This paper proposes an efficient Hybrid System Level (HSL) power estimation methodology for MPSoC. Within this methodology, the Functional Level Power Analysis (FLPA) is extended to set up generic power models for the different parts of the system. Then, a simulation framework is developed at the transactional level to evaluate accurately the activities used in the related power models. The combination of the above two parts lead to a hybrid power estimation that gives a better trade-off between accuracy and speed. The proposed methodology has several benefits: it considers the power consumption of the embedded system in its entirety and leads to accurate estimates without a costly and complex material. The proposed methodology is also scalable for exploring complex embedded architectures. The usefulness and effectiveness of our HSL methodology is validated through a typical mono-processor and multiprocessor embedded system designed around the Xilinx Virtex II Pro FPGA board.
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.
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