Wattch: a framework for architectural-level power analysis and optimizations
Proceedings of the 27th annual international symposium on Computer architecture
Dynamic Thermal Management for High-Performance Microprocessors
HPCA '01 Proceedings of the 7th International Symposium on High-Performance Computer Architecture
Techniques for Multicore Thermal Management: Classification and New Exploration
Proceedings of the 33rd annual international symposium on Computer Architecture
Physical aware frequency selection for dynamic thermal management in multi-core systems
Proceedings of the 2006 IEEE/ACM international conference on Computer-aided design
Throughput of multi-core processors under thermal constraints
ISLPED '07 Proceedings of the 2007 international symposium on Low power electronics and design
Temperature-aware processor frequency assignment for MPSoCs using convex optimization
CODES+ISSS '07 Proceedings of the 5th IEEE/ACM international conference on Hardware/software codesign and system synthesis
Hotspot: acompact thermal modeling methodology for early-stage VLSI design
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Temperature and supply Voltage aware performance and power modeling at microarchitecture level
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Central vs. distributed dynamic thermal management for multi-core processors: which one is better?
Proceedings of the 19th ACM Great Lakes symposium on VLSI
Power-thermal profiling of software applications
Microelectronics Journal
Thermal-aware global real-time scheduling and analysis on multicore systems
Journal of Systems Architecture: the EUROMICRO Journal
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The objectives of this paper are (1) to develop a frequency planning methodology that maximizes the total performance of multi-core processors and that limits their maximum temperature as specified by the design constraints; and (2) to establish the implications of technology scaling on the performance limits of multi-core processors. Given the intricate designs and workloads of multi or many-core processors, it is computationally exhaustive to develop models that accurately calculate the temperature and performance of a given processor under various operating conditions. To abstract the underlying design complexity, we propose the use of supervised machine learning techniques to develop versatile models that capture the thermal characterization of multi-core processors under various input conditions and workloads. We then use the developed models to create a framework where various design constraints and objectives are expressed and solved using combinatorial optimization techniques. Using established power modeling and thermal simulation tools, we show that it is possible to boost the performance of multi-core processors by up to 11.4% at no impact to the maximum temperature.