A New Approach for Energy Management in User-Centric Applications Vasily G. Moshnyaga Dept. Electronics Engineering and Computer Science Fukuoka University Fukuoka, Japan
[email protected] Abstract— Energy reduction has become an important design problem. Although most of consumer electronic systems are user centric, existing energy management methods are device centric. These methods either assume unchangeable operational environment or rely on very simplified policies, which eventually lead to large energy losses. In this paper, we present a new energy management approach that allows systems to monitor their users for energy saving. We discuss applications of this approach in personal computers, TV sets and home automation system and show the experimental results. Keywords- energy management; computer display; TV set; home system
I.
INTRODUCTION
Reducing energy consumption of electric and electronic systems has become an important design problem. Energy efficient design requires systematic optimization at all levels of design abstraction: from process technology and logic design to architectures and algorithms. Existing techniques include those at the algorithmic level (such as, strength reduction [1], algorithmic [2] and algebraic transformations [3], retiming [4], etc), architectural level (such as pipelining[5] and parallel processing[6]), logic (logic minimization [7,8], pre-computation [9]), circuit[10] and technological level[11]. We refer to these optimizations as static because they are applied during the design phase, assuming a worst-case scenario, and their implementation is time invariant. An alternative approach is to adjust the system operation and energy consumption to the application workload dynamically, i.e. during system operation. Methods include: Application-driven system reconfiguration [12]. The idea is to map a wide class of signal-processing algorithms to an appropriate architectural template and then reconfigure the system to deliver just right amount of computational resources for an application. Dynamic voltage-frequency scaling [13]. The tradeoff is that at lower voltages circuits become slower, and the maximum operating frequency is reduced. Significant energy savings can be achieved by recognizing that peak performance is not always required and therefore the operating voltage and frequency of the processor can be scaled dynamically. The frequency can be lowered by performing operations in parallel to maintain the same overall performance. Energy-quality scaling [14, 15]. The idea is to obtain the minimum energy consumption at required computational quality. If the computational order of a system is varying based on signal statistics (known a priori), processing the most significant (from the point of precision) computations first achieves required computational quality at less energy. The implementation requires dynamically reconfigurable architectures that allow energy consumption per input sample
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Fig.1: Energy management schemes: (a) existing; (b) proposed
to be varied with respect to quality. Applications include filtering, image processing (DCT, IDCT, Fourier transform), video processing (vector quantization, video decoding), etc. Network-driven optimizations [16]. The distributed network resources are exploited to reduce energy consumption in portable (wireless) devices by transferring large amounts of computation typically performed at the portable devices to the high-powered servers of the network. Despite differences, these dynamic methods exploit the same idea; namely, to keep the system in the lowest power mode whenever there is no activity on inputs, and activate the system whenever the inputs signals change. To implement the idea, the system incorporates an extra unit that constantly monitors the input activity or workload (X) and based on it determines the new operational mode (S) for the system, as shown in Fig.1 (a). Depending on application, the workload (X) can be measured by different metrics (e.g. the average rate, at which events arrive at the processor [15], the idling time per sample interval [13-17], etc). If the workload overcomes a given threshold, the controller activates/deactivates the system modules or reconfigures the system or changes frequency and voltage to deliver the required performance at the minimum energy consumption per sample. Unfortunately, putting new functionality into a system to monitor the workload does not come for free: it drains energy and consumes extra area and cost. Also, switching the system between the modes introduces delays, which affect performance. Therefore, solutions are usually linked to predicting the workload in advance based on various heuristics [14]. However, it is not always possible to make correct predictions due to peculiarities of application, operational environment and/or the user demands, which are varying in time. In this paper we advocate a novel approach to energy saving, which in contrast to the other methods monitors not only the system workload but also the user and the environment. Existing power management policies are device centric; that is they either ignore the user, assuming unchangeable operational environment for the device or rely on very simplified policies, which eventually lead to large energy losses. Take a TV for example. A variety of methods has been proposed to reduce the energy consumption of TV.
However, majority of them ignore the viewer. Up-to-the-date TV sets produce bright and high quality pictures even though nobody watches them. Obviously, for real energy saving we must put more intelligence into systems making their operation user-centric, i.e. adaptable to the varying user behavior. II.
