2026-06-24T19:46:37Zhttps://docta.ucm.es/rest/oai/request

oai:docta.ucm.es:20.500.14352/1237702025-09-08T23:58:57Zcom_20.500.14352_14col_20.500.14352_15

00925njm 22002777a 4500 dc Atienza, David author Zhu, Kai author Huang, Darong author Costero Valero, Luis María author 2025-08-13 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. 2168-2356 10.1109/MDAT.2025.3597956 https://hdl.handle.net/20.500.14352/123770 2168-2364 https://ieeexplore.ieee.org/document/11123443 A 20-Year Retrospective on Power and Thermal Modeling and Management