Parameter variations and impact on circuits and microarchitecture
Proceedings of the 40th annual Design Automation Conference
Architecture-Level Compact Thermal R-C Modeling
Architecture-Level Compact Thermal R-C Modeling
Compact thermal modeling for temperature-aware design
Proceedings of the 41st annual Design Automation Conference
Analytical Model for Sensor Placement on Microprocessors
ICCD '05 Proceedings of the 2005 International Conference on Computer Design
MiBench: A free, commercially representative embedded benchmark suite
WWC '01 Proceedings of the Workload Characterization, 2001. WWC-4. 2001 IEEE International Workshop
TACO: temperature aware clock-tree optimization
ICCAD '05 Proceedings of the 2005 IEEE/ACM International conference on Computer-aided design
Systematic temperature sensor allocation and placement for microprocessors
Proceedings of the 43rd annual Design Automation Conference
Temperature aware task scheduling in MPSoCs
Proceedings of the conference on Design, automation and test in Europe
MiDataSets: creating the conditions for a more realistic evaluation of Iterative optimization
HiPEAC'07 Proceedings of the 2nd international conference on High performance embedded architectures and compilers
Hotspot: acompact thermal modeling methodology for early-stage VLSI design
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Accurate direct and indirect on-chip temperature sensing for efficient dynamic thermal management
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems - Special section on the ACM IEEE international conference on formal methods and models for codesign (MEMOCODE) 2009
Integration, the VLSI Journal
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The main contribution of this work is an analytical model for finding the upper bound on the temperature difference among various locations on the die. The proposed model can be used in many applications, such as estimation of maximum temperature variations on the die and estimating the maximum placement error in temperature sensor placement algorithms. The model also identifies the conditions under which these maximum temperature variations might happen, which is very helpful for generating test data for thermal stress tests and for augmenting different benchmarks. Experiments show that maximum temperature differences can be underestimated as much as 9°C. Based on this model, a temperature sensor placement algorithm is also proposed which is able to guaranty a maximum temperature error due to placement of the sensor. The ability of the proposed model to estimate point to point maximum temperature difference can improve the efficiency and accuracy of the sensor placement technique so that we can reduce the number of thermal sensors needed by about 16% on average.