THE USER-CENTRIC ENERGY MANAGEMENT
The main idea of our approach is to extend the controller functionality to monitor the demands on system operation imposed by the user and adjust the system performance to the variation in these demands. Fig.1 (b) illustrates the idea. The user monitoring is done by non-intrusive sensors (e.g. temperature sensors, motion sensors, video camera or CMOS image sensors, acoustic sensors, RFID tag readers, etc). Having the sensor readings, the controller evaluates the system operation (power consumption mode, output quality, active resources, etc.) and the user status (location, position, movement, eye-gaze, etc.), estimates the user demands for the system at that time, and based on them generates signals, which deliver the required functionality without loosing energy. For example, when a user watches TV, the TV is kept bright. If there are several people in the room but nobody looks at the TV screen, the screen is dimmed to save energy. When nobody is present in the room, the TV is powered down or put into a sleep mode, which keeps only the audio on. In our research, we have investigated several applications of this novel energy management approach. Examples include: personal computers, television sets, personal media gadgets, home automation system. All these systems are user centric; they receive inputs from the user and deliver services to the user. Obviously, their energy management must be usercentric. Below we discuss some applications of the approach in details. A. Personal Computers Personal computer (or PC) is a classic user-centric system that interactively receives commands from its user (through keyboard, mouse, microphone, etc) and delivers him or her information through display, printer, speaker, or other output device. In a typical PC, display accounts for 1/3 of the total PC energy consumption [18]. To reduce the energy, OS-based Advanced Configuration and Power Interface (ACPI) [19] sets display to low-power modes after specified periods of inactivity on mouse and/or keyboard. The ASPI efficiency strongly depends on inactivity intervals, set by the user. From one hand, if the inactivity intervals are improperly short, e.g. 1 or 2 minutes, the ACPI can be quite troublesome by shutting the display off when it must be on. From another hand, if the inactivity intervals are set to be long, the ACPI efficiency decreases. Because modifying the intervals requires system setting, a half of the world’s PC users never adjust the power management of their PCs for fear that it will impede performance [20]. Those who do the adjustment, usually assign long intervals. HP inspected 183,000 monitors worldwide and found that almost a third was not set to take advantage of the energy saving features. Just enabling these features after 20 minutes of inactivity can save up to 381 kWh for a monitor per year [21]. Evidently, to prevent such a problem the PC energy management must employ more efficient user monitoring.
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In contrast to the ACPI, which “senses” the user through keyboard and/or mouse, we “watch” the user through camera or CMOS image sensor [22]. Our user-centric energy management is based on the following assumptions: (1) PC is equipped with a color video camera (CMOS image sensor). The camera is located at the top of display. When the user looks at display it faces the camera frontally; (2) The target object is a single PC user. The user’s motion is slow relatively to the frame rate. The background is stable and constant; (3) The display has a number of backlight intensity levels with the highest level corresponding to the largest power consumption and the lowest level to the smallest power, respectively. The highest level of backlight intensity is enabled either initially or whenever the user looks at the screen. Fig.2 shows the proposed display energy management scheme. The user’s eye-gaze detector receives an RGB color image, analyzes its content and outputs two logic signals, u1 and u0. If a face pattern is detected in the image, the detector sets u0 to 1; otherwise u0=0. The zero value of u0 enforces the voltage converter to shrink the backlight supply voltage to 0 Volts; dimming the display off. If the gaze detector determines that the user looks at the screen, it sets u1=1. When both u0 and u1 are 1, the display operates as usual in the high power mode. If the user’s gaze has been off the screen for more than N consecutive frames, u1 becomes 0. When u0=1 and u1=0, the input voltage (Vb) of the high-voltage inverter is decreased by ΔV. This voltage drop lowers backlight luminance and so shrinks the power consumption of the display. Any on-screen gaze in this low-power mode reactivates the initial backlight luminance and moves the display onto the high-power mode. However, if no on-screen gaze has been detected for N consecutive frames and the backlight luminance has already reached the lowest level, the display is put asleep. Returning back from the sleep mode requires pushing the ON button. To detect eye-gaze we use a low-complexity eye-tracking algorithm [23] that scans the Six Segment Rectangular filter [24] over the integral representation of input image to locate the Between-The-Eyes (BTE) pattern of human face (see Fig.3). At each location, the SSR filter compares the integral sums of the segments as follows: At each location, the SSR filter compares the integral sums of the segments as follows: Sum (1) < Sum ( 2) & Sum (1) < Sum ( 4), (2) Sum (3) < Sum ( 2) & Sum (3) < Sum (6) If the above criteria are not satisfied, the user is assumed to be absent in front of the camera. Otherwise, the SSR is considered to be a candidate for the BTE pattern (i.e. face
