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An Analysis of Power Consumption in a Smartphone

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Mobile consumer-electronics devices, especially phones, are powered from batteries which are limited in size and therefore capacity. This implies that managing energy well is paramount in such devices. Good energy management requires a good understanding of where and how the energy is used. To this end we present a detailed analysis of the power consumption of a recent mobile phone, the Openmoko Neo Freerunner. We measure not only overall system power, but the exact breakdown of power consumption by the device's main hardware components. We present this power breakdown for micro-benchmarks as well as for a number of realistic usage scenarios. These results are validated by overall power measurements of two other devices: the HTC Dream and Google Nexus One. We develop a power model of the Freerunner device and analyse the energy usage and battery lifetime under a number of usage patterns. We discuss the significance of the power drawn by various components, and identify the most promising areas to focus on for further improvements of power management. We also analyse the energy impact of dynamic voltage and frequency scaling of the device's application processor.
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An Analysis of Power Consumption in a Smartphone
Aaron Carroll
NICTA and University of New South Wales
Aaron.Carroll@nicta.com.au
Gernot Heiser
NICTA, University of New South Wales and Open Kernel Labs
gernot@nicta.com.au
Abstract
Mobile consumer-electronics devices, especially phones,
are powered from batteries which are limited in size and
therefore capacity. This implies that managing energy
well is paramount in such devices.
Good energy management requires a good understand-
ing of where and how the energy is used. To this end we
present a detailed analysis of the power consumption of
a recent mobile phone, the Openmoko Neo Freerunner.
We measure not only overall system power, but the exact
breakdown of power consumption by the device’s main
hardware components. We present this power breakdown
for micro-benchmarks as well as for a number of realis-
tic usage scenarios. These results are validated by over-
all power measurements of two other devices: the HTC
Dream and Google Nexus One.
We develop a power model of the Freerunner device
and analyse the energy usage and battery lifetime under
a number of usage patterns. We discuss the significance
of the power drawn by various components, and identify
the most promising areas to focus on for further improve-
ments of power management. We also analyse the energy
impact of dynamic voltage and frequency scaling of the
device’s application processor.
1 Introduction
Mobile devices derive the energy required for their op-
eration from batteries. In the case of many consumer-
electronics devices, especially mobile phones, battery ca-
pacity is severely restricted due to constraints on size
and weight of the device. This implies that energy effi-
ciency of these devices is very important to their usabil-
ity. Hence, optimal management of power consumption
of these devices is critical.
At the same time, device functionality is increasing
rapidly. Modern high-end mobile phones combine the
functionality of a pocket-sized communication device
with PC-like capabilities, resulting in what are generally
referred to as smartphones [11]. These integrate such di-
verse functionality as voice communication, audio and
video playback, web browsing, short-message and email
communication, media downloads, gaming and more.
The rich functionality increases the pressure on battery
lifetime, and deepens the need for effective energy man-
agement.
A core requirement of effective and efficient manage-
ment of energy is a good understanding of where and how
the energy is used: how much of the system’s energy is
consumed by which parts of the system and under what
circumstances.
In this paper we attempt to answer this question and
thus provide a basis for understanding and managing
mobile-device energy consumption. Our approach is to
measure the power consumption of a modern mobile de-
vice, the Openmoko Neo Freerunner mobile phone, bro-
ken down to the device’s major subsystems, under a wide
range of realistic usage scenarios.
Specifically, we produce a breakdown of power distri-
bution to CPU, memory, touchscreen, graphics hardware,
audio, storage, and various networking interfaces. We
derive an overall energy model of the device as a func-
tion of the main usage scenarios. This should provide
a good basis for focusing future energy-management re-
search for mobile devices.
Furthermore, we validate the results with two addi-
tional mobile devices at a less detailed level: the HTC
Dream and Google Nexus One. Along with the Freerun-
ner, these three devices represent approximately the last
three to four years of mobile phone technology.
The paper is structured as follows. In Section 2 we
describe our measurement platform and benchmarking
methodology. Section 3 describes each experiment and
presents the results, and in Section 4 we perform a
coarse-grained validation of the results. We then analyse
this data in Section 5. Section 6 surveys existing work.
Finally, we conclude in Section 7.
2 Methodology
Our approach to profiling energy consumption is to take
physical power measurements at the component level on
a piece of real hardware. In this section, we describe
the hardware and software used in the experiments, and
explain our benchmarking methodology.
There are three elements to the experimental setup:
the device-under-test (DuT), a hardware data acquisition
(DAQ) system, and a host computer.
2.1 Device under test
The DuT was the Openmoko Neo Freerunner (revision
A6) mobile phone. It is a 2.5G smartphone featuring a
large, high-resolution touchscreen display, and many of
the peripherals typical of modern devices. Table 1 lists
its key components. The notable differences between our
device and a modern smartphone are the lack of a camera
and 3G modem.
Component Specification
SoC Samsung S3C2442
CPU ARM 920T @ 400 MHz
RAM 128 MiB SDRAM
Flash 256 MiB NAND
Cellular radio TI Calypso GSM+GPRS
GPS u-blox ANTARIS 4
Graphics Smedia Glamo 3362
LCD Topploy 480 ×640
SD Card SanDisk 2 GB
Bluetooth Delta DFBM-CS320
WiFi Accton 3236AQ
Audio codec Wolfson WM8753
Audio amplifier National Semiconductor LM4853
Power controller NXP PCF50633
Battery 1200 mAh, 3.7 V Li-Ion
Table 1: Freerunner hardware specifications.
This device was selected because the design files, par-
ticularly the circuit schematics [7], are freely available.
This is critical for our approach to power measurement,
which relies on understanding the power distribution net-
work at the circuit level. For this reason, few other de-
vices would be suitable.
The high-level architecture of the Freerunner is shown
in Figure 1. The total system memory is split equally be-
tween two banks, one external RAM package, and one
on-chip. All peripherals except the graphics chip com-
municate with the application processor (CPU) by pro-
grammed I/O over various serial buses.
The other devices studied, the HTC Dream (G1) and
Google Nexus One (N1), are described in Section 4.
Applications
Processor
NAND
SDRAM
GSM
GPS
Codec
Amp
WiFi
Bluetooth
Graphics SDRAM
LCD
SD Card
Host bus
I2C
SDIO
US B
serial
serial
Figure 1: Architecture of the Freerunner device, showing
the important components and their interconnects.
2.2 Experimental setup
To calculate the power consumed by any component,
both the supply voltage and current must be determined.
