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Learning-Directed Dynamic Voltage and Frequency Scaling for Computation Time Prediction

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2 Author(s)
Ming-Feng Chang ; Dept. of Comput. Sci. & Inf. Eng., Nat. Taipei Univ. of Technol., Taipei, Taiwan ; Wen-Yew Liang

Dynamic voltage and frequency scaling (DVFS) is an effective technique for reducing power consumption. A number of DVFS researches apply learning methods in an attempt to approach the DVFS prediction model instead of using complicated mathematical models. In this paper, we propose a lightweight learning-directed DVFS technique using Counter Propagation Networks (CPN) to identify the task behavior and predict the corresponding voltage/frequency setting precisely. An adjustable performance mechanism is also provided to users that have diverse performance requirement. The algorithm has been implemented on the Linux operating system and used a PXA270 development board. The results show that the learning-directed DVFS method could accurately predict the suitable frequency, given runtime statistics information of a running program. In this way, the user can easily control the energy consumption by specifying allowable performance loss factor.

Published in:

Trust, Security and Privacy in Computing and Communications (TrustCom), 2011 IEEE 10th International Conference on

Date of Conference:

16-18 Nov. 2011