Battery Energy Consumption Footprint of Embedded Multimedia Systems

Jiucai Zhang, Song Ci, and Xueyi Wang Department of Computer and Electronics Engineering University of Nebraska-Lincoln NE 68182, USA Vczhang,xueyiwang}@huskers.un!.edu, [email protected]

Abstract-The rapid demand of mobile multimedia causes a grant challenge on energy and environment. However, little research has been done on the carbon footprint of embedded multimedia system. To fill this gap, this paper presents a comprehensive architecture to measure the carbon footprint of

of raw video data, it has to be effectively compressed. Otherwise, tremendous energy will be needed for video transmission. Therefore, many battery powered systems adopted computation intensive and energy consuming video

systems. The measurement results of H.264 under various

compression algorithms and international standards such as H.263, MPEG-4, and H.264 [3] for efficient video compres­

system operation modes provide an insightful guideline to

sion. Typically, these algorithms consume about

H.264 codec, which has been widely used in mobile multimedia

design and optimize system parameters to minimize the carbon footprint of various embedded multimedia systems. Keywords·carbon footprint; H.264; battery; multimedia;

I. INTRODUCTION

60% '" 80%

of the total energy of battery powered mobile systems [4]. Furthermore, battery is not an ideal energy source. The nonlinear battery effects may cause carbon footprint consumption of battery to vary with discharging current of battery.

The rapid development of semiconductor, computer, and,

Although battery powered embedded multimedia devices

communication technologies enables mobile devices such

with H.264 codec have been widely used, little research

as Smartphones, PDAs, laptops having computation and

has been done on the analysis of the carbon footprint of

communication capability to support multimedia applica­

H.264 codec with consideration of nonlinear battery effects.

tions such as multimedia streaming, VoIP, and mobile TV.

To bridge this gap, in this paper, we propose an architecture

Recently, demand for these multimedia devices has soared

to track carbon footprint in H.264 codec under various oper­

worldwide, which causes fast growing energy cost. For in­

ation modes. The architecture determines the computational

stance, according to a recent survey, worldwide smartphone

complexity profile of H.264 codec based on the number

sales have been increasing by 29 percent per year to 180

of basic computational operations required by a codec to

million units in 2009 [1]. Using these smartphones for a

perform the key subfunctions and the frequency of use of

year on average consumes 759.78 billion joules of energy. To

each of the required subfunctions. Then, carbon footprint

put this in carbon footprint perspective, these smartphones

model of battery can convert computational complexity to

emit

20.16Gt of CO2

a year. Therefore, energy consumption

carbon footprint of the H.264 codec. The carbon footprint

of these devices has significant energy and environmental

measurement provides a way to understand its absolute

impacts.

energy-efficiency of H.264 codec. The quantified energy

On the front of global warming issues and energy security,

efficiency can be used to reduce overall energy consumption

many researchers are engaged in identifying strategies to reduce their climate impacts. So does the field of battery

by knowing the theoretical bound of energy efficiency.

powered embedded multimedia systems. In such systems,

The measurement can also be used as an objective tool to compare the energy-efficiency performance of various

live videos are captured and compressed on battery-powered

system designs.

embedded devices, and then the compressed video is trans­

The paper is organized as follows. Section II analyzes the

mitted to remote users through wired/wireless channels.

carbon footprint of mobile multimedia system. Section III

To enable low carbon footprint, it is expected that video

proposes the architecture for carbon footprint measurement

communications over battery powered mobile multimedia

of H.264 codec. Section IV measures carbon footprint of

devices can respond quickly to the challenge of energy

H.264 codec under various operation configurations. The paper is concluded in Section V.

consumption. Understanding energy consumption in mobile multimedia devices is the key for efficient energy management of the next-generation mobile multimedia. In general, energy con­ sumption of mobile multimedia is mainly on computation, communication, and 110 devices [2]. Due to large volume 978·1·4244· 7614-5/10/$26.00 ©2010 IEEE

II. T HE CARBON FOOTPRINT OF MOBILE MULTIMEDIA SYSTEM

A general architecture of mobile multimedia system is shown in Figure 1. Raw video data are compressed by video

Camera

0.68

LCD

0.67

o (I) <

�. (J)

-:::::0 3 (I) o �. -(") (I) (I)

�(J)

:2

0.66



0'"

0.65

� C

0.64

0 .2

0.63

0

0.62

.� c

.0

Cii

u

0.61 0.6

Figure 1.

