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Sensors 2018, 18(9), 3068; https://doi.org/10.3390/s18093068

Learning-Directed Dynamic Voltage and Frequency Scaling Scheme with Adjustable Performance for Single-Core and Multi-Core Embedded and Mobile Systems

1
Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei 10608, Taiwan
2
MediaTek Inc., Hsinchu 30078, Taiwan
This paper is an expanded version of “Learning-Directed Dynamic Volt-age and Frequency Scaling for Computation Time Prediction” published in Proceedings of 2011 IEEE 10th International Conference on Trust, Security and Privacy in Computing and Communications, Changsha, China, 16–18 November 2011.
*
Author to whom correspondence should be addressed.
Received: 6 August 2018 / Revised: 6 September 2018 / Accepted: 8 September 2018 / Published: 12 September 2018
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Abstract

Dynamic voltage and frequency scaling (DVFS) is a well-known method for saving energy consumption. Several DVFS studies have applied learning-based methods to implement the DVFS prediction model instead of complicated mathematical models. This paper proposes a lightweight learning-directed DVFS method that involves using counter propagation networks to sense and classify the task behavior and predict the best voltage/frequency setting for the system. An intelligent adjustment mechanism for performance is also provided to users under various performance requirements. The comparative experimental results of the proposed algorithms and other competitive techniques are evaluated on the NVIDIA JETSON Tegra K1 multicore platform and Intel PXA270 embedded platforms. The results demonstrate that the learning-directed DVFS method can accurately predict the suitable central processing unit (CPU) frequency, given the runtime statistical information of a running program, and achieve an energy savings rate up to 42%. Through this method, users can easily achieve effective energy consumption and performance by specifying the factors of performance loss. View Full-Text
Keywords: dynamic voltage and frequency scaling (DVFS); embedded systems; energy consumption; low-power software design; multicore computing systems; mobile devices dynamic voltage and frequency scaling (DVFS); embedded systems; energy consumption; low-power software design; multicore computing systems; mobile devices
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Chen, Y.-L.; Chang, M.-F.; Yu, C.-W.; Chen, X.-Z.; Liang, W.-Y. Learning-Directed Dynamic Voltage and Frequency Scaling Scheme with Adjustable Performance for Single-Core and Multi-Core Embedded and Mobile Systems. Sensors 2018, 18, 3068.

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