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candidate) and two local minimum (i.e. dark) points each are extracted from the regions 1 and 3 of the SSR for left and right eyes, respectively. If both eyes are located, the user is assumed to be looking at the screen. The details can be found in [25] The detector was implemented in hardware[26] based on a single XILINX FPGA board and VGA CMOS camera connected to the board through parallel I/O interface. It operates at 48MHz frequency; 3.3V voltage and provides user gaze detection at 20fps rate, 84% detection accuracy while consuming in only 150mW of power. Fig.4-5 illustrate the power savings achieved by the proposed technology for 17”TFT display (35W on peak, 0.63W in sleep mode, four different levels of screen brightness) in comparison to the existing ACPI power management (20 min. inactivity setting) [27]. Even though our technology takes a little more power than the ACPI for the user monitoring, it saves 36% of the total energy consumed by display on this short (100 seconds) test. For a longer test, the energy savings provided by our approach are much larger. B. Television Set Despite of wide acceptance of “green energy” regulations, TVs have become more energy consuming. Recently emerged plasma televisions, which are 50% bigger than their cathode-ray tube equivalents, consume about four times more energy. A 50-inch flat-screen plasma HDTV now burns over 500Watts of power thus consuming same energy as dishwasher or in-room air-conditioner. Dimming the screen brightness is one of the most effective energy saving techniques proposed for TV. Sensing light is already a feature of many TVs to enable dimming based on ambient light level. Also, users can set the brightness by selecting one of three operation modes: the “standard mode” delivers the highest
Fig.5. Screenshots of PC display and corresponding power consumption: when the user looks at screen, the screen is bright (power: 35W); else the screen is dimmed (power: 15.6W)
level of brightness; the “saving mode” refers to the dimmed screen and “no brightness mode” reflects the dark screen. The brightness level in the saving mode can also be changed. Unless the user changes the mode, the TV maintains same brightness. Similarly to PC users, majority of TV viewers do not change brightness for energy savings, fearing that it affects picture quality. Besides, users usually watch TV while doing other activities: reading books, working on PC, preparing food, chatting with friends, etc. As a result, the TV wastes large energy for producing high quality pictures when nobody watching them. To save energy, leading TV produces have embedded “user sensors” into TV to adjust performance to the user behavior. For example, the “VIERA”® plasma TV from Panasonic senses the user through the remote controller. If the time from the last use of remote controller exceeds a pre-defined duration (e.g. 1, 2 or 3 hours), the TV automatically powers off. Additionally to this “remote button sensor”, the latest “Bravia”® HDTV from Sony incorporates an infra-red motion sensor, which switches the TV off when no motion has been detected in front of it over a period of time (e.g. 5 min, 30 min or 1 hour) pre-set by the user. Also Hitachi and Toshiba use hand gesture sensors to control TV.
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Fig.6: An illustration of the face tracking algorithm and image resolution used in the search
As we mentioned in Section 2.A, “sensing” the viewer explicitly (through fingers, hands or body motion) has several problems: it is either incorrect (a moving dog or a tree in the window can keep the TV on) or troublesome, i.e. requires the user either to push the remote control frequently to prevent shutting the TV down when someone is watching it, or to enlarge the allowed duration of inactivity interval. Our approach is free of such problems because it “watches” the viewer through a camera and detects precisely whether he or she looks at the screen or not. In contrast to PC, the TV viewer monitoring unit searches for any face in the image that looks at the camera frontally. The search is done by scanning the SSR filter (Fig.3) over the integral representation of green image plane produced by camera. Because the size of a face decreases with the distance from camera, and large faces require large SSR filters, we repeat the scan for a scaled-down image (at most 6 times) ending the search whenever a viewer is detected. Fig.6 illustrates the flowchart of the searching process. The right column shows the scale-down images used in the search; (the scales are 2/3, 1/2, 1/3, 1/4 and 1/6 from top to bottom). The user-centric TV energy management works as following. Turning the TV on automatically sets the standard (brightest) mode and starts the camera-based viewer monitoring. If the monitoring cannot find faces in the current image or finds that none of the