Architecture of mobile multimedia systems

0.2

0.3 0.4 0.5 0.6 0.7 0.8 0.9 Current (normorlized to 1 C 41.3mA) =

Figure 3.

Battery carbon footprint vs. discharge current rate.

codec such as H.264, H.263, and MPEG-4, and then the compressed data packets are passed through the network protocol stack and are transmitted over a communication

codec. The number of function calls is measured by using

channel. To estimate the overall carbon footprint of a mobile

program profiling technology under different configuration

multimedia device, it is important to understand that there

of the codec. By mapping these computational requirements

are at least three major factors in carbon footprint: I) com­

to a given processing capability of hardware platform, an

putational complexity of video codec including algorithm complexity, bit rate, and distortion (or perception quality)

estimate of the codec's carbon footprint can be determined for the platform. The methodology of energy profiling is

[5], 2) communications and 3) nonlinear battery effects. The

shown in Figure 2. Program profiling involves running a

computational complexity can be measured by the approx­

version of a H.264 codec program in which instrumentation

imate number of operations required to execute the codec

code has been incorporated to determine how much time

under a specific configuration. The optimal configuration

different components of the H.264 codec program will take.

of video codec may have significantly lower computational complexity than a less optimal one for a given hardware

First, it determines how much CPU time was spent for each

The program profiling generates two forms of information.

platform, despite the fact that the two configurations may

of the function in the H.264 codec. Second, it keeps tracking

produce identical perceptual quality (or video distortion).

of how many times each function gets called, categorized by

A higher compression ratio will result in a lower bit rate,

which function performs the call. Both forms of information

but resulting in higher computational complexity and bigger

can be useful. The timing information gives a sense of the

carbon footprint, and vice versa. Furthermore, battery is not

relative importance of the different functions in determining

an ideal energy source, and the energy efficiency of battery

the overall run time. The calling information allows us to understand the dynamic behavior of the H.264 codec.

varies with battery discharge current rates. Battery tends to lead to bigger carbon footprint at a high discharge current rate. Therefore, achieving a smaller carbon footprint needs a systematic consideration of both video source adaptation and network transmission adaptation under a given battery characteristics. A. Carbon Footprint Profiles of H.264 Codec

B. Nonlinear Battery Carbon Footprint

Mobile multimedia devices are usually powered by Nickel-cadmium and Lithium-ion battery. Due to nonlinear battery effects, such as current effect, recovery effect, aging effects, and self-discharging, no battery chemistry can obtain

100%

columbic efficiency [6]. Generally, the efficiency will

To derive an accurate estimate of the carbon footprint

drop as the current is increased because the rate dependent

of H.264 codec , we should first discuss the complex­

component of electrode over-potentials and the formation of

ity of H.264 codec. H.264 codec contains a number of

concentration gradients will consume energy [7]. In order to

optional coding features that may have significant effects

effectively design and utilize the battery for dynamic power

on carbon footprint of codec. In this paper, we take a

management, deep understanding of how carbon footprint

systematic approach to quantifying the carbon footprint

varies with current is highly desirable. In this paper, we have

of a H.264/AV C baseline codec. The objective here is

set up a testbed to study this relationship. The experiment

to determine the number of basic operations required by

results of a Bellcore PLION cell [7] by using Arbin Battery Testing Instrument BT2000 are presented in Figure 3. In this

the codec to perform each of the key functions of the

Camera

Encoder

Simple Scalar

Program profiling

Others Program profile Flat profile: Each

sample counts as

,

cumulative

t.ime

74.87 22.59 1.06 0.65

seconds

326.71 425.29 429.90 432.72

0.01

seconds.

self

self

seconds

calls

326.71 3303048305 98.58 2415105 4.61 102372150 2.82 41055625

total

s/call

p.CO O. 00 0.00 O. 00

s/call

0.00 O. 00 0.00 0.00

name

computeSAO FullPelBlockHot.ionsearch HadamsrdSA04x4 computeSATD

Times that the function

Times that the function

get called

get called

Energy per

Integer-Pel

ME

instruction

Energy profiling of encoder

Figure 2.

