Abstract:
The utility of a mobile computer, such as a laptop, is
largely constrained by battery life. The display stands
Out as a major consumer of battery energy, so reducing
that consumption is desirable. In this paper, we motivate
and study energy-adaptive display sub-systems
that match display energy consumption to the functionality
Required by the workload/user. Through a detailed
characterization of display usage patterns, we show that
screen usage of a typical user is primarily associated with
content that could be displayed in smaller and simpler
Displays with significantly lower energy use. We propose
example energy-adaptive designs that use emerging
OLED displays and software optimizations that we call
Dark windows. Modeling the power benefits from this
approach shows significant, though user-specific, energy
benefits. Prototype implementations also show acceptability
of the new user interfaces among users.
SOFT COPY:
1 Introduction
With the increased acceptance and use of mobile
devices such as laptops and pocket computers, mobile
computing systems are rapidly becoming one of the key
markets of interest for computing systems. Since the batteries
on these mobile systems are typically limited in
capacity, reducing the energy consumption is one of the
key challenges in designing mobile systems.
Among the various components that contribute to the
consumption of electrical energy, the display sub-system
(the electronics associated with the visual representation
of the data generated by the system - namely the display
and the controller) often plays an important role.
For example, Udani and Smith report that the display
component of the system can easily take over half the
total energy of a laptop system [7]. Similarly, Choi et al.
report that the display component of the system consumed
close to 61% of the total power of the system for a
handheld device. Furthermore, unlike some of the other
components of the system, display power consumption
has traditionally been relatively invariant across technology
shrinks and unamenable to opportunities to exploit
“slack,” [5] making it a likely greater fraction of the total
power of future systems.
Previous approaches to reducing display power consumption
have either focused on aggressively turning off
the entire display when it is not being used or have resorted
to designing systems with lower-quality or smaller
sized displays to minimize power. However, new technologies,
such as Organic Light Emitting Diodes (OLEDs),
are becoming available that allow lower power consumption
when a reduced area of the screen is in use.
We propose utilizing this new flexibility to reduce energy
consumption. Our work is based on the intuition
that different workloads and users have varying display
needs. Having a “one-size-fits-all” display targeted at
the needs of the most aggressive workload/user often
leads to large energy inefficiencies in the display energy
consumptions of other workloads and users. Consequently,
an energy-adaptive system design that consumes
energy only on portions and characteristics of the
screen that are being used by the application and are relevant
to the user, can achieve energy benefits.
In this work, we make two key contributions. First, we
perform a detailed characterization of the display screen
usage of a representative test user population. Our results
indicate that, on average, our users use only about
60% of the screen area available to them. Addition-
ally, screen usage is often associated with content that
could have been equivalently displayed, with no loss in
visual quality, on much simpler lower-power displays
(lower size, resolution, color, brightness, refresh rates,
etc.) Using a detailed analysis of the user traces, we
also correlate our results to user and application behavior.
To the best of our knowledge, our work is the first to
identify, quantify, and analyze such mismatch opportunities
in workload/user needs and current display properties.
Overall, our results indicate that an energy-adaptive
system design that matches display power consumption
to the functionality required by the workload/user can
significantly reduce the energy consumption of future
display sub-systems.
Building on the insights from the above analysis,
we propose example energy-adaptive display system
designs. At the hardware level, our designs leverage
emerging Organic LED (OLED) displays [3] that use energy
proportional to the overall light output of the display.
At the software level, we propose dark windows
optimizations that enable the windowing environment to
change the brightness and color of areas of the screen that
are not of interest to the user. We model the power bene-
fits and study the user experience with our designs. Our
results indicate significant, though user-specific, energy
reductions with acceptable user interfaces.
The rest of the paper is organized as follows. Section 2
discusses our user study in greater detail. Section 3 discusses
the example energy-adaptive display systems that
we consider and our experiences with those. Section 4
further explores the design space with energy-adaptive
displays and Section 5 discusses related work. Section 6
concludes the paper and discusses future work.
2 User Study
This section presents the results from our user study
characterizing typical system usage of a representative
user test population. Our goal was to understand
the screen usage patterns and identify opportunities for
power reduction. Section 2.1 discusses our methodology
and Section 2.2 discusses our results.
2.1 Methodology for the user study
Our user study is based on usage of the MicrosoftWindows
environment by seventeen users. We chose Microsoft
Windows environments because of their widespread
acceptance and representativeness of the general
mobile market. The users were chosen to include characteristics
representative of typical usage and cover a cross
section of mobile system usage (administrative tasks,
code development, personal productivity, entertainment,
etc.). Since we were interested in understanding both
current and future user behavior when using mobile environments,
we studied both laptop and desktop users.
(Many of our laptop users used their machines as their
main machine - both as a desktop and a laptop.) The
systems used by the test users include a variety of screen
sizes and display resolutions. Column 2 in Figure 1 summarizes
the properties of the systems used by our test
population.
An application-level logger program was run on the
users' machines for times ranging from 1 to 14 days. The
logger program was used to collect periodic information
about (i) the current window of focus – its size, its location,
and its title and (ii) the size of total screen area used
(all non-minimized windows). Our sampling rate was set
to once a second. Screen savers were set to turn on after a
reasonable time (1-5 minutes) to allow us to isolate only
the usage patterns when the user was active.
Column 3 in Figure 1 summarizes the length of the
user traces. The traces range from 9 hours to 346 hours.
The variation in the traces represent the differences in
how individual users used their machines during their
participation in the study. Overall, our samples represent
close to 100 days of continuous computer usage time.
Column 4 in Figure 1 summarizes the length of the
“active” user traces, after factoring out the time spent in
the screen saver as an indication of the time the user was
idle. Traces are still collected during the time it takes
for the screen saver to be activated, but given the length
of our logs, the effect of this is minor. The sizes of our
active user logs range from about 6 hours to 61 hours of
computer usage per user. Given that this is the time we
are interested in, the rest of the paper will focus on the
active window usage without considering the time spent
in the screen saver. Existing technologies can save power
spent in the idle time by turning off the displays.
2.2 User study results
Average screen usage. Figure 1 summarizes the information
Screen usage corresponding to application behavior.
In order to understand the relationship between the
screen usage and the application behavior, we took the
samples from each of our user logs and categorized them
into four bins – (i) samples where the window of focus
usage was between 0 and 25% of the total screen area, (ii)
samples where the window of focus usage was between
25% and 50%, (iii) samples where the window of focus
usage was between 50% and 75%, and (iv) samples
where the window of focus usage was between 75% and
100%. For each bin, we then analyzed the key applications
associated with the samples. Figure 4 summarizes
our results. As before, we compute the arithmetic mean
of the averages per individual users to avoid distortions
due to trace lengths.
Overall, the workloads used by our user population
span a range of applications representative of typical
system usage. Broadly, they can be categorized into
(i) access related - web browsing and e-mail (Internet
Explorer, Netscape, Outlook, Pachyderm mail reader,
Messenger), (ii) personal productivity and code development
(Word, Emacs, Powerpoint, Excel, Visual studio,
Dreamweaver, X-term, Realplayer, Image viewer,
Acrobat reader, Ghostview), and (iii) system related and
application control windows (File Explorer, navigation
windows, taskbars, menus, status and properties messages,
confirmation and password query windows).
Focusing on the windows associated with the various
applications, we observe two interesting trends. First,
Active area is 0-25% (23% of the time for typical user)
Key applications: 20% Task bar, 15% Program Manager, 5% X-term, 60% miscellaneous windows (message composition,
MSN messenger, real player, menu and message windows – properties, connection status, file downloads, alerts
and reminders, volume control, printer status, find-and-replace, organizer preferences, file explorer, spell-check, wizards,
status messages, file-find, password query windows, confirmation windows)
Active area is 25-50% (22% of the time for typical user)
Key applications: 19% X-term, 18% message composition, 6% internet explorer, 57% miscellaneous windows (mailrelated
windows, file explorer, emacs and notepad, MSN messenger chat windows, other status windows)
Active area is 50-75% (28% of the time for typical user)
Key applications: 33% Internet Explorer, 24% mail composition and reading, 43% miscellaneous windows (emacs and
notepad, Image editor and photo viewer, messenger chat windows, Frontpage, Framemaker and ghostview, file explorer,
Powerpoint, dreamweaver, winlogger)
Active area is 75-100% (27% of the time for typical user)
Key applications: 21% Outlook, 20% Internet Explorer, 7% Excel, 52% miscellaneous windows (Powerpoint, Framemaker,
Acrobat reader,Word [various files], Visual C++ [various files]), Dreamweaver, Imageviewer)
Figure 4: Understanding screen usage by application. Windows are classified based on their sizes into four bins, and
for each of the four bins, the key applications dominating the samples in the bin are summarized.
system-related status messages and query windows typically
use small window sizes; in fact, these windows
constitute a significant fraction of the samples associated
with smaller size windows. Additionally, these windows
usually display fairly low content that do not need the
aggressive characteristics of the display – for example,
a low-resolution display with support for a small number
of colors would be adequate to obtain an equivalent
user experience. Second, personal-productivity applications
and development environments and web-browsing
and e-mail applications typically use larger portions of
the display area. The actual fraction of the screen area
used appears to be highly dependent on individual user
preferences for window size, fonts, etc. However, even
with these large windows, characteristics of the displays
such as resolution, brightness, and color are not used to
their full capacity.
