RT Journal Article
T1 A 20-Year Retrospective on Power and Thermal Modeling and Management
A1 Atienza, David
A1 Zhu, Kai
A1 Huang, Darong
A1 Costero Valero, Luis María
AB 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.
PB IEEE
SN 2168-2356
YR 2025
FD 2025-08-13
LK https://hdl.handle.net/20.500.14352/123770
UL https://hdl.handle.net/20.500.14352/123770
LA eng
DS Docta Complutense
RD 14 jun 2026