An empirical validation of power-performance scaling: DVFS vs. multi-core scaling in big.LITTLE processor

Yoo, Seehwan

doi:10.1587/elex.12.20150236

Cited by 5 publications

(1 citation statement)

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“…To improve energy efficiency, platforms have been enhanced with Dynamic Voltage and Frequency Scaling (DVFS) [4], which can dynamically adjust the supply voltage and clock frequency of a processor to change task execution time and energy. In real-time application domains, approximated results obtained in time are preferred over accurate results obtained after the deadline.…”

Section: Introductionmentioning

confidence: 99%

Approximation-Aware Task Deployment on Heterogeneous Multicore Platforms With DVFS

Kritikakou

et al. 2023

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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Heterogeneous multi-core platforms, such as ARM big.LITTLE are widely used to execute embedded applications under multiple and contradictory constraints, such as energy consumption and real-time execution. To fulfill these constraints and optimize system performance, application tasks should be efficiently mapped on multi-core platforms. Embedded applications are usually tolerant to approximated results but acceptable Quality-of-Service (QoS). Modeling embedded applications by using the elastic task model, namely, Imprecise Computation (IC) task model, can balance system QoS, energy consumption, and real-time performance during task deployment. However, stateof-the-art approaches seldom consider the problem of IC task deployment on heterogeneous multi-core platforms. They typically neglect task migration, which can improve the solutions due to its flexibility during the task deployment process. This paper proposes a novel QoS-aware task deployment method to maximize system QoS under energy and real-time constraints, where frequency assignment, task allocation, scheduling, and migration are optimized simultaneously. The task deployment problem is formulated as mixed-integer non-linear programming. Then, it is linearized to mixed-integer linear programming to find the optimal solution. Furthermore, based on problem structure and problem decomposition, we propose a novel heuristic with low computational complexity. The sub-problems regarding frequency assignment, task allocation, scheduling, and adjustment are considered and solved in sequence. Finally, the simulation results show that the proposed task deployment method improves the system QoS by 31.2% on average (up to 112.8%) compared to the state-of-theart methods and the designed heuristic achieves about 53.9% (on average) performance of the optimal solution with a negligible computing time.

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Section: Introductionmentioning

confidence: 99%