To measure current, we inserted sense resistors on the
power supply rails of the relevant components—this is
relatively simple on the DuT selected, since most of them
have been designed with placeholders for sense resistors,
factory-populated with 0 . Where this was not the case,
choke inductors could be reused in the same way. In both
cases, we replaced the part with a current-sense resistor
selected such that the peak voltage drop did not exceed
10 mV, which in all cases is less than 1 % of the supply
voltage and therefore presents an acceptably small per-
turbation. With a known resistance and measured voltage
drop, current can be determined by Ohm’s law.
To measure the voltages, we used a National Instru-
ments PCI-6229 DAQ, to which the sense resistors were
connected via twisted-pair wiring. The key characteris-
tics of this hardware are summarised in Table 2.
Characteristic Value
Max. sample rate 250 kS/s
Input ranges ±0.2V, ±1V, ±5V and ±10 V
Resolution 16 b
Accuracy 112 µV @ ±0.2 V range
1.62 mV @ ±5 V range
Sensitivity 5.2 µV @ ±0.2 V range
48.8 µV @ ±5 V range
Input impedance 10 G
Table 2: National Instruments PCI-6229 DAQ specifica-
tions [6].
The sense-resistor voltage drops were sampled differ-
entially at the ±0.2V input range. We used the same
physical connections to measure supply voltages; these
were taken relative to ground from the component side
of the resistors, in the ±5V range.
We were able to directly measure the power consumed
by the following components: CPU core, RAM (both
banks), GSM, GPS, Bluetooth, LCD panel and touch-
screen, LCD backlight, WiFi, audio (codec and ampli-
fier), internal NAND flash, and SD card. Since the graph-
ics module had too many supply rails to measure directly,
we instead used a combination of direct and subtractive
measurements.
Power to the DuT was supplied through a bench power
supply connected to the phone’s battery terminals so we
did not need to deal with battery management. This also
prevents the OS’s power policies from interfering with
the benchmarks. Total system power consumption was
measured at this point by inserting a sense resistor be-
tween the supply and the phone. For the G1 and N1 we
measured total system power by inserting a sense resistor
between the device and its battery.
Measuring backlight power required special attention,
because its supply voltage (10–15 V, depending on the
brightness) far exceeded the maximum range supported
by our DAQ hardware. To resolve this, we pre-scaled the
backlight voltage with some external circuitry, consist-
ing of a high-input-impedance voltage follower feeding
a fixed voltage divider. This brought the voltage within
the ±5V range.
2.2.1 Voltage regulation efficiency
Our measurement approach yields the power directly
consumed by each component. However, a certain
amount of additional power is lost in converting the sup-
ply (i.e. battery) voltage to the levels required by the
components. We have not included this factor in the
results reported, because the conversion efficiencies are
unknown. However, based on the data sheet of a similar
part (the NXP PCF 50606), the efficiency conversion is
likely to be in the range of 75–85 %, depending on the
current drawn.
Because of this, we differentiate between “total
power”, measured at the battery, and “aggregate power”,
measured as the sum of individual component measure-
ments. The latter assumes no power is consumed in
the non-instrumented components, and while we haven’t
been able to measure precisely what their contribution is,
it is certainly less than 10 %, and probably within a few
percent of the aggregate consumption.
One exception to this is the backlight boost converter,
the efficiency of which we measured to be 67 %. We de-
termined the cause of this poor efficiency to be heating in
an external component. We found no evidence to suggest
this is an issue for any of the other voltage regulators.
2.3 Software
The DuT ran the Freerunner port of the Android 1.5 op-
erating system [1] using the Linux v2.6.29 kernel. Ex-
cept for the CPU micro-benchmark, the kernel was con-
figured with the ondemand frequency scaling governor,
using 100 MHz and 400MHz—the only two frequencies
supported by both the hardware and OS.
On the host system we ran the power-data collection
software which interfaced with the National Instruments
DAQmxBase 3.3 library to collect raw data from the
DAQ, aggregate it, and write the result to file for post-
processing. Each data point collected was an average of
2000 consecutive voltage samples. We configured the
tool such that a complete power snapshot of the system
could be generated approximately every 400 ms.
The benchmarks were coordinated on the host ma-
chine, which communicated with the DuT via a serial
connection. It was responsible for executing benchmarks
on the DuT, synchronising the power measurement soft-
ware with the benchmark, and collecting other relevant
data.
2.4 Benchmarks
We ran two types of benchmarks. First, a series of
micro-benchmarks designed to independently charac-
terise components of the system, particularly their peak
and idle power consumption.
Second, we ran a series of macro-benchmarks based
on real usage scenarios. For low-interactivity applica-
tions (e.g. music playback), we simply launched them
from the command line. For interactive applications,
such as web browsing, we took a trace-based approach.
A trace consisted of a sequence of input events, in-
cluding a time-stamp, the name of the device provid-
ing the input (the touchscreen or one of two push-
buttons), and for touchscreen events, the coordinates
of the touch. The Linux kernel provides this informa-
tion by reading from the /dev/input/event*de-
vice files. To collect the trace, we used the target ap-
plication normally, while in the background storing the
input events to file. We then replayed the events under
benchmarking conditions by writing the collected data
to the /dev/input/event*files at the correct time.
Although this approach does bypass the hardware and
interrupt paths that would usually be followed for a
touchscreen event, our measurements showed the addi-
tional power to be negligible. The vast majority of en-
ergy required to handle a touchscreen event is consumed
in delivering it from the kernel to software.
0
5
10
15
20
25
30
35
GSM
CPU
RAM
WiFi
Graphics
Audio
Rest
Power (mW)
Figure 2: Power breakdown in the suspended state. The
aggregate power consumed is 68.6 mW.
3 Results
3.1 Baseline cases
Prior to running any benchmarks, we established the
baseline power state of the device, when no applications
are running. There are two different cases to consider:
suspended and idle. For the idle case, there is also the
application-independent power consumption of the back-
light to consider.
3.1.1 Suspended device
A mobile phone will typically spend a large amount of
time in a state where it is not actively used. This means
that the application processor is idle, while the commu-
nications processor performs a low level of activity, as
it must remain connected to the network be able to re-
ceive calls, SMS messages, etc. As this state tends to
dominate the time during which the phone is switched
on, the power consumed in this state is critical to battery
lifetime.
The Android OS running on the application proces-
sor aggressively suspends to RAM during idle periods,
whereby all necessary state is written to RAM and the
devices are put into low-power sleep modes (where ap-
propriate). To quantify power use while suspended, we
forced the device into Android’s suspended state and
measured the power over a 120 second period. Figure 2
shows the results, averaged over 10 iterations. The av-
erage aggregate power is 68.6 mW, with a relative stan-
dard deviation (RSD) of 8.2 %. The large fluctuations
are largely due to the GSM (14.4 % RSD) and graphics
(13.0 %) subsystems.
