Enhancing online power estimation accuracy for smartphones

Kim, Minyong; Kong, Joonho; Chung, Sung Woo

doi:10.1109/tce.2012.6227431

Cited by 30 publications

(22 citation statements)

References 5 publications

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“…Regression-based models, as is the case for PowerBooter (Zhang et al, 2010), the models by Dong et al, (Dong and Zhong, 2011), Kim et al (Kim et al, 2012), Kim et al (Kim et al, 2015), Shukla et al (Shukla et al, 2016), and Xu et al (Xu et al, 2013) amongst others, are quite popular. However, these models assume-by definition-that the variations in power consumption are linear causing an increased estimation error when it is not the case (Hoque et al, 2015).…”

Section: Model Constructionmentioning

confidence: 99%

A Novel Easy-to-construct Power Model for Embedded and Mobile Systems - Using Recursive Neural Nets to Estimate Power Consumption of ARM-based Embedded Systems and Mobile Devices

Djedidi

Djeziri

M'Sirdi

et al. 2018

Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics

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Abstract:This paper features a novel modeling scheme for power consumption in embedded and mobile devices. The model hereafter presented is built thought data fitting techniques using a NARX nonlinear neural net. It showcases the advantages of using a nonlinear model to estimate power consumption over the widely used linear regression models, where The NARX neural net is simpler, easier to implement, and more importantly more suitable as power changes are not always linear. Finally, experimental results validate the model with one with an accuracy of 97.1% on a smartphone.

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Section: Model Constructionmentioning

confidence: 99%

A Novel Easy-to-construct Power Model for Embedded and Mobile Systems - Using Recursive Neural Nets to Estimate Power Consumption of ARM-based Embedded Systems and Mobile Devices

Djedidi

Djeziri

M'Sirdi

et al. 2018

Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics

View full text Add to dashboard Cite

show abstract

“…Others track kernel queries to determine power consumption (Pathak et al, 2011). Other techniques, like the one presented in (Kim et al, 2012b;Kim and Chung, 2013;Kim et al, 2015) involve the construction of polynomial models to estimate the power consumption as a function of the frequency and of the load. In this case study, we use battery readings.…”

Section: Power Modelmentioning

confidence: 99%

“…For instance, Zhang et al propose a model called "PowerBooter" to the estimate power consumption of a smartphone through its battery sensors (Zhang et al, 2010). Another study used system calls to estimate the power consumptions (Pathak et al, 2011), while others used polynomial and regression models in a series of works to estimate the power consumption of smartphone components including the CPU and the GPU (Minyong Kim et al, 2012;Kim et al, 2012b;Kim and Chung, 2013;Kim et al, 2015). These studies offer simple modelling techniques and also show a clear dependence between frequency and power consumption.…”

Section: Introductionmentioning

confidence: 99%

Modular Modelling of an Embedded Mobile CPU-GPU Chip for Feature Estimation

Djedidi

Djeziri

M'Sirdi

et al. 2017

Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics

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Abstract:This paper deals with the modelling of a CPU-GPU chip embedded in an Android phone. The model is used for the estimation of variables that characterise the operating state of System on Chip (SoC). The proposed model is built to demonstrate the causal relationships between the variables, through its interconnected structure of subsystems. This structure allows the extension of other components or the easy exchange of subsystems in the case of a change in components or operating mode. The model developed here requires no additional instrumentation-other than the one present on the phone-which facilitates its implementation. It is used for the estimation of the state of the system and can also be used for monitoring and behaviour prediction. The model is validated and the results are promising for further implementation.

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“…For example, the power consumption differences betweenÛ a =0 andÛ a =0.8 at the lowest CPU frequency and the highest CPU frequency are 5.88 mW and 213.05 mW, respectively. This is because the CPU power consumption is a function of both the CPU frequency and the CPU load [22].…”

Section: Effect Of Cpu Frequency On Response Time and Power Consumptionmentioning

confidence: 99%

Response Time Constrained CPU Frequency and Priority Control Scheme for Improved Power Efficiency in Smartphones

Kyong

et al. 2017

IEICE Trans. Inf. & Syst.

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SUMMARYDynamic voltage and frequency scaling (DVFS) is an essential mechanism for power saving in smartphones and mobile devices. Central processing unit (CPU) load based DVFS algorithms are widely used due to their simplicity of implementation. However, such algorithms often lead to a poor response time, which is one of the most important factors of user experience, especially for interactive applications. In this paper, the response time is mathematically modeled by considering the CPU frequency and characteristics of the running applications based on the Linux kernel's completely fair scheduler (CFS), and a Response time constrained Frequency & Priority (RFP) control scheme for improved power efficiency of smartphones is proposed. In the RFP algorithm, the CPU frequency and priority of the interactive applications are adaptively adjusted by estimating the response time in real time. The experimental results show that RFP can save energy up to 24.23% compared to the ondemand governor and up to 7.74% compared to HAPPE while satisfying the predefined threshold of the response time in Android-based smartphones.

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Enhancing online power estimation accuracy for smartphones

Cited by 30 publications

References 5 publications

A Novel Easy-to-construct Power Model for Embedded and Mobile Systems - Using Recursive Neural Nets to Estimate Power Consumption of ARM-based Embedded Systems and Mobile Devices

A Novel Easy-to-construct Power Model for Embedded and Mobile Systems - Using Recursive Neural Nets to Estimate Power Consumption of ARM-based Embedded Systems and Mobile Devices

Modular Modelling of an Embedded Mobile CPU-GPU Chip for Feature Estimation

Response Time Constrained CPU Frequency and Priority Control Scheme for Improved Power Efficiency in Smartphones

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