Screen usage corresponding to user behavior. Focusing
on the individual user logs, we observe that individual
user preferences and pre-set defaults tend to significantly
influence the overall screen usage characteristics.
For example, User 1 who, on average uses 63% of
the display area, has Internet Explorer set to use 96%
of the screen area, while User 5 who, on average uses
37% of the display area, has Internet Explorer set to use
67% of the display area. Similarly, User 12, who has
the largest screen usage in our study, has a default mail
composition window of 95% that dominates the traces.
This user-specific sizing of windows appears to be particularly
characteristic of web browsing, email, and editor
applications. In contrast, for development applications
(Visual Studio, and Dreamweaver, Powerpoint), most
of our users prefer to have larger windows – possibly
because of the multi-window content structure of these
applications. Similarly, system-related and applicationcontrol
messages typically use smaller windows irrespective
of the user – mainly since the content in these
windows is relatively low and in most cases the window
sizes are pre-determined by the application. An illustrative
examples is the case of User 8 who maximized
all windows as a matter of routine (“to be able to read
better”). This user still consumes only 85% of the total
screen area because of the smaller window sizes associated
with system-related and application-control messages.
Finally, while the laptop users have a slightly larger
screen usage (62%) than the desktop users (56%), in
general, the results are fairly similar over the laptop and
desktop users.
2.3 Summary
Summarizing the results of our user study, overall,
there is a significant mismatch between the properties
supported in the display and the actual usage of these
attributes by the users in our user study. The size of the
display used exhibits the greatest mismatch – users use
only about 60% of the screen area available. A large fraction
of the smaller windows are typically associated with
system-related and application-control windows that are
independent of user preferences. User preference for
smaller window sizes and font sizes can also translate
into a greater use of smaller sized windows.
Similarly, there are significant mismatches between
the actual screen usage and other attributes of the display
such as resolution, brightness, color, refresh rate, etc. In
particular, most of the smaller windows include content
that could have been equivalently displayed, with no loss
in visual quality, on much simpler lower-power displays
(lower size, resolution, color, brightness, refresh rates,
etc.) Many of the larger windows also do not use all the
aggressive characteristics of the display.
Overall, these results indicate that energy-adaptive
system designs that match display power consumption
to the functionality required by the workload/user have
significant potential to reduce the energy consumption of
the display sub-system.
3 Energy-adaptive Display Sub-systems
This section studies some example energy-adaptive
systems to evaluate how the potential benefits identified
in the user study can be translated to energy reductions.
Section 3.1 describes the hardware and software components
of these designs. Section 3.2 discusses the experimental
methodology used in prototyping and studying
the user interfaces and the energy consumption for
the different designs. Section 3.3 discusses our results
and Section 3.4 summarizes.
3.1 System Design
Our designs use emerging display technologies and
modified window system software to exploit the mismatches
between workload/user requirements and display
properties.
Hardware Support: OLED displays. To enable
energy-adaptive designs, a key requirement is support
at the display sub-system for variability in the display
power based on the properties of the screen output.
That is, it should be possible to change the energy consumption
of regions of the display independent of each
other. Several emerging display technologies support
such variable power over different regions of the screen.
This is preferable to existing technologies, where energy
tradeoffs have to be made for the entire screen.
Organic Light Emitting Diode (OLED) displays [3]
are a good example of this class. In OLEDs, the energy
consumption of a pixel is related to its brightness and
color. OLED displays are built from small organic molecules
that efficiently emit light when stimulated by an
electric field. More than 100 companies are developing
aspects of OLED technology. Kodak, Sanyo, and Sony
have shown prototypes from 5.5-inch displays to 13-inch
displays at trade shows. In general, OLEDs have better
image quality compared to conventional LCDs (better
horizontal and vertical viewing angles, higher brightness,
and faster response times) and do not need a separate
backlight, resulting in lower power. As the technology
matures, the biggest challenges are in overcoming
yield problems and consequently reducing costs. Small
OLED displays are currently available in devices like cell
phones. Larger displays, for handhelds and laptops, are
expected in 2004 [3].
For our prototype, we assumed a laptop system with
a 15” AMOLED (Active Matrix Organic Light Emitting
Diode) display. (Section 4 discusses other hardware approaches
to implement the similar functionality.) The
only hardware changes to a current commodity design
will be to replace the conventional LCD panel, backlight,
and controller with their OLED equivalents.
Software Support: Dark Windows. The software support
for energy-adaptive displays can be implemented at
one of several levels: the application level, the window
system level, or the operating system level. In this paper,
we focus on modifications to the windowing system.
Specifically, the “Dark-Windows” modifications use the
window of focus as an approximation of the active area
of user interest to ensure that energy is spent mainly
on portions of the screen that have the user's attention.
For example, if a user is using an editor to take notes,
her attention is typically on the screen area used by
her editor, her current window of focus. Energy-aware
changes to the brightness, color, or other properties
of the remaining portions of the screen can result in
energy benefits without impacting the user experience.
Our discussions below focus on optimizations that
automatically change the luminescence and color of
non-active screen areas to reduce power. Section 4
discusses other properties of the background that can
be changed and optimizations that focus on mismatches
internal to the window of focus.
3.2 Prototyping and Evaluation Methodology
Our goals with the evaluation were two-fold. First,
we wanted to understand the intrusiveness of the energyadaptive
user interfaces and the complexity and overhead
associated with implementing the dark windows
optimizations. Second, we also wanted to quantify the
energy benefits in the context of one particular technology,
namely OLED displays, and understand the design
Xvnc
VNC Server
VNC Viewer
Applications
VNC protocol X protocol
_ _ _ _ _
_ _ _ _ _
_ _ _
_ _ _ _ _ _ _ _ _ _
_ _ _ _ _ _ _
_ _ _ _ _ _ _ _ _ _ _ Original
frame buffer
Modified
frame buffer
Xvnc VNC Viewer
Figure 5: Methodology used to prototype the user interface.
Interface Name Modification
HalfDimmed Areas outside the window of focus
are dimmed by 50%.
FullyDimmed Areas outside the window of focus
are fully dimmed (turned off).
GrayScale Areas outside the window of focus
are changed to gray, by setting red,
green, and blue values to the average
of the three.
GreenScale Areas outside the window of focus
are changed to green (lowest power
color for OLEDs). The green value
is set to the average of the three colors,
and the red and blue values are
zeroed. This also dims the screen
by 67%.
Figure 6: Summary of the dark windows optimizations
considered. The window of focus is left untouched, while
the background areas are modified in either brightness
or color to save energy.
tradeoffs between the various optimizations.
Prototyping the User Interface We implemented several
prototypes in order to understand the impact of the
dark-windows changes on the user interface. Given the
difficulties with modifying the Microsoft windowing environments,
we implemented our prototypes on the X
Window System under Linux.
To enable a clean implementation of the changes, we
used the open-source VNC (Virtual Network Computing)
server [6]. VNC provides a virtual representation of
the display hardware, i.e. a remote framebuffer, and allows
one to run a server on a machine while viewing its
display remotely from anywhere on the internet. While
the remote viewing property was not of interest to us, by
using VNC, we were able to gain two advantages. First,
since it is a virtual frame buffer, we were able to easily
manipulate the pixel values. Secondly, since VNC
is linked with the X-Windows server, we had access to
the window server data structures, such as the window of
focus.
Figure 5 shows an overview of our changes. We modified
the X-VNC server with two additional functions –
to track the window of focus and other objects in the
display area, and to change the values of the pixels in
the frame buffer based on our dark-windows algorithms.
To reduce the performance overhead from our software
changes, our modifications closely track the incremental
change update mechanism of the VNC protocol.1 Each
framebuffer update represents a region that has changed,
and is sent to the viewer as required to keep the display
up-to-date. Before each frame buffer update is sent to
the viewer, we perform two functions. First, we query
the X-server and record the window of focus – the window
that keyboard events are directed to – including its
origin, width, and height. We then divide the update region
into two groups – those that are in the window of
focus and those that are outside it. The pixels within the
windowof focus are sent unmodified, while the rest have
the desired transformation applied to them. Overall, the
performance overhead of our implementation was negligible
with performance acceptable to users.
We experimented with several transformations, described
in Figure 6. These included transformations that
modified the brightness of the background (non-windowof-
focus) regions (HalfDimmed and FullyDimmed2) as
well as transformations that modified the color of
the background regions (GrayScale and GreenScale).
(Screenshots of these interfaces are shown in Figure 8
in the next section.) In all these cases, we also implemented
a user-defined keyboard short-cut to be able to
turn off the modifications if desired.
Quantifying energy benefits for typical users. Though
we used VNC for implementation convenience, we still
wanted to focus our benefits study on the commodity Microsoft
Windows based systems used in our user study.
