Power analysis of embedded software: a first step towards software power minimization
IEEE Transactions on Very Large Scale Integration (VLSI) Systems - Special issue on low-power design
The design and use of simplepower: a cycle-accurate energy estimation tool
Proceedings of the 37th Annual Design Automation Conference
Wattch: a framework for architectural-level power analysis and optimizations
Proceedings of the 27th annual international symposium on Computer architecture
Proceedings of the conference on Design, automation and test in Europe - Volume 1
A power estimation methodology for systemC transaction level models
CODES+ISSS '05 Proceedings of the 3rd IEEE/ACM/IFIP international conference on Hardware/software codesign and system synthesis
PowerViP: Soc power estimation framework at transaction level
ASP-DAC '06 Proceedings of the 2006 Asia and South Pacific Design Automation Conference
Model Driven High-Level Power Estimation of Embedded Operating Systems Communication Services
ICESS '09 Proceedings of the 2009 International Conference on Embedded Software and Systems
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
System-level power estimation tool for embedded processor based platforms
Proceedings of the 6th Workshop on Rapid Simulation and Performance Evaluation: Methods and Tools
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In this contribution, we propose an efficient power estimation methodology for complex RISC processor-based platforms. 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 evaluate accurately the activities used in the related power models. The combination of the two parts above leads to a heterogeneous power estimation that gives a better trade-off between 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 benchmarks. Estimated power values are compared to real board measurements for the both ARM940T and ARM CortexA8 architectures. Our obtained power estimation results provide 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.