2014 24th International Workshop on Power and Timing Modeling, Optimization and Simulation (PATMOS) 2014
DOI: 10.1109/patmos.2014.6951893
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Fast energy evaluation of embedded applications for many-core systems

Abstract: The growing concerns of energy efficiency and performance scalability motivate research in the area of manycore embedded systems. The software development of such systems plays an important role on the system performance, while accounting for a significant part of the total energy consumption. Thus, it becomes imperative to consider the software energy consumption at early stages of the software development. This paper proposes an instruction-driven energy analysis approach that provides an accurate and practi… Show more

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Cited by 14 publications
(9 citation statements)
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References 13 publications
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“…Authors in [14] present a fast energy evaluation of embedded applications for many-core systems. They use OVP to feed an instruction-driven energy model.…”
Section: Related Workmentioning
confidence: 99%
See 1 more Smart Citation
“…Authors in [14] present a fast energy evaluation of embedded applications for many-core systems. They use OVP to feed an instruction-driven energy model.…”
Section: Related Workmentioning
confidence: 99%
“…Like in [14] also this tool consists of the same two parts. The power model development and the system-level simulator with the power estimator kernel.…”
Section: Related Workmentioning
confidence: 99%
“…The processor EC related to the execution of a given task is a function of the number of executed instructions. In our model, the energy cost of each instruction is determined from a gate-level implementation of the processor, as proposed by Rosa et al [36].…”
Section: Energy Modelmentioning
confidence: 99%
“…The set of classes is defined as C = {c 0, c 2, … ,c 8 }, with 9 different classes (e.g. arithmetic, logic, branch) [36]. Results show that the error of adopted instruction analyzer module varies from 0.06% to 8.05% when compared to a gate-level implementation [36].…”
Section: Energy Modelmentioning
confidence: 99%
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