We therefore constructed a synthetic trace that modeled
the average behavior observed in our user study and re-
1We originally chose a simpler implementation approach where we
simply traversed the finally-generated framebuffer to apply our modi-
fication heuristic on each pixel. However, this led to an inordinately
large number of computations and caused a perceptible slowdown in
the system. We chose to instead use the rectangular update mechanism
to obviate this problem.
2Our optimization allows the brightness to be dimmed by any userdefined
fraction; however, in this paper, for space reasons, we consider
only two representative data points - half-dimmed and fully-dimmed.
Avg. window size 59%
Avg. background windows' size 17%
0-25% screen usage 23%
(taskbar, xterms, miscellaneous)
25-50% screen usage 22%
(xterms, editors, mail readers, miscellaneous)
50-75% screen usage 28%
(web browsers, mail readers, editors, miscellaneous)
75-100% screen usage 27%
(web browsers, mail readers, miscellaneous)
Default screen background Teal
Default window color White
Default foreground font Black
Figure 7: Summary of properties of synthetic trace. The
trace models the average behavior exhibited in the user
study and was created to match the data from Figure 4.
played it on our prototype systems. The trace runs for
about 1000 seconds and uses a set of applications similar
in nature to those used in the user study. The script
for the trace models the initiation and termination of applications
and varies the window sizes and the duration
of time windows were open to obtain statistics consistent
with the user study. While the users in our test population
used a variety of backgrounds and window colors, a
majority of them used the default windows settings. Our
synthetic trace therefore predominantly uses those colors.
To bracket the impact of specific user choices in this
respect, we also performed experiments where the screen
background and windowcolors are varied. Figure 7 summarizes
the window properties of the synthetic trace and
a comparison of the data with that in Figure 4 shows the
close correlation between the behavior of the trace and
the user study.
Given the current unavailability of 15” OLED displays
(aside from rare prototypes), we decided to use a software
power model representative of future systems. The
model computes the display sub-system power as the
sum of the controller power, the driver power, and the
display panel power. The controller and driver power are
modeled as constant values irrespective of what is displayed
on the screen. The panel power represents the
pixel array power and is the total of the power consumed
by each pixel in the panel array. In turn, the power consumed
by each pixel is proportional to the brightness of
the red, green, and blue components.
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ ! _ _ _ _ _ _ "
The values of the parameters of the power model were
chosen to represent likely future OLED displays, and
were validated by discussions with researchers working
in the area and by inspecting data sheets of current displays.
For the 15” display we model, we set # $ % & ' ( % ) * ( to 0.5W and # + ( , - * ( to 1W. The # . / 0 1 # 2 3 0 4 can be
a maximum of 8.5W. Interestingly, with current OLED
technologies, pixels consume different amounts of power
based upon the particular color, with green typically being
the lowest. For a 1024x768 resolution display with
pixel values ranging from 0 (off) to 1 (fully on), the values
for # ( * + 5 # 6 ( * * & 5
and # 7 ) 8 *
are 4.39 W, 2.29 W, and
4.39 W.
3.3 User Experience and Energy Benefits
User Interface Intrusiveness. Figure 8 summarizes the
various interfaces. (Color pictures are available in the
electronic version of the paper.) The picture on the left
represents the original configuration, while the two pictures
on the top right represent dark-window configurations
with modified background brightness, HalfDimmed
and FullyDimmed. The two pictures on the bottom right
represent the dark-window configurations with modified
background colors, GrayScale and GreenScale. The performance
overhead of the dark windows implementation
was small and there was no significant difference in the
response time of the user interfaces.
To determine the difference in the quality of the various
interfaces, we informally surveyed 9 users. We asked
each user two questions. First, without describing any
battery life issues, we asked users to choose their best interface.
Second, we showed the users the battery life advantages
from the various dark-windows modifications
and then asked users to choose their best interface again,
with the assumption that they were in a situation that
required long battery life, such as a meeting or an airplane
trip. Most of the users indicated a willingness to
use dark windows optimizations to tradeoff longer battery
life for a different user interface. Of the 9 users
we queried, 4 of them preferred GreenScale, 3 preferred
HalfDimmed, and 2 preferred FullyDimmed. Most of the
users expressed a desire to be able to see the contents
of the background, even at the expense of some energy.
Interestingly, even without an awareness of the energy
benefits, four of the users chose some of the dark windows
interfaces (GrayScale and HalfDimmed) as their
preferred interface. Note that even without the software
dark windows' optimizations, the base energy-adaptive
display hardware still provides the ability to have lower
power consumption based on what is displayed on the
screen. Compared to LCDs, this can achieve some power
Original Interface
Dark Windows – Brightness Control
Dark Windows – Color Control
Background half dim Background fully dim
Background grayscale Background greenscale
Figure 8: Screen shots of the user interfaces with dark windows optimizations.
0
2.5
5
7.5
10
LCD OLED Half
Dimmed
Fully
Dimmed
Gray Scale Green
Scale
Power consumption (Watts)
Figure 9: Power benefits from energy-adaptive display
designs.
benefits with no changes in the user interfaces.
Our user interfaces could be improved in several ways
(we discuss these in Section 4). However, our goal was
not to perform an exhaustive exploration of the design
space for user interfaces, but instead to establish that
energy-adaptive display sub-systems can provide energy
benefits with interfaces that users are likely to find acceptable,
particularly in return for longer battery lifetimes.
Energy Measurements. Each of our interfaces modi-
fies the pixels in a different manner, resulting in differing
energy use. We measure the power consumed by our
synthetic trace for each configuration on our modified
VNC server. We study a default LCD configuration, a
default OLED configuration, and four OLED configurations
with the four dark windows optimizations discussed
earlier. We use the OLED power model discussed earlier
to compute the power for the five OLED configurations.
The LCD power model is based on characteristics noted
by Choi et al. [1]. For our synthetic trace, this leads to
very little variance (less than 1%).
Figure 9 summarizes the results. Compared to the constant
10W power consumption of the LCD, the hardware
support for energy-adaptive displays in the base OLED
system achieves a 25% reduction in power. Most of these
benefits are due to power reductions for the teal background
color. (With our OLED display, a teal pixel -
RGB: [0,131,131] consumes only 30% of the maximum
power a pixel can consume - RGB: [255,255,255].)
However, there are also some benefits from power reduction
specific to the content of the windows (e.g.,
web browser). The software dark windows changes
can provide additional power reductions. The Fully-
Dimmed optimization provides an additional 20% over
0
2.5
5
7.5
10
1 96 191 286 381 476 571 666 761 856 951 1046 1141 1236 1331 1426 1521 1616 1711 1806 1901 1996 2091 2186
Power (Watts)
0%
50%
100%
150%
200%
250%
300%
350%
400%
450%
500%
550%
600%
650%
700%
750%
800%
850%
900%
950%
1000%
1050%
1100%
1150%
1200%
LCD
Fully
Dimmed
OLED
window of focus variation
Figure 10: Power variation over time for non-adaptive and energy-adaptive displays.
the OLED case, and a total of 43% over the LCD case.
These benefits are from dimming of the background windows
(predominantly white) and the screen background
(teal). HalfDimmed provides half these benefits while
still allowing some of the information to be visible.
The GreenScale optimization provides 40% energy benefits
compared to LCD (and a 15% benefit compared to
OLED). This optimization computes the average of the
R, G, B values and includes it as the new value for the
green pixel. (Recall that the green pixel takes the lowest
energy compared to the other pixels.) The combination
of using the most energy-efficient color and a reduction
in brightness by 67% leads to the energy benefits. In
contrast the GrayScale optimization averages the R, G,
B values and uses this average as the new values for the
R, G, and B pixels, turning them gray. However, our
results indicate a 1% increase in energy with this configuration
compared to OLED (though still 28% better than
the non-adaptive LCD case). This is because converting
our default background color (RGB: [0,131,131]) to gray
scale ends up moving more bits to the higher-power R
and B pixels (gray-RGB: [87,87,87]). An alternate background
color such as pure blue or red may have benefited
from having a grayscale background optimization.
Figure 10 shows the power variation over time for
three cases - LCD, OLED, and FullyDimmed. The
LCD case represents an non-energy-adaptive display and
shows relatively constant power consumption, invariant
to the size and content of what is displayed on the screen.
The OLED case shows the benefits that can be obtained
from using an energy-adaptive display technology. Since
the benefits are mainly from the background screen color,
the profile of the curve follows the profile of the percentage
of the screen devoted to the background (after
removing the window of focus and other background
windows). The FullyDimmed shows the benefits from
both hardware and software changes for energy adaptivity.
The profile of this curve follows the profile of the
window of focus curve.