The GSM subsystem power clearly dominates while
suspended, consuming approximately 45 % of the overall
power. Despite maintaining full state, RAM consumes
negligible power—less than 3mW. Note that the GSM
0
10
20
30
40
50
60
70
80
90
GSM
CPU
RAM
WiFi
Graphics
LCD
Audio
Rest
Power (mW)
Figure 3: Average power consumption while in the idle
state with backlight off. Aggregate power is 268.8 mW.
subsystem in our device does not use system memory—it
has its own bank of RAM which we include in the GSM
power measurements.
3.1.2 Idle device
The device is in the idle state if it is fully awake (not sus-
pended) but no applications are active. This case consti-
tutes the static contribution to power of an active system.
We run this case with the backlight turned off, but the
rest of the display subsystem enabled.
Figure 3 shows the power consumed in the idle state.
As with the suspend benchmark, we ran 10 iterations,
each of 120 seconds in the idle state. Power consumed
in this state was very stable, with an RSD of 2.6 %, in-
fluenced largely by GSM, which varied with an RSD of
30 %. All other components showed an RSD below 1 %.
Figure 3 shows that the display-related subsystems
consume the largest proportion of power in the idle
state—approximately 50 % due to the graphics chip and
LCD alone, and up to 80 % with backlight at peak bright-
ness. GSM is also a large consumer, at 22 % of aggregate
power.
3.1.3 Display
Figure 4 shows the power consumed by the display
backlight over the range of available brightness levels.
That level is an integer value between 1 and 255, pro-
grammed into the power-management module, used to
control backlight current. Android’s brightness-control
user-interface provides linear control of this value be-
tween 30 and 255.
The minimum backlight power is approximately
7.8 mW, the maximum 414 mW, and a centred slider cor-
responds to a brightness level of 143, consuming 75 mW.
The backlight consumes negligible power when disabled
(as in the above idle benchmarks).
0
50
100
150
200
250
300
350
400
450
0 50 100 150 200 250
Power (mW)
Brightness level
Figure 4: Display backlight power for varying brightness
levels.
We also measured how the content displayed on the
LCD affected its power consumption: 33.1 mW for a
completely white screen, and 74.2 mW for a a black
screen. Display content can therefore affect overall
power consumption by up to 43 mW.
3.2 Micro-benchmarks
As mentioned in Section 2.4, we used micro-benchmarks
to determine the contribution to overall power from var-
ious system components. Specifically we used bench-
marks to exercise the application processor (CPU and
memory), the flash storage devices, and the network in-
terfaces.
3.2.1 CPU and RAM
To measure CPU and RAM power, we ran a subset of the
SPEC CPU2000 suite. There are several reasons for not
running all benchmarks of the suite. Firstly, we could
only use benchmarks which we could build and run on
the Android OS, which rules out those written in C++
or Fortran, due to Android’s lack of run-time support for
these languages. They also needed to fit into the phone’s
limited memory and their execution times needed to be
short enough to give reasonable turn-around. Finally,
we were only interested in establishing the power con-
sumption of CPU and memory, rather than making com-
parisons between different platforms’ algorithms, hence
completeness of the suite was not a relevant considera-
tion.
From the candidates remaining according to the above
criteria, we selected a set representing a good spectrum
of CPU and memory utilisation, from highly CPU-bound
to highly memory-bound. We determined memory-
boundedness by running the entire suite on a server
Linux system and comparing the slowdown due to fre-
quency scaling. Snowdon et al. [9] show that this slow-
down is primarily due to memory-boundedness. While
0
20
40
60
80
100
120
140
160
180
200
equake
vpr
gzip
crafty
mcf
idle
Power (mW)
CPU(100MHz)
RAM(100MHz)
CPU(400MHz)
RAM(400MHz)
Figure 5: CPU and RAM power when running SPEC
CPU2000 micro-benchmarks, sorted by CPU power.
we do not expect the benchmarks to behave similarly on
the different platforms, our aim is only to select bench-
marks with different characteristics.
The SPEC CPU2000 benchmarks ultimately selected
are equake,vpr,gzip,crafty and mcf.
For each of the benchmarks, we measured the aver-
age CPU and RAM power at fixed core frequencies of
100 MHz and 400 MHz. We also measured power for
the system in the idle state. Figure 5 shows these results,
averaged over 10 runs. The RSD is less than 3 % in all
cases.
For the idle, equake,vpr and gzip workloads,
CPU power dominates RAM power considerably at both
frequencies. However, crafty and mcf show that
RAM power can exceed CPU power, albeit by a small
margin.
Table 3 shows the effect of frequency scaling on the
performance, as well as combined CPU and RAM power
and energy of the benchmarks. The wide range of slow-
down factors across the different benchmarks validates
our selection of workloads as representing a range of
CPU/memory utilisations.
Benchmark Performance Power Energy
equake 26 % 36 % 135 %
vpr 31 % 40 % 125 %
gzip 38 % 43 % 112 %
crafty 63 % 62 % 100 %
mcf 74 % 69 % 93 %
idle - 71 % -
Table 3: SPEC CPU2000 performance, power and en-
ergy of 100 MHz relative to 400 MHz. Both CPU and
RAM power/energy are included.
0
20
40
60
80
100
NAND CPU RAM SD CPU RAM
Power (mW)
Reads
Writes
SD CardInternal NAND Flash
Figure 6: SD, NAND, CPU and RAM power for flash
storage read and write benchmarks.
3.2.2 Flash storage
Bulk storage on the Freerunner device is provided by
256 MiB of internal NAND flash, and an external micro
Secure Digital (SD) card slot. To measure their max-
imum power consumption, we used the Linux dd pro-
gram to perform streaming reads and writes. For reads
we copied a 64 MiB file, filled with random data, to
/dev/null in 4 KiB blocks. For writes, 8 MiB of ran-
dom data was written, with an fsync between succes-
sive 4 KiB blocks to ensure predictability of writes. Be-
tween each iteration we forced a flush of the page cache.
Figure 6 shows the power consumed by the NAND
flash and SD card, as well as the CPU and RAM, aver-
aged over 10 iterations of each workload. Table 4 shows
the corresponding data throughput, efficiency (including
NAND/SD power and the CPU and RAM power to sup-
port it), and idle power consumption. The power and
throughput RSD is less than 5 % in all cases.
The graphics module, which contains the physical
SD card interface, showed a power increase of 2.2 mW
(2.6 % above static) for writes, and a 21.1 mW increase
(26 %) for reads.
Metric NAND SD
Idle (mW) 0.4 1.4
Read
throughput (MiB/s) 4.85 2.36
efficiency (MiB/J) 65.0 31.0
Write
throughput (KiB/s) 927.1 298.1
efficiency (MiB/J) 10.0 5.2
Table 4: Flash storage power and performance.
