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ISLPED '99 Proceedings of the 1999 international symposium on Low power electronics and design
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Proceedings of the 37th Annual Design Automation Conference
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ISLPED '00 Proceedings of the 2000 international symposium on Low power electronics and design
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ISLPED '01 Proceedings of the 2001 international symposium on Low power electronics and design
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Proceedings of the 39th annual Design Automation Conference
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Proceedings of the 2002 international symposium on Low power electronics and design
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PACS '00 Proceedings of the First International Workshop on Power-Aware Computer Systems-Revised Papers
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VLSID '03 Proceedings of the 16th International Conference on VLSI Design
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ISQED '01 Proceedings of the 2nd International Symposium on Quality Electronic Design
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IEEE Transactions on Very Large Scale Integration (VLSI) Systems
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IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Quality-Driven Proactive Computation Elimination for Power-Aware Multimedia Processing
Proceedings of the conference on Design, Automation and Test in Europe - Volume 1
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Proceedings of the 42nd annual Design Automation Conference
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Proceedings of the 2004 IEEE/ACM International conference on Computer-aided design
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ASP-DAC '03 Proceedings of the 2003 Asia and South Pacific Design Automation Conference
Power efficient co-simulation framework for a wireless application using platform based SoC
SAMOS'07 Proceedings of the 7th international conference on Embedded computer systems: architectures, modeling, and simulation
A precise high-level power consumption model for embedded systems software
EURASIP Journal on Embedded Systems
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SAMOS'05 Proceedings of the 5th international conference on Embedded Computer Systems: architectures, Modeling, and Simulation
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In this paper, we present a methodology and techniques for generating cycle-accurate macro-models for register transfer (RT)-level power analysis. The proposed macro-model predicts not only the cycle-by-cycle power consumption of a module, but also the moving average of power consumption and the power profile of the module over time. We propose an exact power function and approximation steps to generate our power macro-model. First-order temporal correlations and spatial correlations of up to order three are considered in order to improve the estimation accuracy. A variable reduction algorithm is designed to eliminate the "insignificant" variables using a statistical sensitivity test. Population stratification is employed to increase the model fidelity. Experimental results show our macro-models with 15 or fewer variables, exhibit