As is evident from the above discussion, the benefits
from energy-adaptive display designs are highly dependent
on the choices of the background color and the window
color. Our default trace modeled the windows default
since that most closely represented a majority of
the users in our test population. To better bracket the impact
of other choices of background and window colors,
Figure 11 shows the energy consumption of the various
configurations for the extreme cases of pure white and
pure black backgrounds and window colors. With black
backgrounds and black windows, the base OLED design
achieves most of the benefits from energy adaptivity –
close to 80% reductions compared to LCD. The power
consumed is mainly due to the controller and driver and
minor elements of windows such as title bars, etc. Additional
software optimizations get minimal improvements
on top of this. In contrast, with white backgrounds and
white windows, the base OLED configuration gets close
to no improvements. The FullyDimmed system gets the
maximum benefits (35%) by reducing the power spent
on the background. The other two cases show intermediate
points where both the hardware and software optimizations
obtain good benefits. These results indicate
0
1
2
3
4
5
6
7
8
9
10
LCD
OLED
Half
Fully
Gray
Green
LCD
OLED
Half
Fully
Gray
Green
LCD
OLED
Half
Fully
Gray
Green
LCD
OLED
Half
Fully
Gray
Green
Black Windows White Windows Black Windows White Windows
Black Background Black Background White Background White
Background
Power (Watts)
that different designs may be better for different usage
scenarios. It is important for designers to understand
typical user behavior before designing energy adaptive
display sub systems. In some cases, it might be adequate
to just include support for adaptivity at the display hardware
level and choose color and size defaults with power
in mind. In general, however, for systems that allow a
great flexibility in user choices for background and window
colors, it might be preferable to implement software
optimizations in addition to the base hardware support
for adaptivity.
4 Discussion
The configurations discussed in the previous section
illustrate some example energy-adaptive designs. In this
section, we discuss other possible energy-adaptive options
for display design.
Display energy adaptivity in hardware. Rather than
using OLEDs, other display technologies that enable
energy-adaptivity can be used. These include other optoelectronic
and emissive displays (Field-Emission Displays
[FEDs] and even conventional Cathode-Ray-Tube
[CRT] displays) as well as hybrid technologies like LCD
displays with OLED backlights. With display technologies
like LCDs that do not support energy variability,
designs can still integrate a multi-modal “hierarchy of
displays” configuration. For example, a mobile device
could have two displays – one higher quality (high resolution,
color, high refresh rate, larger size) and consequently
higher energy use, and another lower quality
and lower energy. While the adaptivity in this design is
more coarse-grained than with the OLED systems considered
in this paper, the insights from our study are still
likely to be valid.
Display energy adaptivity in software. Rather than using
the window of focus, other indications of user activity
could be used.
cursor could be kept bright, while the rest of the screen
is dimmed. Another design dimension is to include support
for user-controlled dimming areas. For example,
one possible user interface could include a “sticky-lamp”
placed by the user to light up a specific portion of the
screen. Much as we do in the physical world, the user
could use multiple “sticky-lamps” to light up the workarea.
An alternate implementation could include a “headlight”
on the mouse pointer. The users could then point
the light over regions that they are interested in and move
down as they read along.
Still other dimming interfaces could be application
specific. For instance, in a programming environment,
there may be a concept of the current procedure and related
variables. Portions of the screen related to these
could be made bright – for example, all uses of the variables
and all calls to the procedure. In an email application,
perhaps only the current message needs to be bright.
In a word processor, the line of text being edited could be
bright, the surrounding couple of lines lightly dimmed,
and the rest of the document greatly dimmed. Similarly,
in the case of applications like Microsoft Powerpoint
which use frames within an application, the notion
of a frame-of-focus can be defined, similar to the window
of focus. As another dimension to these user interfaces,
these can all be made time-based. For example, areas
of the screen that have recently changed could be bright,
fading to a dim value as time progresses. When inactivity
is detected, an email application could dim its screen
area until new mail arrives.
Other user interfaces can be developed by combining
the interfaces above. Additionally, other sorts of display
mismatches could be exploited. In this paper, we have
focused on identifying the mismatch between the total
area of the display and the area of interest to the user.
Other properties of the display, such as resolution and
refresh rate could be exploited as well.
Support for output modes beyond displays. The notion
of having multiple displays can be taken one step
further to match output content to notification mechanisms
beyond displays. For example, an email notification
that says “You have mail” on the display could be
replaced by an LED that blinks on the arrival of email
or other similar notification mechanism such as speech
output, vibrations, etc.(Figure 12). As discussed in Section
2, a lot of the smaller windows are typically lowcontent
windows which may be amenable to other forms
of non-visual communication. This combined with the
large design space for alternatives for energy adaptiveness
indicate the potential for an interesting future area
of research – energy-aware user interfaces.
5 RelatedWork
Concurrent with our work, there are two other studies
that have looked at adapting the output of the displays
from an energy perspective. Choi et al. [1] perform
a detailed characterization of the power consumption of
the display sub-system of a handheld device (including
the power of the panel, the panel bus, the backlight, the
frame buffer and the data and address bus driving the
frame buffer). They propose three optimizations that (1)
vary the refresh rate to exploit the after-image caused
by the time constant of the storage capacitors, (2) vary
the color depth to be able to reduce the memory requirements
and hence the memory power, and (3) vary the
backlight luminance with a corresponding compensation
of the brightness and contrast. Kamijoh et al [4] discuss
the energy trade-offs in the IBM wristwatch computer.
While they focus mainly on the hardware-level tradeoffs
and kernel optimizations to use the various standby and
idle configurations of hardware, they also discuss the
implication of controlling the number of pixels turned
on or off on the energy as well as reducing the duty
factor of the display to control the brightness of the entire
screen (e.g., at night). Additionally, while not focusing
mainly on the display component of the power, Flinn
and Satyanarayan [2] also evaluated in detail the energy
benefit of reduced computation with lowered fidelity of
images for web browsing and video playing. Their study
also proposed a method called ”zoned backlighting,” to
enable energy benefits in the display subsystem. While
zoned backlighting could allow independent control of
illumination level for different regions of the screen, no
existing display supports such zoned backlighting yet.
Similar to these studies, our work also explores the
possibility of adapting the output of the displays from an
energy perspective. However, in contrast to the studies,
we primarily focus on the content and intent of the output
screen display. In particular, our work is the first to
perform a detailed characterization of display usage patterns
to identify and understand the common mismatches
between workload/user needs and current display properties.
Based on these insights, our work also explores
several new user interfaces and hardware designs that allow
energy-adaptive control on the portions of the screen
that are not of immediate relevance to the user, while
continuing to provide similar functionality on portions
of the screen of relevance to the users.
Finally, this work focuses mainly on the display panel
power. Complementary to our work, other studies have
also focused on the power consumed by the display controller
and driver (e.g., [8]).
6 Conclusions
As mobile systems, applications, and services become
more pervasive, it becomes ever more important to
identify design strategies to lower energy consumption
and increase battery lifetimes. This paper focuses on the
display sub-system and motivates and evaluates energyadaptive
system designs for future mobile environments.
The first part of our study performs a detailed analysis
of display usage traces from 17 users, representing a few
hundreds of hours of active usage. To the best of our
knowledge, this study is the first to identify, quantify,
and analyze potential mismatch opportunities in workload/
user needs and current display properties. We find
that on average, the window of focus – a good indication
of the area of interest to the user – uses only about 60%
of the total screen area. Additionally, in many cases, the
screen usage is associated with content that could have
been equivalently displayed, with no loss in visual quality,
on much simpler lower power displays. Our analysis
of the user traces indicates that many of these mismatches
could be traced back to the typical content of the
windows as opposed to specific user preferences.
Based on these insights, the second part of the study
proposes energy-adaptive display systems that match
energy use to the functionality required by the workload/
user to obtain significant energy savings. To support
the energy adaptivity in hardware, our designs leverage
emerging display technologies like OLEDs that provide
variability in the energy consumed based on the properties
of the pixels. For energy adaptivity at the software
level, we propose several dark windows optimizations
that allow the windowing environment to change
the brightness and color of portions of the screen that
are not of interest to the user. We develop prototypes of
the user interfaces and model the power benefits of such
energy adaptive designs. Our results indicate significant
energy reductions with acceptable tradeoffs in the user
interface.
In addition to the designs that we evaluate, we also
discuss other points in the design space including several
other alternative hardware and software interfaces
for energy-adaptivity. These designs are likely to achieve
even further energy benefits. Our work leads to several
interesting challenges including the design of energyaware
user interfaces as well as more intelligent heuristics
to automatically identify mismatches between the
workload/user intent and the display sub-system functionality.
As mobile systems continue to develop, the contribution
of the display power to the total mobile system
power is only likely to increase, particularly in larger
mobile devices. Similarly, as mobile workloads continue
to develop, mismatches between the display system required
for the most aggressive application and the needs
of the common-case workloads are only likely to get exacerbated.
The combination of these trends indicate a
huge potential for system designs that flexibly adapt their
energy consumption based on workloads needs. We believe
that energy-adaptive display designs like the ones
that we discuss in this paper are an extremely promising
approach to exploit this potential and that they will become
an important part of future mobile system designs.
7 Acknowledgements
This work greatly benefited from discussions with Ramakrishna
Anne, Bill Hamburgen, John Rudin, and Ken
Nicholas. We would also like to thank Norm Jouppi, Dan
Sieworek, Amin Vahdat, and the anonymous reviewers
for their comments on the paper. We would like to thank
John Sontag and Jim Rowson for their support of this
work. Finally, we would like to thank the users who participated
in our user study. Lu (Annie) Luo's work in
this paper was primarily supported by an HP internship.
Her research is also funded in part by NSF under award
number CCR-0205266 and by an IBM PhD Fellowship.
