LMS-based low-complexity game workload prediction for DVFS

Benedikt Dietrich, Swaroop Nunna, Dip Goswami, Samarjit Chakraborty, Matthias Gries

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

25 Citaten (Scopus)


While dynamic voltage and frequency scaling (DVFS) based power management has been widely studied for video processing, there is very little work on game power management. Recent work on proportional-integral-derivative (PID) controllers fro predicting game workload used hand-turned PID controller gains on relatively short game plays. This left open questions on the robustness of the PID controller and how sensitive the prediction quality is on the choice of the gain values, especially for long game plays involving different scenarios and scene changes. In this paper we propose a Least Mean Squares (LMS) Linear Predictor, which is a regression model commonly used for system parameter identification. Our results show that game workload variation can be estimated using a linear-in-parameters (LIP) model. This observation dramatically reduces the complexity of parameter estimation as the LMS Linear Predictor learns the relevant parameters of the model iteratively as the game progresses. The only parameter to be tuned by the system designer is the learning rate, which is relatively straightforward. Our experimental results using the LMS Linear Predictor show comparable power savings and game quality with those obtained from a highly-tuned PID controller.
Originele taal-2Engels
Titel2010 IEEE International Conference on Computer Design
Plaats van productiePiscataway
UitgeverijInstitute of Electrical and Electronics Engineers
Aantal pagina's8
ISBN van elektronische versie978-1-4244-8937-4
ISBN van geprinte versie978-1-4244-8936-7
StatusGepubliceerd - 2010
Extern gepubliceerdJa
EvenementInternational Conference on Computer Design - Amsterdam, Nederland
Duur: 3 okt 20106 okt 2010


CongresInternational Conference on Computer Design
Verkorte titelICCD 2010

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  • Citeer dit

    Dietrich, B., Nunna, S., Goswami, D., Chakraborty, S., & Gries, M. (2010). LMS-based low-complexity game workload prediction for DVFS. In 2010 IEEE International Conference on Computer Design (blz. 417-424). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICCD.2010.5647675