approaches. Next, we categorize dynamic runtime management strategies that balance performance, power consumption, and reliability. Finally, we conclude with a discussion of emerging challenges and promising research directions.IEEEUniversidad Complutense de Madrid20252025-08-1320252025-08-13journal articlehttp://purl.org/coar/resource_type/c_6501info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/20.500.14352/123770reponame:Docta Complutenseinstname:Universidad Complutense de Madrid (UCM)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:docta.ucm.es:20.500.14352/1237702026-06-02T12:44:21Z
dc.title.none.fl_str_mv A 20-Year Retrospective on Power and Thermal Modeling and Management
title A 20-Year Retrospective on Power and Thermal Modeling and Management
spellingShingle A 20-Year Retrospective on Power and Thermal Modeling and Management
Atienza, David
Hardware
3304.06 Arquitectura de Ordenadores
title_short A 20-Year Retrospective on Power and Thermal Modeling and Management
title_full A 20-Year Retrospective on Power and Thermal Modeling and Management
title_fullStr A 20-Year Retrospective on Power and Thermal Modeling and Management
title_full_unstemmed A 20-Year Retrospective on Power and Thermal Modeling and Management
title_sort A 20-Year Retrospective on Power and Thermal Modeling and Management
dc.creator.none.fl_str_mv Atienza, David
Zhu, Kai
Huang, Darong
Costero Valero, Luis María
author Atienza, David
author_facet Atienza, David
Zhu, Kai
Huang, Darong
Costero Valero, Luis María
author_role author
author2 Zhu, Kai
Huang, Darong
Costero Valero, Luis María
author2_role author
author
author
dc.contributor.none.fl_str_mv Universidad Complutense de Madrid
dc.subject.none.fl_str_mv Hardware
3304.06 Arquitectura de Ordenadores
topic Hardware
3304.06 Arquitectura de Ordenadores
description As processor performance advances, increasing power densities and complex thermal behaviors threaten both energy efficiency and system reliability. This survey covers more than two decades of research on power and thermal modeling and management in modern processors. We start by comparing analytical, regression-based, and neural network-based techniques for power estimation, then review thermal modeling methods, including finite element, finite difference, and data-driven approaches. Next, we categorize dynamic runtime management strategies that balance performance, power consumption, and reliability. Finally, we conclude with a discussion of emerging challenges and promising research directions.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025-08-13
2025
2025-08-13
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.14352/123770
url https://hdl.handle.net/20.500.14352/123770
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv IEEE
publisher.none.fl_str_mv IEEE
dc.source.none.fl_str_mv reponame:Docta Complutense
instname:Universidad Complutense de Madrid (UCM)
instname_str Universidad Complutense de Madrid (UCM)
reponame_str Docta Complutense
collection Docta Complutense
repository.name.fl_str_mv
repository.mail.fl_str_mv
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