faces looks at the screen, the TV is switched to the “energy saving mode”, by dimming the screen. If it has already been in the “energy saving mode” longer than a pre-defined time, T1, and still no viewers found, the TV is turned to the “no-screen” mode i.e. dark screen (see Fig.7). Any face gazing at the screen re-activates the power-up mode and the process repeats. However, if the no-screen mode lasts longer than a pre-set time (T2), the TV, camera and monitoring unit are powered down. Fig.8 shows the relative energy saving figures achieved by the proposed “user-centric” energy management in comparison to the standard mode and the motion-based screen-off mode (set to 5min and 30min of inactivity) estimated on 40” KDL-40V5 Sony Bravia® TV. The test lasted 40 minutes and covered three different parts. In the first part (10min), two viewers were moving sporadically in front
Fig.7. TV screenshots when the viewer looks at screen (top); and when does not (bottom). 100 80 60 40 20 0 S
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Fig.8. Results of TV energy saving
of camera (TV) not looking at the TV screen. In the second part (20 min.), one of the viewers left the room and the second watched the TV while working with PC. In the last part (10min) the user was reading the book seating in front of TV almost motionless. In this test, the existing motion-based energy management activates the “no-screen” mode only in the last 5 min. In contrast, our “user-centric” management lowers the brightness each time the TV is not watched. Fig.8 shows the relative energy consumed in standard mode (A), by motion-based energy management (M) and by our user-centric energy management (U), evaluated in the standard mode (M1, U1) and the saving mode (M2, U2). As we see, our approach reduces energy by 21% and 32% respectively, while motionbased scheme reduced only by 6% and 12%, respectively. Obviously, for other scenarios the results are different.
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base station, received the control commands from HCC and based on them connects or disconnects the device to/from the Dishwasher Dryer Play-station power line. Based on the data received, the location of users, Gate way Device Sensor User Sensor their behavior and instructions, the system can switch off those appliances that are active but not in use or activate those Fig 9: System Architecture he or she needs. Unlike existing home automation approaches, we teach our system to understand the contextual cues of home occupants. The concept is similar to the Aware Home C. Home energy management [33] with the difference that it aims home energy saving. Our Home is another application where the user-centric energy goal is to develop a user-centric pervasive system that management is profitable. In modern homes, electric and coordinates operational modes of home appliances with the electronic devices account for 1/3 of the total household user’s location, status and performance to form a comfortable energy consumption [27]. Separate studies in US, Dutch and and pleasant home environment of low energy consumption. British homes have reported that 26 to 36% of domestic Based on sensor reading and the energy usage patterns of energy is “behavioral” – determined by the way we use home occupants, the system sets the usage of all room devices, machines, not the efficiency of the hardware itself [28]. lighting, HVAC, TV, music, and also motorized shades to Recently developed home systems (e.g. [29-31]) allow users control sunlight emitted in room areas. It turns off lights at to manage home appliances remotely and switch them on or areas no dwelled, dims lights in corridors and hallways, off from an in-wall control panel or a PC. However, we separates circuits creating energy saving solutions. Only when humans have two amazing features which undermine such some one walks on to the room, the system does the lighting control. First, we are quite lazy to routinely perform manual come or turns on air-conditioner. During mornings and control of several dozens of appliances everyday. Second, we afternoons it automatically opens shutters and drapes, turning frequently forget about the control leaving appliances ON at lights off when not needed, while at striking hot noon, it closes night of even after going away from home. What we need is a the drapes to decrease solar effects and saving energy at the system that not only monitors home appliances and reports same time. The system turns off air-conditioning at vacant their status to the user but automatically adjusts the work of areas, monitors doors or room windows, to avoid air leak, the appliances to the user’s needs. Obviously, such a system monitors filter cleanliness status and other factors that help must be also user-centric: that is, capable of monitoring not decrease energy consumption. It prevents overrunning of only electric appliances but also their users. devices, pumps, water heaters, HVAC and directly alerts when Unlike existing systems, our user-centric home energy high level consumption thresholds are reached. Obviously any management is a network of sensors which monitors all users user instruction (say switching the room lights on) interrupts in a home, determines their location, behavior and control the HCC to adjust its