References
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Power Color TFT LCD Display for Hand-Held Embedded
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[2] Jason Flinn and M. Satyanarayanan. Energy-Aware Adaptation
for Mobile Applications. In Proceedings of the
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[3] Stanford Resources Inc., editor. Organic Light-Emitting
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[4] N. Kamijoh, T. Inoue, C. M. Olsen, M. T. Raghunath,
and C. Narayanaswami. Energy Trade-offs in the IBM
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For Future Mobile Environments
Abstract:
The utility of a mobile computer, such as a laptop, is
largely constrained by battery life. The display stands
Out as a major consumer of battery energy, so reducing
that consumption is desirable. In this paper, we motivate
and study energy-adaptive display sub-systems
that match display energy consumption to the functionality
Required by the workload/user. Through a detailed
characterization of display usage patterns, we show that
screen usage of a typical user is primarily associated with
content that could be displayed in smaller and simpler
Displays with significantly lower energy use. We propose
example energy-adaptive designs that use emerging
OLED displays and software optimizations that we call
Dark windows. Modeling the power benefits from this
approach shows significant, though user-specific, energy
benefits. Prototype implementations also show acceptability
of the new user interfaces among users.
SOFT COPY:
1 Introduction
With the increased acceptance and use of mobile
devices such as laptops and pocket computers, mobile
computing systems are rapidly becoming one of the key
markets of interest for computing systems. Since the batteries
on these mobile systems are typically limited in
capacity, reducing the energy consumption is one of the
key challenges in designing mobile systems.
Among the various components that contribute to the
consumption of electrical energy, the display sub-system
(the electronics associated with the visual representation
of the data generated by the system - namely the display
and the controller) often plays an important role.
For example, Udani and Smith report that the display
component of the system can easily take over half the
total energy of a laptop system [7]. Similarly, Choi et al.
report that the display component of the system consumed
close to 61% of the total power of the system for a
handheld device. Furthermore, unlike some of the other
components of the system, display power consumption
has traditionally been relatively invariant across technology
shrinks and unamenable to opportunities to exploit
“slack,” [5] making it a likely greater fraction of the total
power of future systems.
Previous approaches to reducing display power consumption
have either focused on aggressively turning off
the entire display when it is not being used or have resorted
to designing systems with lower-quality or smaller
sized displays to minimize power. However, new technologies,
such as Organic Light Emitting Diodes (OLEDs),
are becoming available that allow lower power consumption
when a reduced area of the screen is in use.
We propose utilizing this new flexibility to reduce energy
consumption. Our work is based on the intuition
that different workloads and users have varying display
needs. Having a “one-size-fits-all” display targeted at
the needs of the most aggressive workload/user often
leads to large energy inefficiencies in the display energy
consumptions of other workloads and users. Consequently,
an energy-adaptive system design that consumes
energy only on portions and characteristics of the
screen that are being used by the application and are relevant
to the user, can achieve energy benefits.
In this work, we make two key contributions. First, we
perform a detailed characterization of the display screen
usage of a representative test user population. Our results
indicate that, on average, our users use only about
60% of the screen area available to them. Addition-
ally, screen usage is often associated with content that
could have been equivalently displayed, with no loss in
visual quality, on much simpler lower-power displays
(lower size, resolution, color, brightness, refresh rates,
etc.) Using a detailed analysis of the user traces, we
also correlate our results to user and application behavior.
To the best of our knowledge, our work is the first to
identify, quantify, and analyze such mismatch opportunities
in workload/user needs and current display properties.
Overall, our results indicate that an energy-adaptive
system design that matches display power consumption
to the functionality required by the workload/user can
significantly reduce the energy consumption of future
display sub-systems.
Building on the insights from the above analysis,
we propose example energy-adaptive display system
designs. At the hardware level, our designs leverage
emerging Organic LED (OLED) displays [3] that use energy
proportional to the overall light output of the display.
At the software level, we propose dark windows
optimizations that enable the windowing environment to
change the brightness and color of areas of the screen that
are not of interest to the user. We model the power bene-
fits and study the user experience with our designs. Our
results indicate significant, though user-specific, energy
reductions with acceptable user interfaces.
The rest of the paper is organized as follows. Section 2
discusses our user study in greater detail. Section 3 discusses
the example energy-adaptive display systems that
we consider and our experiences with those. Section 4
further explores the design space with energy-adaptive
displays and Section 5 discusses related work. Section 6
concludes the paper and discusses future work.
2 User Study
This section presents the results from our user study
characterizing typical system usage of a representative
user test population. Our goal was to understand
the screen usage patterns and identify opportunities for
power reduction. Section 2.1 discusses our methodology
and Section 2.2 discusses our results.
2.1 Methodology for the user study
Our user study is based on usage of the MicrosoftWindows
environment by seventeen users. We chose Microsoft
Windows environments because of their widespread
acceptance and representativeness of the general
mobile market. The users were chosen to include characteristics
representative of typical usage and cover a cross
section of mobile system usage (administrative tasks,
code development, personal productivity, entertainment,
etc.). Since we were interested in understanding both
current and future user behavior when using mobile environments,
we studied both laptop and desktop users.
(Many of our laptop users used their machines as their
main machine - both as a desktop and a laptop.) The
systems used by the test users include a variety of screen
sizes and display resolutions. Column 2 in Figure 1 summarizes
the properties of the systems used by our test
population.
An application-level logger program was run on the
users' machines for times ranging from 1 to 14 days. The
logger program was used to collect periodic information
about (i) the current window of focus – its size, its location,
and its title and (ii) the size of total screen area used
(all non-minimized windows). Our sampling rate was set
to once a second. Screen savers were set to turn on after a
reasonable time (1-5 minutes) to allow us to isolate only
the usage patterns when the user was active.
Column 3 in Figure 1 summarizes the length of the
user traces. The traces range from 9 hours to 346 hours.
The variation in the traces represent the differences in
how individual users used their machines during their
participation in the study. Overall, our samples represent
close to 100 days of continuous computer usage time.
Column 4 in Figure 1 summarizes the length of the
“active” user traces, after factoring out the time spent in
the screen saver as an indication of the time the user was
idle. Traces are still collected during the time it takes
for the screen saver to be activated, but given the length
of our logs, the effect of this is minor. The sizes of our
active user logs range from about 6 hours to 61 hours of
computer usage per user. Given that this is the time we
are interested in, the rest of the paper will focus on the
active window usage without considering the time spent
in the screen saver. Existing technologies can save power
spent in the idle time by turning off the displays.
2.2 User study results
Average screen usage. Figure 1 summarizes the information
Screen usage corresponding to application behavior.
In order to understand the relationship between the
screen usage and the application behavior, we took the
samples from each of our user logs and categorized them
into four bins – (i) samples where the window of focus
usage was between 0 and 25% of the total screen area, (ii)
samples where the window of focus usage was between
25% and 50%, (iii) samples where the window of focus
usage was between 50% and 75%, and (iv) samples
where the window of focus usage was between 75% and
100%. For each bin, we then analyzed the key applications
associated with the samples. Figure 4 summarizes
our results. As before, we compute the arithmetic mean
of the averages per individual users to avoid distortions
due to trace lengths.
Overall, the workloads used by our user population
span a range of applications representative of typical
system usage. Broadly, they can be categorized into
(i) access related - web browsing and e-mail (Internet
Explorer, Netscape, Outlook, Pachyderm mail reader,
Messenger), (ii) personal productivity and code development
(Word, Emacs, Powerpoint, Excel, Visual studio,
Dreamweaver, X-term, Realplayer, Image viewer,
Acrobat reader, Ghostview), and (iii) system related and
application control windows (File Explorer, navigation
windows, taskbars, menus, status and properties messages,
confirmation and password query windows).
Focusing on the windows associated with the various
applications, we observe two interesting trends. First,
Active area is 0-25% (23% of the time for typical user)
Key applications: 20% Task bar, 15% Program Manager, 5% X-term, 60% miscellaneous windows (message composition,
MSN messenger, real player, menu and message windows – properties, connection status, file downloads, alerts
and reminders, volume control, printer status, find-and-replace, organizer preferences, file explorer, spell-check, wizards,
status messages, file-find, password query windows, confirmation windows)
Active area is 25-50% (22% of the time for typical user)
Key applications: 19% X-term, 18% message composition, 6% internet explorer, 57% miscellaneous windows (mailrelated
windows, file explorer, emacs and notepad, MSN messenger chat windows, other status windows)
Active area is 50-75% (28% of the time for typical user)
Key applications: 33% Internet Explorer, 24% mail composition and reading, 43% miscellaneous windows (emacs and
notepad, Image editor and photo viewer, messenger chat windows, Frontpage, Framemaker and ghostview, file explorer,
Powerpoint, dreamweaver, winlogger)
Active area is 75-100% (27% of the time for typical user)
Key applications: 21% Outlook, 20% Internet Explorer, 7% Excel, 52% miscellaneous windows (Powerpoint, Framemaker,
Acrobat reader,Word [various files], Visual C++ [various files]), Dreamweaver, Imageviewer)
Figure 4: Understanding screen usage by application. Windows are classified based on their sizes into four bins, and
for each of the four bins, the key applications dominating the samples in the bin are summarized.
system-related status messages and query windows typically
use small window sizes; in fact, these windows
constitute a significant fraction of the samples associated
with smaller size windows. Additionally, these windows
usually display fairly low content that do not need the
aggressive characteristics of the display – for example,
a low-resolution display with support for a small number
of colors would be adequate to obtain an equivalent
user experience. Second, personal-productivity applications
and development environments and web-browsing
and e-mail applications typically use larger portions of
the display area. The actual fraction of the screen area
used appears to be highly dependent on individual user
preferences for window size, fonts, etc. However, even
with these large windows, characteristics of the displays
such as resolution, brightness, and color are not used to
their full capacity.
