During Internet streaming, a significant portion of the battery power is always consumed by the display panel on mobile devices. To reduce the display power consumption, backlight scaling, a scheme that intelligently dims the backlight has been proposed. To maintain perceived video appearance in backlight scaling, a computationally intensive luminance compensation process is required. However, this step, if performed by the CPU as existing schemes suggest, could easily offset the power savings gained from backlight scaling. Furthermore, computing the optimal backlight scaling values requires per-frame luminance information, which is typically too energy intensive for mobile devices to compute. Thus, existing schemes require such information to be available in advance. And such an offline approach makes these schemes impractical. To address these challenges, in this paper, we design and implement GoCAD, a GPU-assisted Online Content-Adaptive Display power saving scheme for mobile devices in Internet streaming sessions. In GoCAD, we employ the mobile device's GPU rather than the CPU to reduce power consumption during the luminance compensation phase. Furthermore, we compute the optimal backlight scaling values for small batches of video frames in an online fashion using a dynamic programming algorithm. Lastly, we make novel use of the widely available video storyboard, a pre-computed set of thumbnails associated with a video, to intelligently decide whether or not to apply our backlight scaling scheme for a given video. For example, when the GPU power consumption would offset the savings from dimming the backlight, no backlight scaling is conducted. To evaluate the performance of GoCAD, we implement a prototype within an Android application and use a Monsoon power monitor to measure the real power consumption. Experiments are conducted on more than 460 randomly selected YouTube videos. Results show that GoCAD can effectively produce power savings without affecting rendered video quality.

Backlight scaling is a technique proposed to reduce the display panel power consumption by strategically dimming the backlight. However, for mobile video applications, a computationally intensive luminance compensation step must be performed in combination with backlight scaling to maintain the perceived appearance of video frames. This step, if done by the Central Processing Unit (CPU), could easily offset the power savings via backlight dimming. Furthermore, computing the backlight scaling values requires per-frame luminance information, which is typically too energy intensive to compute on mobile devices. In this article, we propose Content-Adaptive Display (CAD) for two typical Internet mobile video applications: video streaming and real-time video communication. CAD uses the mobile device’s Graphics Processing Unit (GPU) rather than the CPU to perform luminance compensation at reduced power consumption. For video streaming where video frames are available in advance, we compute the backlight scaling schedule using a more efficient dynamic programming algorithm than existing work. For real-time video communication where video frames are generated on the fly, we propose a greedy algorithm to determine the backlight scaling at runtime. We implement CAD in one video streaming application and one real-time video call application on the Android platform and use a Monsoon power meter to measure the real power consumption. Experiment results show that CAD can save more than 10% overall power consumption for up to 55.7% videos during video streaming and up to 31.0% overall power consumption in real-time video calls.

Backlight scaling is a technique proposed to reduce the display panel power consumption by strategically dimming the backlight. However, for Internet streaming to mobile devices, a computationally intensive luminance compensation step must be performed in combination with backlight scaling to maintain the perceived appearance of video frames. This step, if done by the CPU, could easily offset the power savings via backlight dimming. Furthermore, computing the backlight scaling values requires per-frame luminance information, which is typically too energy intensive to compute on mobile devices.

In this paper, we research on an interdisciplinary field of how the Smart Grid can help energy cost reduction in wireless communications. We study how the advances in the Smart Grid such as the information on predicted electricity price can be used to help reduce the energy cost of the cellular networks. Particularly, smart energy management at cell-sites is studied to minimize the electricity cost for mobile operators by leveraging time-varying electricity prices in the Smart Grid. We first propose a theoretical framework to optimally manage energy at a single cell-site (without and with energy harvester, respectively) by judiciously choosing which power source (the Power-Grid or a storage-battery) to use, when and how much to use in the cell-site; as well as controlling when and how much to charge the storage-battery. Moreover, we extend the framework from a single cell-site to a network including multiple cell-sites with different electricity prices or energy storage capacity, etc., where cell association, power control, and scheduling can be jointly considered to provide load shifting and cell ON/OFF to achieve mobile network operation with the lowest electricity cost.

