Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Temperature-aware microarchitecture
Proceedings of the 30th 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
Compact thermal modeling for temperature-aware design
Proceedings of the 41st annual Design Automation Conference
ICCAD '05 Proceedings of the 2005 IEEE/ACM International conference on Computer-aided design
A systematic method for functional unit power estimation in microprocessors
Proceedings of the 43rd annual Design Automation Conference
Architecture-level thermal behavioral characterization for multi-core microprocessors
Proceedings of the 2008 Asia and South Pacific Design Automation Conference
Parameterized architecture-level dynamic thermal models for multicore microprocessors
ACM Transactions on Design Automation of Electronic Systems (TODAES)
Power-thermal profiling of software applications
Microelectronics Journal
ACM Transactions on Design Automation of Electronic Systems (TODAES)
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In this paper, we propose a new architecture-level parameterized transient thermal behavioral modeling algorithm for emerging thermal related design and optimization problems for high-performance chip-multiprocessor (CMP) design. We propose a new approach, called ParThermPOF, to build the parameterized thermal performance models from the given architecture thermal and power information. The new method can include a number of parameters such as the locations of thermal sensors in a heat sink, different components (heat sink, heat spread, core, cache, etc.), thermal conductivity of heat sink materials, etc. The method consists of two steps: first, response surface method based on low-order polynomials is applied to build the parameterized models at each time point for all the given sampling nodes in the parameter space. Second, an improved generalized pencil-of-function (GPOF) method is employed to build the transfer-function based behavioral models for each time-varying coefficient of the polynomials generated in the first step. Experimental results on a practical quad-core microprocessor show that the generated parameterized thermal model matchs the given data very well. ParThermPOF is very suitable for design space exploration and optimization where both time and system parameters need to be considered.