Screen usage corresponding to user behavior. Focusing
on the individual user logs, we observe that individual
user preferences and pre-set defaults tend to significantly
influence the overall screen usage characteristics.
For example, User 1 who, on average uses 63% of
the display area, has Internet Explorer set to use 96%
of the screen area, while User 5 who, on average uses
37% of the display area, has Internet Explorer set to use
67% of the display area. Similarly, User 12, who has
the largest screen usage in our study, has a default mail
composition window of 95% that dominates the traces.
This user-specific sizing of windows appears to be particularly
characteristic of web browsing, email, and editor
applications. In contrast, for development applications
(Visual Studio, and Dreamweaver, Powerpoint), most
of our users prefer to have larger windows – possibly
because of the multi-window content structure of these
applications. Similarly, system-related and applicationcontrol
messages typically use smaller windows irrespective
of the user – mainly since the content in these
windows is relatively low and in most cases the window
sizes are pre-determined by the application. An illustrative
examples is the case of User 8 who maximized
all windows as a matter of routine (“to be able to read
better”). This user still consumes only 85% of the total
screen area because of the smaller window sizes associated
with system-related and application-control messages.
Finally, while the laptop users have a slightly larger
screen usage (62%) than the desktop users (56%), in
general, the results are fairly similar over the laptop and
desktop users.
2.3 Summary
Summarizing the results of our user study, overall,
there is a significant mismatch between the properties
supported in the display and the actual usage of these
attributes by the users in our user study. The size of the
display used exhibits the greatest mismatch – users use
only about 60% of the screen area available. A large fraction
of the smaller windows are typically associated with
system-related and application-control windows that are
independent of user preferences. User preference for
smaller window sizes and font sizes can also translate
into a greater use of smaller sized windows.
Similarly, there are significant mismatches between
the actual screen usage and other attributes of the display
such as resolution, brightness, color, refresh rate, etc. In
particular, most of the smaller windows include content
that could have been equivalently displayed, with no loss
in visual quality, on much simpler lower-power displays
(lower size, resolution, color, brightness, refresh rates,
etc.) Many of the larger windows also do not use all the
aggressive characteristics of the display.
Overall, these results indicate that energy-adaptive
system designs that match display power consumption
to the functionality required by the workload/user have
significant potential to reduce the energy consumption of
the display sub-system.
3 Energy-adaptive Display Sub-systems
This section studies some example energy-adaptive
systems to evaluate how the potential benefits identified
in the user study can be translated to energy reductions.
Section 3.1 describes the hardware and software components
of these designs. Section 3.2 discusses the experimental
methodology used in prototyping and studying
the user interfaces and the energy consumption for
the different designs. Section 3.3 discusses our results
and Section 3.4 summarizes.
3.1 System Design
Our designs use emerging display technologies and
modified window system software to exploit the mismatches
between workload/user requirements and display
properties.
Hardware Support: OLED displays. To enable
energy-adaptive designs, a key requirement is support
at the display sub-system for variability in the display
power based on the properties of the screen output.
That is, it should be possible to change the energy consumption
of regions of the display independent of each
other. Several emerging display technologies support
such variable power over different regions of the screen.
This is preferable to existing technologies, where energy
tradeoffs have to be made for the entire screen.
Organic Light Emitting Diode (OLED) displays [3]
are a good example of this class. In OLEDs, the energy
consumption of a pixel is related to its brightness and
color. OLED displays are built from small organic molecules
that efficiently emit light when stimulated by an
electric field. More than 100 companies are developing
aspects of OLED technology. Kodak, Sanyo, and Sony
have shown prototypes from 5.5-inch displays to 13-inch
displays at trade shows. In general, OLEDs have better
image quality compared to conventional LCDs (better
horizontal and vertical viewing angles, higher brightness,
and faster response times) and do not need a separate
backlight, resulting in lower power. As the technology
matures, the biggest challenges are in overcoming
yield problems and consequently reducing costs. Small
OLED displays are currently available in devices like cell
phones. Larger displays, for handhelds and laptops, are
expected in 2004 [3].
For our prototype, we assumed a laptop system with
a 15” AMOLED (Active Matrix Organic Light Emitting
Diode) display. (Section 4 discusses other hardware approaches
to implement the similar functionality.) The
only hardware changes to a current commodity design
will be to replace the conventional LCD panel, backlight,
and controller with their OLED equivalents.
Software Support: Dark Windows. The software support
for energy-adaptive displays can be implemented at
one of several levels: the application level, the window
system level, or the operating system level. In this paper,
we focus on modifications to the windowing system.
Specifically, the “Dark-Windows” modifications use the
window of focus as an approximation of the active area
of user interest to ensure that energy is spent mainly
on portions of the screen that have the user's attention.
For example, if a user is using an editor to take notes,
her attention is typically on the screen area used by
her editor, her current window of focus. Energy-aware
changes to the brightness, color, or other properties
of the remaining portions of the screen can result in
energy benefits without impacting the user experience.
Our discussions below focus on optimizations that
automatically change the luminescence and color of
non-active screen areas to reduce power. Section 4
discusses other properties of the background that can
be changed and optimizations that focus on mismatches
internal to the window of focus.
3.2 Prototyping and Evaluation Methodology
Our goals with the evaluation were two-fold. First,
we wanted to understand the intrusiveness of the energyadaptive
user interfaces and the complexity and overhead
associated with implementing the dark windows
optimizations. Second, we also wanted to quantify the
energy benefits in the context of one particular technology,
namely OLED displays, and understand the design
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Figure 5: Methodology used to prototype the user interface.
Interface Name Modification
HalfDimmed Areas outside the window of focus
are dimmed by 50%.
FullyDimmed Areas outside the window of focus
are fully dimmed (turned off).
GrayScale Areas outside the window of focus
are changed to gray, by setting red,
green, and blue values to the average
of the three.
GreenScale Areas outside the window of focus
are changed to green (lowest power
color for OLEDs). The green value
is set to the average of the three colors,
and the red and blue values are
zeroed. This also dims the screen
by 67%.
Figure 6: Summary of the dark windows optimizations
considered. The window of focus is left untouched, while
the background areas are modified in either brightness
or color to save energy.
tradeoffs between the various optimizations.
Prototyping the User Interface We implemented several
prototypes in order to understand the impact of the
dark-windows changes on the user interface. Given the
difficulties with modifying the Microsoft windowing environments,
we implemented our prototypes on the X
Window System under Linux.
To enable a clean implementation of the changes, we
used the open-source VNC (Virtual Network Computing)
server [6]. VNC provides a virtual representation of
the display hardware, i.e. a remote framebuffer, and allows
one to run a server on a machine while viewing its
display remotely from anywhere on the internet. While
the remote viewing property was not of interest to us, by
using VNC, we were able to gain two advantages. First,
since it is a virtual frame buffer, we were able to easily
manipulate the pixel values. Secondly, since VNC
is linked with the X-Windows server, we had access to
the window server data structures, such as the window of
focus.
Figure 5 shows an overview of our changes. We modified
the X-VNC server with two additional functions –
to track the window of focus and other objects in the
display area, and to change the values of the pixels in
the frame buffer based on our dark-windows algorithms.
To reduce the performance overhead from our software
changes, our modifications closely track the incremental
change update mechanism of the VNC protocol.1 Each
framebuffer update represents a region that has changed,
and is sent to the viewer as required to keep the display
up-to-date. Before each frame buffer update is sent to
the viewer, we perform two functions. First, we query
the X-server and record the window of focus – the window
that keyboard events are directed to – including its
origin, width, and height. We then divide the update region
into two groups – those that are in the window of
focus and those that are outside it. The pixels within the
windowof focus are sent unmodified, while the rest have
the desired transformation applied to them. Overall, the
performance overhead of our implementation was negligible
with performance acceptable to users.
We experimented with several transformations, described
in Figure 6. These included transformations that
modified the brightness of the background (non-windowof-
focus) regions (HalfDimmed and FullyDimmed2) as
well as transformations that modified the color of
the background regions (GrayScale and GreenScale).
(Screenshots of these interfaces are shown in Figure 8
in the next section.) In all these cases, we also implemented
a user-defined keyboard short-cut to be able to
turn off the modifications if desired.
Quantifying energy benefits for typical users. Though
we used VNC for implementation convenience, we still
wanted to focus our benefits study on the commodity Microsoft
Windows based systems used in our user study.