0
100
200
300
400
500
600
700
800
WiFi GSM CPU RAM
Power (mW)
WiFi
GPRS
Figure 7: Power consumption of WiFi and GSM
modems, CPU, and RAM for the network micro-
benchmark.
3.2.3 Network
In this benchmark we stressed the two main networking
components of the device: WiFi and GPRS (provided by
the GSM subsystem). The test consisted of downloading
a file via HTTP using wget. The files contained random
data, and were 15 MiB for WiFi, and 50 KiB for GPRS.
The results of 10 iterations of the benchmark are shown
in Figure 7.
WiFi showed a throughput of 660.1±36.8KiB/s, and
GPRS 3.8±1.0KiB/s. However, they both show compa-
rable power consumption far exceeding the contribution
of the RAM and CPU. The increased CPU and RAM
power for WiFi reflects the cost of processing data with
a higher throughput. Despite highly-variable throughput,
GSM showed a relatively consistent power consumption
with an RSD of approximately 2 %.
To test the effect of signal strength on power and
throughput, we re-ran the network benchmarks with the
device shielded within a metal box of 2 mm thickness.
Over GPRS, this resulted in an increase of GSM power of
30 %, but no effect on throughput. The shielding resulted
in a reported signal strength drop of 10 dBm. Over WiFi,
the signal strength dropped by only 2 dBm, and no effect
on throughput or power consumption was observed.
3.2.4 GPS
To measure power consumption of the GPS subsystem,
we enabled the module and ran the GPS Status 2 An-
droid application. Table 5 shows the power consumed
by the GPS module in three situations; using only the in-
ternal antenna, with an external active antenna attached,
and when idle (i.e. powered down).
We noticed that the energy consumption of the mod-
ule is largely independent of the received signal—neither
the number of satellites, nor the signal strength, had any
appreciable effect.
This observation is contrary to the part’s data sheet
State Power (mW)
Enabled (internal antenna) 143.1±0.05 %
Enabled (external antenna) 166.1±0.04 %
Disabled 0.0
Table 5: GPS energy consumption.
[10], which specifies that power consumption should
drop by approximately 30 % after satellite acquisition. It
is unclear why we did not see such behaviour; perhaps
due to the GPS module itself, or more likely an error in
hardware integration or software. In addition, the power-
management features of the device are not exploited by
software. Thus, these figures should only be considered
worst-case.
3.3 Usage scenarios
Here we show the results of using macro-benchmarks to
determine power consumption under a number of typi-
cal usage scenarios of a smartphone. Specifically we ex-
amined audio and video playback, text messaging, voice
calls, emailing and web browsing.
3.3.1 Audio playback
This benchmark is designed to measure power in a sys-
tem being used as a portable media player. The sample
music is a 12.3 MiB, 537-second stereo 44.1 kHz MP3,
with the output to a pair of stereo headphones. The
measurements are taken with the backlight off (which is
representative of the typical case of someone listening
to music or podcasts while carrying the phone in their
pocket). However, GSM power was included, as the re-
alistic usage scenario includes the phone being ready to
receive calls or text messages.
Figure 8 shows the power breakdown for this bench-
mark at maximum volume, averaged over 10 iterations.
The audio file is stored on the SD card. Between suc-
cessive iterations we forced a flush of the buffer cache to
ensure that the audio file was re-read each time.
The results show the audio subsystem (amplifier and
codec) consuming 33.1 mW with an RSD of less than
0.2 %. Approximately 58 % of this power is consumed
by the codec, with the remaining 42 % used by the am-
plifier. Compared with the idle state, this corresponds to
a negligible change in codec power, with amplifier power
increasing by 80 %. Overall, the audio subsystem ac-
counts for less than 12 % of power consumed.
In addition to maximum volume, we also measured the
system at 13 % volume. This showed little change—the
audio subsystem power decreased by 4.3 mW (approx-
imately 14 %), mostly in the amplifier. However, for
0
10
20
30
40
50
60
70
80
90
100
GSM
CPU
RAM
Graphics
LCD
Audio
Rest
Power (mW)
Figure 8: Audio playback power breakdown. Aggregate
power consumed is 320.0 mW.
unknown reasons, the power consumed by the graphics
chip increased by 4.6 mW. As a result, the additional
power consumed in the high-volume benchmark is less
than 1 mW compared with the low-volume case.
Again, maintaining a connection to the GSM net-
work requires a significant and highly variable amount of
power, specifically 55.6±19.7mW in this case. While
the MP3 file is loaded from the SD card, the cost of doing
so is negligible at <2% of total power.
3.3.2 Video playback
In this benchmark we measured the power requirements
for playing a video file. We used a 5minute, 12.3 MiB
H.263-encoded video clip (no sound), and played it with
Android’s camera application. Again we forced a flush of
the buffer cache between iterations. The power averaged
over 10 iterations is shown in Figure 9.
Since the purpose of the macro-benchmarks is to anal-
yse the full system, we have included backlight power
in the results. However, rather than arbitrarily choosing
a single brightness, we have plotted the results at 0 %,
33 %, 66 %, and 100 %, corresponding to the position of
Android’s brightness-control slider. These correspond to
brightness levels of 30, 105, 180 and 255 respectively.
GSM power is again included.
While the CPU is the biggest single consumer of
power (other than backlight), the display subsystems still
account for at least 38 % of aggregate power, up to 68 %
with maximum backlight brightness. The energy cost of
loading the video from the SD card is negligible, with an
average power of 2.6mW over the length of the bench-
mark.
3.3.3 Text messaging
We benchmarked the cost of sending an SMS by using
a trace of real phone usage. This consists of loading
0
50
100
150
200
250
300
350
400
Backlight
GSM
CPU
RAM
Graphics
LCD
Rest
Power (mW)
0%
33%
67%
100%
Figure 9: Video playback power breakdown. Aggregate
power excluding backlight is 453.5 mW.
0
50
100
150
200
250
300
350
400
Backlight
GSM
CPU
RAM
Graphics
LCD
Audio
Rest
Power (mW)
0%
33%
67%
100%
Figure 10: Power breakdown for sending an SMS. Ag-
gregate power consumed is 302.2 mW, excluding back-
light.
the contacts application and selecting a contact, typing
and sending a 55-character message, then returning to
the home screen; lasting a total of 62 seconds. To en-
sure the full cost of the GSM transaction is included, we
measured power for an additional 20 seconds. The aver-
age result of 10 iterations of this benchmark are shown
in Figure 10. Again, the power for four backlight bright-
ness levels is shown.
Power consumed is again dominated by the display
components. The GSM radio shows an average power of
66.3±20.9mW, only 7.9 mW greater than idle over the
full length of the benchmark, and accounting for 22 %
of the aggregate power (excluding backlight). All other
components showed an RSD of below 3 %.
