Runtime Power Estimation of Mobile AMOLED Displays

Kim, Dong-Won; Jung, Wonwoo; Cha, Hojung

doi:10.7873/date.2013.027

Cited by 19 publications

(13 citation statements)

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“…In their power model, they demonstrated the linear relationship between power consumption and sRGB value, and achieved 90% accuracy in display power estimation. Other power models for µOLED screens have also been proposed [10], [27], [28]. Unlike dLens, their work only built models for µOLED screens.…”

Section: Related Workmentioning

confidence: 99%

Detecting Display Energy Hotspots in Android Apps

Wan

Jin

et al. 2015

2015 IEEE 8th International Conference on Software Testing, Verification and Validation (ICST)

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Energy consumption of mobile apps has become an important consideration as the underlying devices are constrained by battery capacity. Display represents a significant portion of an app's energy consumption. However, developers lack techniques to identify the user interfaces in their apps for which energy needs to be improved. In this paper, we present a technique for detecting display energy hotspots -user interfaces of a mobile app whose energy consumption is greater than optimal. Our technique leverages display power modeling and automated display transformation techniques to detect these hotspots and prioritize them for developers. In an evaluation on a set of popular Android apps, our technique was very accurate in both predicting energy consumption and ranking the display energy hotspots. Our approach was also able to detect display energy hotspots in 398 Android market apps, showing its effectiveness and the pervasiveness of the problem. These results indicate that our approach represents a potentially useful technique for helping developers to detect energy related problems and reduce the energy consumption of their mobile apps.

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Section: Related Workmentioning

confidence: 99%

Detecting Display Energy Hotspots in Android Apps

Wan

Jin

et al. 2015

2015 IEEE 8th International Conference on Software Testing, Verification and Validation (ICST)

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“…ADEL (Automatic Detector of Energy Leaks) [19] uses an external power consumption meter to detect unnecessary network communication by tracing the indirect use of received data. However this, and other examples of works based on power consumption models [12], [20], [21], are not as powerful as the previously mentioned ones. SEMO [22] is a power monitoring system and application for Android smartphones which profiles application power usage based only on the battery discharge level, and unfortunately produces less reliable results due to this.…”

Section: Related Workmentioning

confidence: 83%

GreenDroid: A tool for analysing power consumption in the android ecosystem

Couto

Cunha

Fernandes

et al. 2015

2015 IEEE 13th International Scientific Conference on Informatics

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This paper presents GreenDroid, a tool for monitoring and analyzing power consumption for the Android ecosystem. This tool instruments the source code of a giving Android application and is able to estimate the power consumed when running it. Moreover, it uses advanced classification algorithms to detect abnormal power consumption and to relate them to fragments in the source code. A set of graphical results are produced that help software developers to identify abnormal power consumption in their source code. This growing awareness on energy efficiency is also changing the way programmers develop their software. As shown by recent empirical studies [1], software developers are more and more concerned on developing energy efficient software. Unfortunately, developing energy-aware software is still a difficult task. While the programming language community has developed advanced and widely-used software tools, such us debuggers and fault localization tools [2], memory profiler tools [3], [4], testing tools [5], [6], [7], benchmark and runtime monitoring frameworks [8], compiler optimizations [9], etc there are no equivalent tools/frameworks to profile/optimize power consumption. This paper presents a software tool, named GreenDroid 1 , for monitoring and analyzing power consumption for the Android ecosystem, one of the largest software ecosystems for mobile devices. This tool uses a power consumption model This work is financed by the FCT-Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within project UID/EEA/50014/2013.

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“…From the energy consumption standpoint, backlit displays contribute by their discrete backlight levels, whereas in OLED displays, each pixel contributes individually. Due to the characteristics of OLED displays [47], their overall energy consumption is greatly influenced by the actual content shown [48,49]. To reduce the energy consumption of OLED displays [48,49] propose contrast and color manipulation methods for user interface layouts.…”

Section: Optimization Targets For 3d Graphics Deployment On Mobile Dementioning

confidence: 99%

Optimization Techniques for 3D Graphics Deployment on Mobile Devices

Koskela

Vatjus-Anttila

2015

3D Res

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3D Internet technologies are becoming essential enablers in many application areas including games, education, collaboration, navigation and social networking. The use of 3D Internet applications with mobile devices provides location-independent access and richer use context, but also performance issues. Therefore, one of the important challenges facing 3D Internet applications is the deployment of 3D graphics on mobile devices. In this article, we present an extensive survey on optimization techniques for 3D graphics deployment on mobile devices and qualitatively analyze the applicability of each technique from the standpoints of visual quality, performance and energy consumption. The analysis focuses on optimization techniques related to data-driven 3D graphics deployment, because it supports off-line use, multiuser interaction, user-created 3D graphics and creation of arbitrary 3D graphics. The outcome of the analysis facilitates the development and deployment of 3D Internet applications on mobile devices and provides guidelines for future research. Graphical Abstract

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Runtime Power Estimation of Mobile AMOLED Displays

Cited by 19 publications

References 0 publications

Detecting Display Energy Hotspots in Android Apps

Detecting Display Energy Hotspots in Android Apps

GreenDroid: A tool for analysing power consumption in the android ecosystem

Optimization Techniques for 3D Graphics Deployment on Mobile Devices

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