We therefore constructed a synthetic trace that modeled
the average behavior observed in our user study and re-
1We originally chose a simpler implementation approach where we
simply traversed the finally-generated framebuffer to apply our modi-
fication heuristic on each pixel. However, this led to an inordinately
large number of computations and caused a perceptible slowdown in
the system. We chose to instead use the rectangular update mechanism
to obviate this problem.
2Our optimization allows the brightness to be dimmed by any userdefined
fraction; however, in this paper, for space reasons, we consider
only two representative data points - half-dimmed and fully-dimmed.
Avg. window size 59%
Avg. background windows' size 17%
0-25% screen usage 23%
(taskbar, xterms, miscellaneous)
25-50% screen usage 22%
(xterms, editors, mail readers, miscellaneous)
50-75% screen usage 28%
(web browsers, mail readers, editors, miscellaneous)
75-100% screen usage 27%
(web browsers, mail readers, miscellaneous)
Default screen background Teal
Default window color White
Default foreground font Black
Figure 7: Summary of properties of synthetic trace. The
trace models the average behavior exhibited in the user
study and was created to match the data from Figure 4.
played it on our prototype systems. The trace runs for
about 1000 seconds and uses a set of applications similar
in nature to those used in the user study. The script
for the trace models the initiation and termination of applications
and varies the window sizes and the duration
of time windows were open to obtain statistics consistent
with the user study. While the users in our test population
used a variety of backgrounds and window colors, a
majority of them used the default windows settings. Our
synthetic trace therefore predominantly uses those colors.
To bracket the impact of specific user choices in this
respect, we also performed experiments where the screen
background and windowcolors are varied. Figure 7 summarizes
the window properties of the synthetic trace and
a comparison of the data with that in Figure 4 shows the
close correlation between the behavior of the trace and
the user study.
Given the current unavailability of 15” OLED displays
(aside from rare prototypes), we decided to use a software
power model representative of future systems. The
model computes the display sub-system power as the
sum of the controller power, the driver power, and the
display panel power. The controller and driver power are
modeled as constant values irrespective of what is displayed
on the screen. The panel power represents the
pixel array power and is the total of the power consumed
by each pixel in the panel array. In turn, the power consumed
by each pixel is proportional to the brightness of
the red, green, and blue components.
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ ! _ _ _ _ _ _ "
The values of the parameters of the power model were
chosen to represent likely future OLED displays, and
were validated by discussions with researchers working
in the area and by inspecting data sheets of current displays.
For the 15” display we model, we set # $ % & ' ( % ) * ( to 0.5W and # + ( , - * ( to 1W. The # . / 0 1 # 2 3 0 4 can be
a maximum of 8.5W. Interestingly, with current OLED
technologies, pixels consume different amounts of power
based upon the particular color, with green typically being
the lowest. For a 1024x768 resolution display with
pixel values ranging from 0 (off) to 1 (fully on), the values
for # ( * + 5 # 6 ( * * & 5
and # 7 ) 8 *
are 4.39 W, 2.29 W, and
4.39 W.
3.3 User Experience and Energy Benefits
User Interface Intrusiveness. Figure 8 summarizes the
various interfaces. (Color pictures are available in the
electronic version of the paper.) The picture on the left
represents the original configuration, while the two pictures
on the top right represent dark-window configurations
with modified background brightness, HalfDimmed
and FullyDimmed. The two pictures on the bottom right
represent the dark-window configurations with modified
background colors, GrayScale and GreenScale. The performance
overhead of the dark windows implementation
was small and there was no significant difference in the
response time of the user interfaces.
To determine the difference in the quality of the various
interfaces, we informally surveyed 9 users. We asked
each user two questions. First, without describing any
battery life issues, we asked users to choose their best interface.
Second, we showed the users the battery life advantages
from the various dark-windows modifications
and then asked users to choose their best interface again,
with the assumption that they were in a situation that
required long battery life, such as a meeting or an airplane
trip. Most of the users indicated a willingness to
use dark windows optimizations to tradeoff longer battery
life for a different user interface. Of the 9 users
we queried, 4 of them preferred GreenScale, 3 preferred
HalfDimmed, and 2 preferred FullyDimmed. Most of the
users expressed a desire to be able to see the contents
of the background, even at the expense of some energy.
Interestingly, even without an awareness of the energy
benefits, four of the users chose some of the dark windows
interfaces (GrayScale and HalfDimmed) as their
preferred interface. Note that even without the software
dark windows' optimizations, the base energy-adaptive
display hardware still provides the ability to have lower
power consumption based on what is displayed on the
screen. Compared to LCDs, this can achieve some power
Original Interface
Dark Windows – Brightness Control
Dark Windows – Color Control
Background half dim Background fully dim
Background grayscale Background greenscale
Figure 8: Screen shots of the user interfaces with dark windows optimizations.
0
2.5
5
7.5
10
LCD OLED Half
Dimmed
Fully
Dimmed
Gray Scale Green
Scale
Power consumption (Watts)
Figure 9: Power benefits from energy-adaptive display
designs.
benefits with no changes in the user interfaces.
Our user interfaces could be improved in several ways
(we discuss these in Section 4). However, our goal was
not to perform an exhaustive exploration of the design
space for user interfaces, but instead to establish that
energy-adaptive display sub-systems can provide energy
benefits with interfaces that users are likely to find acceptable,
particularly in return for longer battery lifetimes.
Energy Measurements. Each of our interfaces modi-
fies the pixels in a different manner, resulting in differing
energy use. We measure the power consumed by our
synthetic trace for each configuration on our modified
VNC server. We study a default LCD configuration, a
default OLED configuration, and four OLED configurations
with the four dark windows optimizations discussed
earlier. We use the OLED power model discussed earlier
to compute the power for the five OLED configurations.
The LCD power model is based on characteristics noted
by Choi et al. [1]. For our synthetic trace, this leads to
very little variance (less than 1%).
Figure 9 summarizes the results. Compared to the constant
10W power consumption of the LCD, the hardware
support for energy-adaptive displays in the base OLED
system achieves a 25% reduction in power. Most of these
benefits are due to power reductions for the teal background
color. (With our OLED display, a teal pixel -
RGB: [0,131,131] consumes only 30% of the maximum
power a pixel can consume - RGB: [255,255,255].)
However, there are also some benefits from power reduction
specific to the content of the windows (e.g.,
web browser). The software dark windows changes
can provide additional power reductions. The Fully-
Dimmed optimization provides an additional 20% over
0
2.5
5
7.5
10
1 96 191 286 381 476 571 666 761 856 951 1046 1141 1236 1331 1426 1521 1616 1711 1806 1901 1996 2091 2186
Power (Watts)
0%
50%
100%
150%
200%
250%
300%
350%
400%
450%
500%
550%
600%
650%
700%
750%
800%
850%
900%
950%
1000%
1050%
1100%
1150%
1200%
LCD
Fully
Dimmed
OLED
window of focus variation
Figure 10: Power variation over time for non-adaptive and energy-adaptive displays.
the OLED case, and a total of 43% over the LCD case.
These benefits are from dimming of the background windows
(predominantly white) and the screen background
(teal). HalfDimmed provides half these benefits while
still allowing some of the information to be visible.
The GreenScale optimization provides 40% energy benefits
compared to LCD (and a 15% benefit compared to
OLED). This optimization computes the average of the
R, G, B values and includes it as the new value for the
green pixel. (Recall that the green pixel takes the lowest
energy compared to the other pixels.) The combination
of using the most energy-efficient color and a reduction
in brightness by 67% leads to the energy benefits. In
contrast the GrayScale optimization averages the R, G,
B values and uses this average as the new values for the
R, G, and B pixels, turning them gray. However, our
results indicate a 1% increase in energy with this configuration
compared to OLED (though still 28% better than
the non-adaptive LCD case). This is because converting
our default background color (RGB: [0,131,131]) to gray
scale ends up moving more bits to the higher-power R
and B pixels (gray-RGB: [87,87,87]). An alternate background
color such as pure blue or red may have benefited
from having a grayscale background optimization.
Figure 10 shows the power variation over time for
three cases - LCD, OLED, and FullyDimmed. The
LCD case represents an non-energy-adaptive display and
shows relatively constant power consumption, invariant
to the size and content of what is displayed on the screen.
The OLED case shows the benefits that can be obtained
from using an energy-adaptive display technology. Since
the benefits are mainly from the background screen color,
the profile of the curve follows the profile of the percentage
of the screen devoted to the background (after
removing the window of focus and other background
windows). The FullyDimmed shows the benefits from
both hardware and software changes for energy adaptivity.
The profile of this curve follows the profile of the
window of focus curve.