In this paper, we presented our recent characterization and analysis on the power consumption of smartphone radio components, including Wi-Fi, GPS and cellular (3G/4G) modules. Different from previous research that focused on the properties of single module under a limited number of usage scenarios, our works are performed on the statistically selected representative Apps with four generations of Samsung Galaxy series smartphones. We found that over the four characterized generations, the average power consumption of the smartphones with a connected Wi-Fi network on the selected Apps increases about 38.20%, indicating more and more energy-hungry designs. However, the raising of the power consumption is not mainly from the radio modules as the power efficiency of the Wi-Fi module indeed improves by about 21%. The 4G module consumes about 15.13% more power compared to the latest 3G module. But the power efficiency of 3G module improves by averagely 15.17% over the four generations. The GPS module also shows 25.80% power consumption improvement over the generations. We also show that the characterized power results can be combined with the real user traces for detailed daily energy usage estimation. Our research established an ethological and evolutionary power consumption trend of smartphones, which will guide the power optimization in the next-generation smartphone design.

Energy accounting determines how much a software principal contributes to the total system energy consumption. It is the foundation for evaluating software and for operating system based energy management. While various energy accounting policies have been tried, there is no known way to evaluate them directly simply because it is hard to track all hardware usage by software in a heterogeneous multicore system like modern smartphones and tablets.

Power trace of a smartphone, as time series data, carries important information of the system behavior and is useful for many applications, such as energy management [1-3], software optimization [4-6] and anomaly detection [7, 8]. However, the power trace measured from the battery terminals include the power consumption by all the hardware components and thus describes the activity of the whole system. Yet modern smartphones are multiprocessing, i.e., multiple applications can be running simultaneously in the same system. Our goal is to answer the following question: “Can we identify smartphone app by power trace?” That is, whether the power trace of a smartphone can be different by running different applications.

No abstract available.

The ability to account system resource usage by software is the key to the design and optimization of modern computer systems. For example, scheduling and memory management are two classic operating system (OS) functions based on the ability to account the CPU and memory usage by process. Energy has become an important system resource due to electricity and thermal concerns. This is particularly true for mobile systems that are battery-powered and require compact form factors. Knowing the energy contribution by a process, or per-process energy accounting, is the foundation for OS energy management and optimization [11, 9], incentive mechanisms for emerging applications in participatory sensing and cooperative communication, detecting rogue applications [8], and software optimization for energy [6].

Mobile video streaming has become one of the most popular applications in the trend of smartphone booming and the prevalence of 3G/4G networks, i.e., HSPA, HSPA+, and LTE. However, the prohibitively high power consumption by 3G/4G radios in smartphones reduces battery life significantly and thus severely hurts user experience. To tackle this challenge, we designed GreenTube, a system that optimizes power consumption for mobile video streaming by judiciously scheduling downloading activities to minimize unnecessary active periods of 3G/4G radio. GreenTube achieves this by dynamically managing the downloading cache based on user viewing history and network condition. We implemented GreenTube on Android-based smartphones. Experimental results show that GreenTube achieves large power reductions of more than 70% (on the 3G/4G radio) and 40% (for the whole system). We believe GreenTube is a desirable upgrade to the Android system, especially in the light of increasing LTE popularity.

Displays based on organic light-emitting diode (OLED) technology are appearing on many mobile devices. Unlike liquid crystal displays (LCD), OLED displays consume dramatically different power for showing different colors. In particular, OLED displays are inefficient for showing bright colors. This has made them undesirable for mobile devices because much of the web content is of bright colors. To tackle this problem, we present the motivational studies, design, and realization of Chameleon, a color adaptive web browser that renders webpages with power-optimized color schemes under user-supplied constraints. Driven by the findings from our motivational studies, Chameleon provides end users with important options, offloads tasks that are not absolutely needed in real time, and accomplishes real-time tasks by carefully enhancing the codebase of a browser engine. According to measurements with OLED smartphones, Chameleon is able to reduce average system power consumption for web browsing by 41 percent and is able to reduce display power consumption by 64 percent without introducing any noticeable delay.

Emerging organic light-emitting diode (OLED)-based displays obviate external lighting, and consume drastically different power when displaying different colors, due to their emissive nature. This creates a pressing need for OLED display power models for system energy management, optimization as well as energy-efficient GUI design, given the display content or even the graphical-user interface (GUI) code. In this work, we study this opportunity using commercial QVGA OLED displays and user studies. We first present a comprehensive treatment of power modeling of OLED displays, providing models that estimate power consumption based on pixel, image, and code, respectively. These models feature various tradeoffs between computation efficiency and accuracy so that they can be employed in different layers of a mobile system. We validate the proposed models using a commercial QVGA OLED module and a mobile device with a QVGA OLED display. Then, based on the models, we propose techniques that adapt GUIs based on existing mechanisms as well as arbitrarily under usability constraints. Our measurement and user studies show that more than 75 percent display power reduction can be achieved with user acceptance.

