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PETS: Power and energy estimation tool at system-level

Abstract : In this paper, we introduce PETS, a simulation based tool to estimate, analyse and optimize power/energy consumption of an application running on complex state-of-the-art heterogeneous embedded processor based platforms. This tool is integrated with power and energy models in order to support comprehensive design space exploration for low power multi-core and heterogeneous multiprocessor platforms such as OMAP, CARMA, Zynq 7000 and Virtex II Pro. Moreover, PETS is equipped with power optimization techniques such as dynamic slack reduction and work load balancing. The development of PETS involves two steps. First step: power model generation. For the power model development, functional-level parameters are used to set up generic power models for the different components of the system. So far, seven power models have been developed for different architectures, starting from the simple low power architecture ARM9 to the very complex DSP TI C64x. Second step: a simulation based virtual platform framework is developed using SystemC IP's and JIT/ISS compilers to accurately grab the activities to estimate power. The accuracy of our proposed tool is evaluated by using a variety of industrial benchmarks. Estimated power and energy values are compared to real board measurements. The power estimation results are less than 4% of error for single core processor, 4.6% for dual-core processor, 5% for quad-core, 6.8% multi-processor based system and effective optimisation of power/energy for the applications
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https://hal-uphf.archives-ouvertes.fr/hal-03663546
Contributor : Mylène Delrue Connect in order to contact the contributor
Submitted on : Tuesday, May 10, 2022 - 11:36:42 AM
Last modification on : Wednesday, May 11, 2022 - 3:48:00 AM

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Santhosh Kumar Rethinagiri, Oscar Palomar, Osman Unsal, Adrian Cristal, Rabie Ben Atitallah, et al.. PETS: Power and energy estimation tool at system-level. 2014 15th International Symposium on Quality Electronic Design (ISQED), Mar 2014, Santa Clara, United States. pp.535-542, ⟨10.1109/ISQED.2014.6783373⟩. ⟨hal-03663546⟩

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