An efficient power estimation methodology for complex RISC processor-based platforms

Abstract

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.

A simulation framework for code-level energy estimation of embedded soft-core processors

“…Another framework, called the Efficient Power Estimation Methodology (EPEM), develops a power/energy estimation virtual platform, which combines FLPA for hardware modeling and a system-level simulation technique for rapid prototyping and estimation. 20,21…”

“…Another framework, called the Efficient Power Estimation Methodology (EPEM), develops a power/ energy estimation virtual platform, which combines FLPA for hardware modeling and a system-level simulation technique for rapid prototyping and estimation. 20,21 PETS is a simulation-based tool to estimate, analyze, and optimize the power/energy consumption of an application running on complex state-of-the-art heterogeneous embedded processor-based platforms. 22 It consists of two parts where the first one is based on power/energy estimation using the FLPA-based modeling approach presented by Rethinagiri et al, 20 while the second part performs the application-code compilation and optimization based on the power estimation results.…”

“…20,21 PETS is a simulation-based tool to estimate, analyze, and optimize the power/energy consumption of an application running on complex state-of-the-art heterogeneous embedded processor-based platforms. 22 It consists of two parts where the first one is based on power/energy estimation using the FLPA-based modeling approach presented by Rethinagiri et al, 20 while the second part performs the application-code compilation and optimization based on the power estimation results. The authors claim to have an error of approximately 4% when targeting single-core softcore processors.…”

ParaDIME: Parallel Distributed Infrastructure for Minimization of Energy for data centers

“…This tool simplifies application porting as well as enables the user to choose the processor architecture upon which to perform hardware/software co-simulation. PETS was initially developed for the evaluation of MPSoC systems [7][8][9]. In ParaDIME, we have extended it to model a variety of other systems, including GPUs [10], DSPs, FPGAs [11] and multi-core (dual-and quad-core) ARM processors [12].…”

ParaDIME: Parallel Distributed Infrastructure for Minimization of Energy

“…This tool simplifies application porting as well as enables the user to choose the processor architecture upon which to perform hardware/software co-simulation. PETS was initially developed for the evaluation of MPSoC systems [10], [9], [11]. In ParaDIME, we have extended it to model a variety of other systems, including DSPs, FPGAs and multi-core (dual-and quad-core) ARM processors [13], [12], [15].…”