Energy profiling architecture

figure, each point is obtained in the following method. First,

a low-resistance shunt and measured the voltage across the

we charge a fresh battery with constant current and constant

shunt using a Arbin Battery Testing Instrument BT2000 [9].

voltage to full, which means that the battery is charged to

At the same time, battery voltage can be directly measured

full with same consumed energy and carbon footprint. After

by one of the BT2000 channels. The measured current and

30

minutes, this battery is discharged

voltage can be used to calculate consumed power, energy,

from the full capacity to exhaustion at discharge current rate

and capacity of the H.264. In this experiment, a fresh battery

battery is rested for

X.C (Here, lC 25°C. The carbon

of

41.3mA).

All discharges took place at

is firstly charged to the full capacity via the constant current

footprint is defined as the ratio between

and constant voltage (CCCV ). Then, the battery is rested

total carbon footprint for charging and consumed power for

for minutes. Finally, the battery is used to power Imote 2.

=

discharging . The experiment results are shown in Figure 3.

The consumed energy in amperes hours can be translated

From this figure, we can conclude that the carbon footprint

into carbon footprint using the carbon footprint of battery

of a Bellcore PLION cell increases with increment of the

as shown in Figure 3. For an embedded multimedia system,

discharge currents due to nonlinear battery current effects.

we usually use H.264 codec to compress videos

This can be observed by the fact that the enhancement of

at

the carbon footprint for discharging a full charged battery

then partition the raw video data into

will change from about 0.59gjWh at the discharge current rate of O.IC to about 0.674gjWh at the discharge current of

lC( 41.3mA),

which provides a useful guideline for carbon­

footprint-aware green computing. III. T HE ARCHITECTURE FOR CARBON FOOTPRINT MEASUREMENT

To track carbon footprint of the H.264 codec, we have

30

(176 x 144

frames per second) captured from a camera. We

50

frames. Using

the aforementioned framework, we can obtain the carbon footprint of H.264 codec at various operation configurations. Then, we compute the number of cycles required to execute a particular subfunction on a chosen hardware platform. This intermediate result may be derived by mapping the subfunction operations on the hardware, applying single­ instruction multiple data parallelism and distributing the required operation as efficiently as possible among the exe­

implemented H.264 codec on an embedded platform Imote 2

cution subunits on the hardware. Furthermore, the number of

with Linux 2.6.29 Kernel and an Xscale PXA processor [8],

CPU cycles is derived by multiplying the intermediate result

which is powered by a 3.7V, 2600mAh lithium-ion battery

by the frequency with which the subfunction was called.

HE18650. The architecture of carbon footprint measurement

Based on the carbon footprint of the battery and complexity

is shown in Figure 4. Based on this setting, the carbon

profile of H.264 codec, carbon footprint of H.264 Codec is

footprint of the H.264 codec is measured in the following procedures. First, we redirected the device current through

derived.

Profile of power & energy





I ,,,)·-,oo,,,,,,, _,.oo"n".

... 0,00621731' .. 0 013 149/ + 0.62514

- ...." . ---- CiJTent °0

Figure 4.

05

1

15

2

rlme{seconds)

2.!5

3

3,5

The system architecture of carbon footprint measurement.

IV. CARBON FOOTPRINT MEASUREMENT OF EMBEDDED MULTIMEDIA SYSTEMS

0.6285 OJ

0.628

0N S2. C

0.6275

(5 .2

0.6265

.� c 0 .0

Co

U

0.627

Due to various configurations of H.264 codec, it is very

. , . . .. .. . � ... ' ... . . . . . ..

difficult to get the carbon footprint of H.264 under all possible modes. The main carbon footprint is consumed by

... -. . ... ...

the module of motion estimation. The two main parameters

.... , .