As is evident from the above discussion, the benefits
from energy-adaptive display designs are highly dependent
on the choices of the background color and the window
color. Our default trace modeled the windows default
since that most closely represented a majority of
the users in our test population. To better bracket the impact
of other choices of background and window colors,
Figure 11 shows the energy consumption of the various
configurations for the extreme cases of pure white and
pure black backgrounds and window colors. With black
backgrounds and black windows, the base OLED design
achieves most of the benefits from energy adaptivity –
close to 80% reductions compared to LCD. The power
consumed is mainly due to the controller and driver and
minor elements of windows such as title bars, etc. Additional
software optimizations get minimal improvements
on top of this. In contrast, with white backgrounds and
white windows, the base OLED configuration gets close
to no improvements. The FullyDimmed system gets the
maximum benefits (35%) by reducing the power spent
on the background. The other two cases show intermediate
points where both the hardware and software optimizations
obtain good benefits. These results indicate
0
1
2
3
4
5
6
7
8
9
10
LCD
OLED
Half
Fully
Gray
Green
LCD
OLED
Half
Fully
Gray
Green
LCD
OLED
Half
Fully
Gray
Green
LCD
OLED
Half
Fully
Gray
Green
Black Windows White Windows Black Windows White Windows
Black Background Black Background White Background White
Background
Power (Watts)
that different designs may be better for different usage
scenarios. It is important for designers to understand
typical user behavior before designing energy adaptive
display sub systems. In some cases, it might be adequate
to just include support for adaptivity at the display hardware
level and choose color and size defaults with power
in mind. In general, however, for systems that allow a
great flexibility in user choices for background and window
colors, it might be preferable to implement software
optimizations in addition to the base hardware support
for adaptivity.
4 Discussion
The configurations discussed in the previous section
illustrate some example energy-adaptive designs. In this
section, we discuss other possible energy-adaptive options
for display design.
Display energy adaptivity in hardware. Rather than
using OLEDs, other display technologies that enable
energy-adaptivity can be used. These include other optoelectronic
and emissive displays (Field-Emission Displays
[FEDs] and even conventional Cathode-Ray-Tube
[CRT] displays) as well as hybrid technologies like LCD
displays with OLED backlights. With display technologies
like LCDs that do not support energy variability,
designs can still integrate a multi-modal “hierarchy of
displays” configuration. For example, a mobile device
could have two displays – one higher quality (high resolution,
color, high refresh rate, larger size) and consequently
higher energy use, and another lower quality
and lower energy. While the adaptivity in this design is
more coarse-grained than with the OLED systems considered
in this paper, the insights from our study are still
likely to be valid.
Display energy adaptivity in software. Rather than using
the window of focus, other indications of user activity
could be used.
cursor could be kept bright, while the rest of the screen
is dimmed. Another design dimension is to include support
for user-controlled dimming areas. For example,
one possible user interface could include a “sticky-lamp”
placed by the user to light up a specific portion of the
screen. Much as we do in the physical world, the user
could use multiple “sticky-lamps” to light up the workarea.
An alternate implementation could include a “headlight”
on the mouse pointer. The users could then point
the light over regions that they are interested in and move
down as they read along.
Still other dimming interfaces could be application
specific. For instance, in a programming environment,
there may be a concept of the current procedure and related
variables. Portions of the screen related to these
could be made bright – for example, all uses of the variables
and all calls to the procedure. In an email application,
perhaps only the current message needs to be bright.
In a word processor, the line of text being edited could be
bright, the surrounding couple of lines lightly dimmed,
and the rest of the document greatly dimmed. Similarly,
in the case of applications like Microsoft Powerpoint
which use frames within an application, the notion
of a frame-of-focus can be defined, similar to the window
of focus. As another dimension to these user interfaces,
these can all be made time-based. For example, areas
of the screen that have recently changed could be bright,
fading to a dim value as time progresses. When inactivity
is detected, an email application could dim its screen
area until new mail arrives.
Other user interfaces can be developed by combining
the interfaces above. Additionally, other sorts of display
mismatches could be exploited. In this paper, we have
focused on identifying the mismatch between the total
area of the display and the area of interest to the user.
Other properties of the display, such as resolution and
refresh rate could be exploited as well.
Support for output modes beyond displays. The notion
of having multiple displays can be taken one step
further to match output content to notification mechanisms
beyond displays. For example, an email notification
that says “You have mail” on the display could be
replaced by an LED that blinks on the arrival of email
or other similar notification mechanism such as speech
output, vibrations, etc.(Figure 12). As discussed in Section
2, a lot of the smaller windows are typically lowcontent
windows which may be amenable to other forms
of non-visual communication. This combined with the
large design space for alternatives for energy adaptiveness
indicate the potential for an interesting future area
of research – energy-aware user interfaces.
5 RelatedWork
Concurrent with our work, there are two other studies
that have looked at adapting the output of the displays
from an energy perspective. Choi et al. [1] perform
a detailed characterization of the power consumption of
the display sub-system of a handheld device (including
the power of the panel, the panel bus, the backlight, the
frame buffer and the data and address bus driving the
frame buffer). They propose three optimizations that (1)
vary the refresh rate to exploit the after-image caused
by the time constant of the storage capacitors, (2) vary
the color depth to be able to reduce the memory requirements
and hence the memory power, and (3) vary the
backlight luminance with a corresponding compensation
of the brightness and contrast. Kamijoh et al [4] discuss
the energy trade-offs in the IBM wristwatch computer.
While they focus mainly on the hardware-level tradeoffs
and kernel optimizations to use the various standby and
idle configurations of hardware, they also discuss the
implication of controlling the number of pixels turned
on or off on the energy as well as reducing the duty
factor of the display to control the brightness of the entire
screen (e.g., at night). Additionally, while not focusing
mainly on the display component of the power, Flinn
and Satyanarayan [2] also evaluated in detail the energy
benefit of reduced computation with lowered fidelity of
images for web browsing and video playing. Their study
also proposed a method called ”zoned backlighting,” to
enable energy benefits in the display subsystem. While
zoned backlighting could allow independent control of
illumination level for different regions of the screen, no
existing display supports such zoned backlighting yet.
Similar to these studies, our work also explores the
possibility of adapting the output of the displays from an
energy perspective. However, in contrast to the studies,
we primarily focus on the content and intent of the output
screen display. In particular, our work is the first to
perform a detailed characterization of display usage patterns
to identify and understand the common mismatches
between workload/user needs and current display properties.
Based on these insights, our work also explores
several new user interfaces and hardware designs that allow
energy-adaptive control on the portions of the screen
that are not of immediate relevance to the user, while
continuing to provide similar functionality on portions
of the screen of relevance to the users.
Finally, this work focuses mainly on the display panel
power. Complementary to our work, other studies have
also focused on the power consumed by the display controller
and driver (e.g., [8]).
6 Conclusions
As mobile systems, applications, and services become
more pervasive, it becomes ever more important to
identify design strategies to lower energy consumption
and increase battery lifetimes. This paper focuses on the
display sub-system and motivates and evaluates energyadaptive
system designs for future mobile environments.
The first part of our study performs a detailed analysis
of display usage traces from 17 users, representing a few
hundreds of hours of active usage. To the best of our
knowledge, this study is the first to identify, quantify,
and analyze potential mismatch opportunities in workload/
user needs and current display properties. We find
that on average, the window of focus – a good indication
of the area of interest to the user – uses only about 60%
of the total screen area. Additionally, in many cases, the
screen usage is associated with content that could have
been equivalently displayed, with no loss in visual quality,
on much simpler lower power displays. Our analysis
of the user traces indicates that many of these mismatches
could be traced back to the typical content of the
windows as opposed to specific user preferences.
Based on these insights, the second part of the study
proposes energy-adaptive display systems that match
energy use to the functionality required by the workload/
user to obtain significant energy savings. To support
the energy adaptivity in hardware, our designs leverage
emerging display technologies like OLEDs that provide
variability in the energy consumed based on the properties
of the pixels. For energy adaptivity at the software
level, we propose several dark windows optimizations
that allow the windowing environment to change
the brightness and color of portions of the screen that
are not of interest to the user. We develop prototypes of
the user interfaces and model the power benefits of such
energy adaptive designs. Our results indicate significant
energy reductions with acceptable tradeoffs in the user
interface.
In addition to the designs that we evaluate, we also
discuss other points in the design space including several
other alternative hardware and software interfaces
for energy-adaptivity. These designs are likely to achieve
even further energy benefits. Our work leads to several
interesting challenges including the design of energyaware
user interfaces as well as more intelligent heuristics
to automatically identify mismatches between the
workload/user intent and the display sub-system functionality.
As mobile systems continue to develop, the contribution
of the display power to the total mobile system
power is only likely to increase, particularly in larger
mobile devices. Similarly, as mobile workloads continue
to develop, mismatches between the display system required
for the most aggressive application and the needs
of the common-case workloads are only likely to get exacerbated.
The combination of these trends indicate a
huge potential for system designs that flexibly adapt their
energy consumption based on workloads needs. We believe
that energy-adaptive display designs like the ones
that we discuss in this paper are an extremely promising
approach to exploit this potential and that they will become
an important part of future mobile system designs.
7 Acknowledgements
This work greatly benefited from discussions with Ramakrishna
Anne, Bill Hamburgen, John Rudin, and Ken
Nicholas. We would also like to thank Norm Jouppi, Dan
Sieworek, Amin Vahdat, and the anonymous reviewers
for their comments on the paper. We would like to thank
John Sontag and Jim Rowson for their support of this
work. Finally, we would like to thank the users who participated
in our user study. Lu (Annie) Luo's work in
this paper was primarily supported by an HP internship.
Her research is also funded in part by NSF under award
number CCR-0205266 and by an IBM PhD Fellowship.
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