3.3.4 Phone call
Figure 11 shows the power consumption when making
a GSM phone call. The benchmark is trace-based, and
includes loading the dialer application, dialing a number,
and making a 57-second call. The dialled device was
configured to automatically accept the call after 10 sec-
0
100
200
300
400
500
600
700
800
Backlight
GSM
CPU
RAM
Graphics
LCD
Rest
Power (mW)
0%
33%
67%
100%
Figure 11: GSM phone call average power. Excluding
backlight, the aggregate power is 1054.3 mW.
0
50
100
150
200
250
300
350
400
450
Backlight
GSM
CPU
WiFi
Graphics
LCD
Rest
GSM
CPU
WiFi
Graphics
LCD
Rest
Power (mW)
0%
33%
67%
100%
GPRSWiFi
Figure 12: Power consumption for the email macro-
benchmark. Aggregate power consumption (excluding
backlight) is 610.0 mW over GPRS, and 432.4 mW for
WiFi.
onds. Thus, the time spent in the call was approximately
40 seconds, assuming a 7-second connection time. The
total benchmark runs for 77 seconds.
GSM power clearly dominates in this benchmark at
832.4±99.0mW. Backlight is also significant, however
note that its average power is lower than in other bench-
marks, since Android disables the backlight during the
call. The backlight is active for approximately 45 % of
the total benchmark.
3.3.5 Emailing
For this benchmark, we used Android’s email applica-
tion to measure the cost of sending and receiving emails.
The workload consisted of opening the email applica-
tion, downloading and reading 5 emails (one of which
included a 60 KiB image) and replying to 2 of them. The
results of the benchmark are shown in Figure 12, aver-
aged over 10 iterations.
The power breakdown between the GPRS and WiFi
0
50
100
150
200
250
300
350
400
450
Backlight
GSM
CPU
WiFi
Graphics
LCD
Rest
GSM
CPU
WiFi
Graphics
LCD
Rest
Power (mW)
0%
33%
67%
100%
GPRSWiFi
Figure 13: Web browsing average power over WiFi and
GPRS. Aggregate power consumption is 352.8mW for
WiFi, and 429.0 mW for GPRS, excluding backlight.
benchmarks is comparable, except for the GSM and WiFi
radios. Despite presenting identical workloads to the ra-
dios, GSM consumes more than three times the power of
WiFi.
3.3.6 Web browsing
Our last benchmark measured the power consumption for
a web-browsing workload using both GPRS and WiFi
connections. The benchmark was trace-based, ran for
a total of 490 seconds, and consisted of loading the
browser application, selecting a bookmarked web site
and browsing several pages. We used the BBC News
website, which we mirrored locally to improve the reli-
ability of the benchmark. After each run, the browser
cache was cleared. The results averaged over 10 itera-
tions are shown in Figure 13, including backlight power
at 4 brightness levels.
GPRS consumes more power than WiFi by a factor of
2.5. The other components do not display any significant
difference between the two benchmarks.
This benchmark, along with the emailing benchmark,
are the only two where a more modern phone can be ex-
pected to show significantly different results. The much
higher bandwidth supported by 3G protocols is likely to
result in them being more power-hungry.
4 Validation
In this section, we measure the power consumption of
two additional smartphones; the HTC Dream (G1), and
the Google Nexus One (N1). Table 6 lists the key fea-
tures of these devices.
We measure the full-system power of these platforms
at the battery; per-component measurements are not pos-
sible because the necessary documentation (schematics,
0
100
200
300
400
500
600
0 50 100 150 200 250
Power (mW)
Brightness level
Display
Keyboard
Buttons
Figure 14: Display, button and keyboard backlight power
on the G1.
etc.) are not available to us. Moreover, there is no reason
to expect these production devices would be capable of
the type of instrumentation we have performed on the
Freerunner, since the additional components and PCB
area would increase the per-unit cost.
4.1 Display and backlight
Figure 14 plots the power consumption of the various
backlights on the G1 as a function of brightness level. In
addition to the LCD display backlight, the G1 features
a backlit physical keyboard and buttons which are not
present on either the Freerunner or the N1. These back-
lights do not have any brightness control, and contribute
189 mW when both enabled. The content of the LCD
display can affect power consumption by up to 17 mW.
The Nexus One features an OLED display, and as such
does not require a separate backlight like the Freerunner
and G1. Furthermore, the effects of display content and
brightness on power consumption are more tightly cou-
pled. For instance, the OLED power consumption for
a black screen is fixed, regardless of the brightness set-
ting. For a completely white screen at minimum bright-
ness, an additional 194 mW is consumed, and at maxi-
mum brightness, 1313 mW.
4.2 CPU
Figure 15 plots the G1 and N1 total system power un-
der our SPEC CPU2000 workloads at the minimum and
maximum frequencies supported by the respective de-
vice: 246 MHz and 384 MHz on the G1, and 245 MHz
and 998 MHz on the N1. Table 7 shows the percentage
slowdown, and reduction in full system power, due to
frequency scaling. This benchmark was run with the dis-
play system powered down and all radios disabled.
G1 N1
SoC Qualcomm MSM7201 Qualcomm QSD 8250
CPU ARM 11 @ 528 MHz ARMv7 @ 1 GHz
RAM 192 MiB 512 MiB
Display 3.2” TFT, 320x480 3.7” OLED, 480x800
Radio UMTS+HSPA UMTS+HSPA
OS Android 1.6 Android 2.1
Kernel Linux 2.6.29 Linux 2.6.29
Table 6: G1 and Nexus One specifications.
0
100
200
300
400
500
600
700
800
900
equake
vpr
gzip
crafty
mcf
idle
equake
vpr
gzip
crafty
mcf
idle
Power (mW)
fmin
fmax
G1N1
Figure 15: N1 and G1 system power for SPEC CPU2000
benchmarks.
Performance (%) Power (%)
Benchmark G1 N1 G1 N1
equake 67 25 87 26
vpr 68 25 87 26
gzip 71 25 86 27
crafty 76 25 89 28
mcf 84 54 91 41
Table 7: SPEC CPU2000 performance and average sys-
tem power of 246 MHz relative to 384 MHz on the G1,
and 245 MHz relative to 998 MHz on the N1.
4.3 Bluetooth
As noted earlier, we were unable to get Bluetooth work-
ing reliably on the Freerunner phone. To get an idea
of Bluetooth power consumption, we re-ran the audio
benchmark on the G1 with the audio output to a Blue-
tooth stereo headset. The power difference between this
and the baseline audio benchmark should yield the con-
sumption of the Bluetooth module, because (as shown in
our Freerunner benchmarks) the power consumed by the
audio subsystem is almost entirely static.