operation. instructions if any, and based on them automatically sets all In our architecture the user sensors are wired, while device home appliances to an appropriate mode that fully satisfies the sensors and HCC control are wireless. The user interface with current user’s needs while maximally saving the energy the HCC is envisioned through touch screens and wall consumption. The heart of the system is home control center mounted control panels. The panels can have from 2 to 16 (HCC) which is built on top of standard gateway architecture, buttons and are provided for each room. The buttons on each as shown in fig.9. The HCC is based on an open Linux control panel are programmed for specific commands in each platform enabling the home owner to build a technologyroom creating moods and activating pre-programmed neutral smart home that can be controlled with a mobile phone, operation or device control. You can change the mood at the using a unified user interface. The HCC receives information touch of a button. from multiple sensors that monitor users and appliances and, III. CHALLENGES AND OPEN PROBLEMS once processed, generates the control signals for the actuators which control device operations. The user sensors include The research presented here is a work in progress and the motion sensors, CMOS cameras or vision sensors, RFID etc. list of things to improve it is long. In the current work on userThe device sensors combine functions of conventional sensors centric PC energy management, we restricted ourselves to a and actuators with transmitters and receivers [32]. Each device simple case of a singular user. However, when talking about sensor is attached to a corresponding appliance and the power the user-gaze monitoring in general, some critical issues arise. line (Fig.10). When activated, it measures the value of electric For instance, how to handle several users? The main PC user current in the corresponding device, transmits the result to the DVD
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might not look at screen while the others do. Concerning this point, we believe that a feasible solution is to keep the display active while there is someone looking at the screen. The TV viewer monitoring also has several challenging issues. First, the viewers can be positioned quite far from the TV set. Second, the viewers can watch TV when laying on a bed, so the viewer’s face can rotate on a large angle. Third, the face illumination may change from a very bright to a complete darkness. In these conditions, the correct real-time face monitoring with low-energy overhead becomes really difficult. The user-centric home energy management is a typical task of context-aware computing, which requires solutions related to situation recognition, pervasive computing, advanced human-computer interface, ambient control, etc. At this time, we assume touch panels and keyboards as input HCI devices. In the future there will be many possible types of input that a system might be capable of. In this case, it will be helpful to indicate to the user what is available for selection of energysaving options, particularly for those such as touch, voice or gesture that may not be readily visible in the way that a keyboard is. Some possible input devices are: virtual touchscreen, presence, voice, gestures, virtual displays, etc. Providing the user with “virtual” devices that project the selection menu or an image of a keyboard onto any flat surface nearby the user is another challenging problem. Odd as it sounds, using one of these soon becomes quite natural. Another possible alternative input for home system is voice recognition. The high-quality voice recognition requires substantial computing power, so handheld devices are still a few years away from being able to take dictation well enough to substitute for a keyboard — but sooner or later they will. ACKNOWLEDGMENT The work was sponsored by The Ministry of Education, Culture, Sports, Science and Technology of Japan under Regional Innovation Cluster Program (Global Type, 2nd Stage) and Grant-in-Aid for Scientific Research (C) No.21500063 REFERENCES [1] Chandrakasan, et al, “Minimizing power user transformations”, IEEE Trans. on CAD, vol.14, pp.12-31, Jan.1995 [2] K.K.Parhi, “Algorithm transformation techniques for concurrent processors”, Proc. IEEE, vol.77, pp.1879-1895, Dec. 1989. [3] M.Potkonjak and J.Rabaey, “Fast implementation of recursive programs using transformations”, Proc.ICASSP,pp.5629-672, 1992. [4] C. Leiserson and J.Saxe, “Optimizing synchronous systems”, J. VLSI Comput. Syst., vol.1, pp.41-67, 1983. [5] H.Loomis and Sinha, “High speed recursive digital filter realization”, Circuits, Syst., Signal Processing, vol.3, no.3, pp.267294, 1984. [6] K. Parhi and D.G.Messershmitt, Pipeline interleaving and parallelism in recursive digital filters”, Part I & II, IEEE Trans. ASSP, vol.37, pp.1099-1134, July 1989. [7] S.Malik, S.Devadas, A Survey of Optimization Techniques Targeting Low Power VLSI Circuits, Proc. ACM/IEEE DAC, pp.1995 [8] S.Iman, M.Pedram, Logic synthesis for low power VLSI designs, Kluwer, 1997 [9] M.Alidina, J.Monteirio, S.Devadas, et al., “Precomputation-based sequence logic optimization for low-power”, IEEE Trans.VLSI Syst. vol.2, pp.398-407, Dec. 1994.
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