An Efficient Power Estimation Methodology for Complex RISC Processor-based Platforms

Santhosh Kumar Rethinagiri 1, 2 Rabie Ben Atitallah 1 Jean-Luc Dekeyser 2 Smail Niar 1 Eric Senn 3
2 DART - Contributions of the Data parallelism to real time
LIFL - Laboratoire d'Informatique Fondamentale de Lille, Inria Lille - Nord Europe
3 Lab-STICC_UBS_CACS_MOCS
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
Abstract : In this contribution, we propose an efficient power estima- tion methodology for complex RISC processor-based plat- forms. In this methodology, the Functional Level Power Analysis (FLPA) is used to set up generic power models for the different parts of the system. Then, a simulation framework based on virtual platform is developed to evalu- ate accurately the activities used in the related power mod- els. The combination of the two parts above leads to a het- erogeneous power estimation that gives a better trade-off be- tween accuracy and speed. The usefulness and effectiveness of our proposed methodology is validated through ARM9 and ARM CortexA8 processor designed respectively around the OMAP5912 and OMAP3530 boards. This efficiency and the accuracy of our proposed methodology is evaluated by using a variety of basic programs to complete media bench- marks. Estimated power values are compared to real board measurements for the both ARM940T and ARM CortexA8 architectures. Our obtained power estimation results pro- vide less than 3% of error for ARM940T processor, 3.5% for ARM CortexA8 processor-based system and 1x faster compared to the state-of-the-art power estimation tools.
Type de document :
Communication dans un congrès
Joseph Cavallaro. GLSVLSI 2012, May 2012, Salt lake city, UTAH, United States. ACM, 2012
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Santhosh Kumar Rethinagiri, Rabie Ben Atitallah, Jean-Luc Dekeyser, Smail Niar, Eric Senn. An Efficient Power Estimation Methodology for Complex RISC Processor-based Platforms. Joseph Cavallaro. GLSVLSI 2012, May 2012, Salt lake city, UTAH, United States. ACM, 2012. 〈hal-00675469〉

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