Displays based on organic light-emitting diode (OLED) technology are appearing on many mobile devices. Unlike liquid crystal displays (LCD), OLED displays consume dramatically different power for showing different colors. In particular, OLED displays are inefficient for showing bright colors. This has made them undesirable for mobile devices because much of the web content is of bright colors.

System energy models are important for energy optimization and management in mobile systems. However, existing system energy models are built in a lab setting with the help from a second computer. Not only are they labor-intensive; but also they do not adequately account for the great diversity in the hardware and usage of mobile systems. Moreover, existing system energy models are intended for energy estimation for time intervals of one second or longer; they do not provide the required rate for fine-grain use such as per-application energy accounting.

No abstract available.

No abstract available.

Emerging organic light-emitting diode (OLED)-based displays obviate external lighting; and consume drastically different power when displaying different colors, due to their emissive nature. This creates a pressing need for OLED display power models for system energy management, optimization as well as energy-efficient GUI design, given the display content or even the graphical user interface (GUI) code. In this work, we present a comprehensive treatment of power modeling of OLED displays, providing models that estimate power consumption based on pixel, image, and code, respectively. These models feature various tradeoffs between computation efficiency and accuracy so that they can be employed in different layers of a mobile system. We validate the proposed models using a commercial QVGA OLED module. For example, our statistical learning-based image-level model reduces computation by 1600 times while keeping the error below 10%, compared to the more accurate pixel-level model.

Emerging organic light-emitting diode (OLED)-based displays have drastically different power consumption when displaying different colors, due to their emissive nature. They bring a new opportunity for power saving by transforming GUI colors. In this work, we study this opportunity using a commercial-off-the-shelf QVGA OLED module and user studies. We present techniques that adapt GUIs based on existing mechanisms as well as arbitrarily under usability constraints. Our measurement and user studies show that more than 75% display power reduction can be achieved with user acceptance.

Nanowire crossbar is one of the most promising circuit solutions for nanoelectronics. However, it is still unclear whether or how they can be competitive in implementing logic circuits, as compared to their MOSFET counterparts. We analyze nanowire crossbars in area, speed, and power, in comparison with their MOSFET counterparts. We show nanowire crossbars do not scale well in terms of logic density and speed. To achieve performance close to their MOSFET counterparts, crossbar circuits need faster field-effect transistors (FETs) to compensate the high resistance of nanowires. Motivated by the analysis and comparative study, we propose a crossbar cells design based on judicious use of silicon nanowires. The crossbar cell is compatible with the conventional MOSFET fabrication and design methodologies, in particular, standard cell-based integrated circuit design. We evaluate logic circuits synthesized with crossbar cells and MOSFET cells based on the MCNC91 benchmark. The results show that crossbar cells can provide a density advantage of more than four times over the traditional MOSFET circuits with the same process technology, while achieving close performance and consuming less than one third power.

Nanowire crossbar is one of the most promising circuit solutions for nanoelectronics. We show nanowire crossbars do not scale well in terms of logic density and speed. We consequently propose a crossbar cell design based on judicious use of silicon nanowire crossbars with microscale pitches and small dimensions. The crossbar cell is compatible with the conventional MOSFET fabrication and standard cell-based integration. We evaluate logic circuits using crossbar cells and show that they can improve density by more than fourfold over the traditional MOSFET circuits with the same process technology, while achieving close performance and over threefold power reduction.

Cochlear implants have achieved great success in restoring hearing to profoundly deaf people. New generation implants pose more restrict requirements on processing precision and make traditional cochlear systems insufficient to deal with the large amount of data which should be transmitted via the wireless link. A new cochlear system with implant DSP is proposed to address the data rate problem. This system needs to transmit only voice-band signals with a data rate of 100 Kbps and the data rate bottleneck is removed for future development. By optimizing the speech processing algorithm and the inductive wireless link, the power consumption of this system increases less than 10% when compared with the traditional system.