Power (mW)
Benchmark Total Bluetooth
Audio baseline 459.7 -
Bluetooth (near) 495.7 36.0
Bluetooth (far) 504.7 44.9
Table 8: G1 Bluetooth power under the audio bench-
mark.
Table 8 shows the total and estimated Bluetooth power
consumption for the audio benchmarks. In the ”near”
benchmark, the headset was placed approximately 30 cm
from the phone, and about 10 m in the ”far” benchmark.
4.4 Benchmarks
Table 9 shows total system power consumption for the
Freerunner, G1, and Nexus One for a selection of our
benchmarks. The power consumption of the backlight
(OLED for the N1) has been subtracted out, since it is
highly dependent on the user’s brightness setting. Ta-
ble 10 shows the additional power consumption of the
OLED display at minimum and maximum brightness
levels.
The lower power consumption of the G1 in the idle,
web and email benchmarks can be attributed to the ex-
cellent low-power state of its SoC and effective use of it
by software. This can be seen in the SPEC benchmarks,
where the idle system consumes less than 22 mW; the
idle CPU power must be lower still.
The power disparity for the phone call benchmark is
likely due to power consumed by the non-radio compo-
nents of the system. The G1 and Nexus One phones enter
a suspended state during the call, offloading all function-
ality to the UMTS module. In contrast, the Freerunner
remains in a fully-active state throughout. The power
consumption of the GSM subsystem alone (832.4 mW) is
comparable to the G1 and N1 system consumption. Due
to lack of freely-available documentation, it is not clear
whether the Freerunner’s GSM chipset lacks this feature,
or if it is not supported in software.
Average System Power (mW)
Benchmark Freerunner G1 N1
Suspend 103.2 26.6 24.9
Idle 333.7 161.2 333.9
Phone call 1135.4 822.4 746.8
Email (cell) 690.7 599.4 -
Email (WiFi) 505.6 349.2 -
Web (cell) 500.0 430.4 538.0
Web (WiFi) 430.4 270.6 412.2
Network (cell) 929.7 1016.4 825.9
Network (WiFi) 1053.7 1355.8 884.1
Video 558.8 568.3 526.3
Audio 419.0 459.7 322.4
Table 9: Freerunner, G1 and N1 system power (ex-
cluding backlight) for a number of micro- and macro-
benchmarks.
5 Analysis
5.1 Where does the energy go?
Our results show that the majority of power consumption
can be attributed to the GSM module and the display,
including the LCD panel and touchscreen, the graphics
accelerator/driver, and the backlight.
In all except the GSM-intensive benchmarks, the
brightness of the backlight is the most critical factor in
determining power consumption. However, this is a rela-
tively simple device from a power-management perspec-
tive, and largely depends on the user’s brightness prefer-
ence. Our results confirm that aggressive backlight dim-
ming can save a great deal of energy, and further moti-
vates the inclusion of ambient light and proximity sen-
sors in mobile devices to assist with selecting an appro-
priate brightness. Moreover, the N1 OLED results show
that merely selecting a light-on-dark colour scheme can
significantly reduce energy consumption.
The GSM module consumes a great deal of both static
and dynamic power. Merely maintaining a connection
with the network consumes a significant fraction of total
power. During a phone call, GSM consumes in excess
of 800 mW average, which represents the single largest
power drain in any of our benchmarks. Unfortunately,
a phone-call-heavy workload presents little scope for
software-level power management. Dimming the back-
light during a call, as Android does, is clearly good pol-
icy, saving up to 40 % power even with the large GSM
consumption.
Overall, the static contribution to system power con-
sumption is substantial. In all of our usage scenarios, ex-
cept GSM phone call, static power accounts for at least
50 % of the total. If the backlight is included, this fig-
OLED Power (mW)
Benchmark Min. Max.
Idle 38.0 257.3
Phone call 16.7 112.9
Web 164.2 1111.7
Video 15.1 102.0
Table 10: Additional power consumed by the N1 OLED
display at maximum and minimum brightness.
ure rises substantially. This leads us to the conclusion
that the most effective power management approach on
mobile devices is to shut down unused components and
disable their power supplies (where possible).
The RAM, audio and flash subsystems consistently
showed the lowest power consumption. While our
micro-benchmarks showed that the peak power of the
SD card could be substantial (50 mW), in practice the
utilisation is low enough such that on average, negligi-
ble power is consumed. Even video playback, one of the
more data-intensive uses of mobile devices, showed SD
power well under 1 % of total power. RAM has simi-
lar characteristics; micro-benchmarks showed that RAM
power can exceed CPU power in certain workloads, but
in practical situations, CPU power overshadows RAM by
a factor of two or more. Audio displayed a largely static
power consumption in the range of 28–34 mW. Overall,
RAM, audio and SD have little effect on the power con-
sumption of the device, and therefore offer little potential
for energy optimisation.
5.2 Dynamic voltage and frequency scaling
Our CPU micro-benchmarks show that dynamic volt-
age and frequency scaling (DVFS) can significantly re-
duce the power consumption of the CPU. However,
this does not imply reduced energy overall, because the
run-time of the workload also increases. Our results
show (Table 3) that only highly memory-bound work-
loads (namely mcf) exhibit a net reduction in CPU/RAM
energy.
However, such a simplistic analysis assumes that af-
ter completing the task, the device consumes zero power.
Clearly this is not a realistic model, particularly for a
smartphone. To correct for this, we can “pad” each of the
measurements with idle power [5] in order to equalise the
run times, according to the following equation:
E=P t +Pidle (tmax t)
where
Eis the equivalent energy consumed for the
benchmark;
% Energy
Benchmark Freerunner G1 N1
equake 95.5 126.0 75.6
vpr 95.8 124.5 75.9
gzip 95.8 120.1 77.7
crafty 95.5 115.6 77.3
mcf 94.9 105.3 65.9
Table 11: SPEC CPU2000 percentage total system en-
ergy consumption of the minimum frequency compared
with the maximum frequency, padded with idle power.
Pis the average power over the run-time of
the benchmark;
tis the run-time of the benchmark;
Pidle is the idle power;
tmax is the maximum run-time of the bench-
mark over all frequencies.
Table 11 shows the energy consumed for each of the
SPEC benchmarks at the lowest frequency, compared to
the highest frequency, padded with idle power.
The results show that the practical benefits of DVFS
depend largely on the CPU hardware (particularly idle
power), and to some extent, the workload.
On the G1, which has a good low-power idle mode, re-
ducing frequency always results in increased energy us-
age. It appears that DVFS on this platform is completely
ineffective.
On the Freerunner, DVFS only yields a marginal en-
ergy reduction of approximately 5 %—a saving of at
most 20 mW. However, the N1 shows considerable ad-
vantages to using DVFS, saving up to 35 %, correspond-
ing to an average power reduction of 138mW. Whether
or not to use DVFS on these two platforms is a policy
decision, since reducing frequency can affect user expe-
rience.
Much of the energy reduction on the Freerunner can be
attributed to the high idle power. For a system going into
suspend (rather than idle) after completing the workload,
DVFS no longer offers an advantage. However, on the
N1 this is not the case: DVFS is still effective, even if
transitioning into a very-low power state. This is due to
the processor’s high efficiency at low frequencies, which
can be seen in Figure 15.
In the case of an idle system, reducing frequency can
result in an energy saving, and at worst has no effect. Our
results show that DVFS reduces idle CPU/RAM con-
sumption by about 30 % on the Freerunner. However, in
absolute terms, this is less than a 20 mW saving: 6.5 % of
an idle system. On the N1, this saving is approximately
36 mW. On the G1, frequency scaling during idle periods
is ineffective due to the processor’s low-power idle state,
which is used aggressively.
5.3 Energy model
We can express the results of Section 3 in a scenario-
based energy model of the Freerunner device, which
shows the energy for each usage scenario as a function
of time:
Eaudio(t)=0.32W×t
Evideo(t) = (0.45W+PBL )×t
Esms(t) = (0.3W+PBL )×t
Ecall(t)=1.05W×t
Eweb(t) = (0.43W+PBL )×t
Eemail(t) = (0.61W+PBL )×t
The equations give the energy consumed in Joules when
the time is supplied in seconds. PBL is the backlight
power (in watts), scenarios without a PBL term are as-
sumed to run with backlight off.
5.4 Modelling usage patterns
To investigate day-to-day power consumption of the de-
vice, we define a number of usage patterns. Suspend rep-
resents the baseline case of a device which is on standby,
without placing or receiving calls or messages. The ca-
sual pattern represents a user who uses the phone for a
small number of voice calls and text messages each day.
Regular represents a commuter with extended time of lis-
tening to music or podcasts, combined with more lengthy
or frequent phone calls, messaging and a bit of email-
ing. The business pattern features extended talking and
email use together with some web browsing. Finally, the
PMD (portable media device) case represents extensive
media playback. The parameters of these patterns are
summarised in Table 12. In each case, GPRS is used for
data networking.
The Freerunner uses a battery of 1.2 Ah capacity,
which is approximately 16 kJ. Table 13 shows the power
use, and resulting battery life corresponding to the above
use patterns. We assume that in all cases requiring back-
light, illumination level is set at approx 66 %, corre-
sponding to 140 mW. In all other cases, backlight is as-
sumed off.
The table shows that total battery life varies by almost
a factor of 2.5 between use cases. It shows that GSM
is the dominating energy drain, followed by CPU and
graphics.
Workload SMS Video Audio Phone call Web browsing Email
Suspend - - - - - -
Casual 15 - - 15 - -
Regular 30 - 60 30 15 15
Business 30 - - 60 30 60
PMD - 60 180 - - -
Table 12: Usage patterns, showing total time for each activity in minutes.
Power (% of total) Battery life
Workload GSM CPU RAM Graphics LCD Backlight Rest [hours]
Suspend 45 19 4 13 1 0 19 49
Casual 47 16 4 12 2 3 16 40
Regular 44 14 4 14 4 7 13 27
Business 51 11 3 11 4 11 10 21
PMD 31 19 5 17 6 6 14 29
Table 13: Daily energy use and battery life under a number of usage patterns.
5.5 Limitations
Our work has a number of limitations which need to be
kept in mind when using our results.
The biggest one is that the Freerunner is not a latest-
generation mobile phone, but is a few years old. The
main feature it is lacking is a 3G cellular interface, which
supports much higher data rates than the 2.5G GPRS in-
terface. Our validation results show that this higher data
rate does not appreciably affect power consumption in
practical situations.
Further, the application processor is based on a rela-
tively dated ARMv4 architecture, however it is clocked
at a rate consistent with 2009-vintage smartphones. The
difference in power consumption compared with more
modern processors can traced largely to idle power; in
other respects, the age of the CPU is not a substantial
limitation.
6 Related Work
Mahesri and Vardhan [4] perform an analysis of power
consumption on a laptop system. Their approach to
component power measurement is driven partially by di-
rect power measurement, but largely by deduction using
modelling and off-line piece-wise analysis. They show
that the CPU and display are the main consumers of en-
ergy for their class of system, and that other components
contribute substantially only when they are used inten-
sively. Their results mirror our observations that RAM
power is insignificant in real workloads.
Bircher and John [2] look at component power esti-
mation using modelling techniques. They demonstrate
an error of less than 9 % on average across all tested sub-
systems, including memory, chipset, disk, CPU, and I/O.
In a later work, Bircher and John [3] measure the
power consumption of the CPU, memory controller,
RAM, I/O, video and disk subsystems under a number
of workloads. Their results show that CPU and disk con-
sume the majority of the power, with the RAM and video
systems consuming very little. However, under the SPEC
CPU suites, they show that RAM power can indeed ex-
ceed CPU power for highly memory-bound workloads.
Sagahyroon [8] perform an analysis similar to ours on
a handheld PC. They show significant consumption in
the display subsystems, particularly in backlight bright-
ness. Unlike our results, theirs suggest that the CPU,
and its operating frequency, is important to overall power
consumption. They also show significant dynamic power
consumption in the graphics subsystems.
7 Conclusions and Future Work
We performed a detailed analysis of energy consumption
of a smartphone, based on measurements of a physical
device. We showed how the different components of the
device contribute to overall power consumption. We de-
veloped a model of the energy consumption for differ-
ent usage scenarios, and showed how these translate into
overall energy consumption and battery life under a num-
ber of usage patterns.
The open nature of the Openmoko Neo Freerunner
smartphone is what allowed us to perform such a detailed
analysis and breakdown of its power consumption. This
is not possible to the same degree on a typical commer-
cial device.
We have compared the detailed measurements with
a coarse-grained analysis of more modern phones, and
shown the results to be comparable.
The ultimate aim of this work is to enable a systematic
approach to improving power management of mobile de-
vices. We hope that by presenting this data, we will en-
able such future research, both in our lab as well as by
others.
Acknowledgments
NICTA is funded by the Australian Government as rep-
resented by the Department of Broadband, Communica-
tions and the Digital Economy and the Australian Re-
search Council through the ICT Centre of Excellence
program.
Thanks to Nicholas FitzRoy-Dale, who provided us
with the input event capture and replay tools, and Yanjin
Zhu, who allowed us to run measurements on her Nexus
One. We would also like to thank Bernard Blackham,
Etienne Le Sueur, Leonid Ryzhyk and our anonymous
reviewers for their feedback on earlier versions of the pa-
per.
Availability
Relevant software and data is available at http://
ertos.nicta.com.au/software/.
References
[1] ANDRIOD ON FREERUNNER COMMUNITY. 2009. http://
code.google.com/p/android-on- freerunner/.
[2] BIRCHER, W. L., AND JOHN, L. K. Complete system power es-
timation: A trickle-down approach based on performance events.
In Proceedings of the IEEE International Symposium on Perfor-
mance Analysis of Systems and Software (San Jose, CA, USA,
Apr. 25–27 2007), IEEE Computer Society, pp. 158–168.
[3] BIRCHER, W. L., A ND JOHN, L. K . Analysis of dynamic
power management on multi-core processors. In Proceedings of
the 22nd International Conference on Supercomputing (Island of
Kos, Greece, June 2008), pp. 327–338.
[4] MA HES RI , A. , AN D VARDHAN, V. Power consumption break-
down on a modern laptop. In Proceedings of the 2004 Workshop
on Power-Aware Computer Systems (Portland, OR, USA, Dec.
2004), B. Falsafi and T. N. Vijaykumar, Eds., vol. 3471 of Lec-
ture Notes in Computer Science, Springer, pp. 165–180.
[5] MI YOS HI , A., LEF UR GY, C., HENSBERGEN, E. V. , RA JA-
MONY, R ., A ND RA JK UM AR , R . Critical power slope: under-
standing the runtime effects of frequency scaling. In Proceedings
of the 16th International Conference on Supercomputing (New
York, NY, USA, June 2002), ACM Press, pp. 35–44.
[6] NATIO NAL INSTRUMENTS CO RP ORAT ION .NI 622x Specifica-
tions, June 2007. 371290G-01.
[7] OPENMOKO, IN C. GTA02 — Neo Freerunner schemat-
ics. http://downloads.openmoko.org/developer/
schematics/GTA02, Aug. 2008. A7RC0.
[8] SAGAHYROON, A. Power consumption in handheld computers.
In Proceedings of the International Symposium on Circuits and
Systems (Dec. 2006), pp. 1721–1724.
[9] SN OWD ON , D. C., LESU EUR , E. , PE TT ER S, S. M., A ND
HEISER, G. Koala: A platform for OS-level power management.
In Proceedings of the 4th EuroSys Conference (Nuremberg, Ger-
many, Apr. 2009).
[10] U-BL OX AG. ATR0630 Data Sheet, July 2006. GPS.G4-X-
06009-P2.
[11] WIKIPEDIA. Smartphone. http://en.wikipedia.org/
wiki/Smartphone. Last visited January 2010.
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... • Power Consumption: Power consumption is linear with the sample rate, while it is exponentially related to the number of sampling bits [26]. For low power consumption and low-cost equipment, on a large bandwidth and large scale, achieving high-resolution quantization on antennas is not practical; especially in terms of high-definition video transmission, millimeter wave communication is facing more severe technical challenges. ...
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The future wireless communication system faces the bottleneck of the shortage of traditional spectrum resources and the explosive growth of the demand for wireless services. Millimeter-wave communication with spectral resources has become an effective choice for the next generation of wireless broadband cellular communication. However, the transmission path loss is large and oxygen and water molecules absorb Characteristics such as seriousness have brought great challenges to millimeter wave communication, and it is necessary to seek a technical approach different from low-frequency wireless communication. In the analysis of millimeter wave transmission characteristics After the analysis, the research progress of millimeter wave communication technology and the RF front-end is comprehensively analyzed, and the technology of millimeter wave communication is thoroughly analyzed with technical challenges and proposed corresponding research directions.
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This paper proposes the use of microprocessor performance counters for online measurement of complete system power consumption. While past studies have demonstrated the use of performance counters for microprocessor power, to the best of our knowledge, we are the first to create power models for the entire system based on processor performance events. Our approach takes advantage of the "trickle-down" effect of performance events in a microprocessor. We show how well known performance-related events within a microprocessor such as cache misses and DMA transactions are highly correlated to power consumption outside of the microprocessor. Using measurement of an actual system running scientific and commercial workloads we develop and validate power models for five subsystems: memory, chipset, I/O, disk and microprocessor. These models are shown to have an average error of less than 9% per subsystem across the considered workloads. Through the use of these models and existing on-chip performance event counters, it is possible to estimate system power consumption without the need for additional power sensing hardware
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Managing the power consumption of computing platforms is a complicated problem thanks to a multitude of hard- ware configuration options and characteristics. Much of the academic research is based on unrealistic assumptions, and has, therefore, seen little practical uptake. We provid e an overview of the difficulties facing power management schemes when used in real systems. We present Koala, a platform which uses a pre- characterised model at run-time to predict the performance and energy consumption of a piece of software. An arbitrary policy can then be applied in order to dynamically trade per- formance and energy consumption. We have implemented this system in a recent Linux kernel, and evaluated it by run- ning a variety of benchmarks on a number of different plat- forms. Under some conditions, we observe energy savings of 30% for a 4% performance loss. Categories and Subject Descriptors D.4.8 ( Operating
Conference Paper
The purpose of this work was to obtain a component- wise breakdown of the power consumption a modern laptop. We measured the power usage of the key components in an IBM ThinkPad R40 laptop using an Agilent Oscilloscope and current probes. We obtained the power consumption for the CPU, optical drive, hard disk, display, graphics card, memory, and wireless card subsystems--either through direct measurement or subtractive measurement and calculation. Moreover, we measured the power consumption of each component for a variety of workloads. We found that total system power consumption varies a lot (8 W to 30 W) depending on the workload, and moreover that the distribution of power consumption among the components varies even more widely. We also found that though power saving techniques such as DVS can reduce CPU power considerably, the total system power is still dominated by CPU power in the case of CPU intensive workloads. The display is the other main source of power consumption in a laptop; it dominates when the CPU is idle. We also found that reducing the backlight brightness can reduce the system power significantly, more than any other display power saving techniques. Finally, we observed OS differences in the power consumption.
http://en.wikipedia.org/ wiki/Smartphone
  • Wikipedia Smartphone
WIKIPEDIA. Smartphone. http://en.wikipedia.org/ wiki/Smartphone. Last visited January 2010.
NI 622x Specifications
  • National Instruments Corporation
NATIONAL INSTRUMENTS CORPORATION. NI 622x Specifications, June 2007. 371290G-01.
Complete system power estimation: A trickle-down approach based on performance events
  • W L And
BIRCHER, W. L., AND JOHN, L. K. Complete system power estimation: A trickle-down approach based on performance events. In Proceedings of the IEEE International Symposium on Performance Analysis of Systems and Software (San Jose, CA, USA, Apr. 25-27 2007), IEEE Computer Society, pp. 158-168.