... ...

that affect computational complexity of motion estimation are maximum search range and number of inter reference frames [10]. Therefore, in this section, we will focus on the

0.626

carbon footprint measurement of H.264 under various search

0.6255 0.6

ranges and different numbers of inter reference frames.

sB

20 OP

Figure S. ranges

o

0

32

8

By analyzing the run-time profiling data of codec subfunc­ tions generated by varying the number of maximum search range, we can obtain the carbon footprint of H.264 codec

Max search range

as a function of maximum search range and quantization

Carbon footprint of H.264 at various values of maximum search

step size in Figure 5. It is worthwhile to emphasize that maximum search range has critical effect on the carbon footprint of H.264 codec. Reducing maximum search range can dramatically reduce the carbon footprint of the codec. The carbon footprint measurement of H.264 under various numbers of reference frames and quantification steps is shown in Figure 6. Carbon footprint of H.264 varies with

OJ

ON S2. C

0.68

(5 .2

0.66

.�

number of inter reference frames and quantization step size.

0.72

In the application scenarios where carbon is very critical,

0.7

reducing the number of reference number of video can lower the carbon footprint. On the other hand, the closer the reference frame is, the higher the frame correlation will be, except for occluded, and uncovered objects. Therefore,

c

.8 0.64 Co U 0.62 60

we should precede the blocking matching process from the nearest reference frame to the farthest reference frame. Thus,

15

a lot of carbon footprint is saved. Both experimental results provide a quantitative solution for carbon-footprint aware

OP

o 0

Inter reference number

computing. V. CONCLUSIONS

Figure 6. fames

Carbon footprint of H.264 at various numbers of inter reference

In this paper, we have developed a comprehensive frame­ work to measure carbon footprint of H.264 codec with

consideration of nonlinear battery effects. The measurement of carbon footprint of H.264 codec will benefit us to enhance energy efficiency in multimedia computing and performance optimization. The measurement results will enable designers to reduce energy consumption in data compression. The carbon footprint results can be adopted by various mobile multimedia devices, providing a design guideline for carbon­ footprint-aware computing. ACKNOWLEDGMENT

This work was partially supported by NSF under Grant No. 0708460 and No. 0801736. REFERENCES [1] D. Y ilmaz and M. Yildiz,"Analysis of the mobile phone effect on the heart rate variability by the calculation of correlation dimension," in 14th National Biomedical Engineering Meet­ ing, may 2009,pp. 1 -4. [2] C.-J. Lian, S.-Y. Chien, C. ping Lin, P.-c. Tseng, and L.­

G. Chen, "Power-Aware Multimedia: Concepts and Design Perspectives," pp. 26-34, 2007. [3] M. Horowitz, A. Joch, F.

Kossentini, and A. Hallapuro,

"H.264/avc baseline profile decoder complexity analysis," IEEE Transactions on Circuits and Systems for Video Tech­ nology, vol. 13,no. 7,pp. 707-716,2003. [4] Y.-W. Huang, c.-y. Chen, C.-H. Tsai, C.-F. Shen, and L.­

G. Chen, "Survey on block matching motion estimation algorithms and architectures with new results," Journal of VLSI Signal Processing Systems archive, vol. 42, no. 3, pp. 297-320,2006. [5] D. Marpe,T. Wiegand,and G. J. Sullivan,"The h.264/mpeg4 advanced video coding standard and its applications," IEEE Image Communications Magazine, vol. 44, no. 8, pp. 134143,2006. [6] R. Rao,S. V rudhula, and D. N.Rakhmatov, "Battery Mod­ eling for Energy-Aware System Design," IEEE Computer Society, vol. 36,pp. 77-87,Dec. 2003. [7] P. Rong and M. Pedram," An Analytical Model for Predicting the Remaining Battery Capacity of Lithium-Ion Batteries," IEEE Journal Very Large Scale Integrated, vol. 14,pp. 441451,May 2006. [8] V. Iverson, J. McVeigh, and B. Reese, " Real-time h.24-avc codec on intel architectures," in International Conference on Image Processing, vol. 2,oct. 2004,pp. 757 - 760 Vol.2. [9] J. Zhang,S. Ci, H. Sharif, and M. Alahmad, "An enhanced circuit-based model for single-cell battery," in IEEE Con­ ference on the Applied Power Electronics Conference and Exposition, Plam Sprints,CA,Faburary 2010. [10] B. I. Richardson and Y. Zhao, "Efficient mode selection for h.264 complexity reduction in a bayesian framework," in IEEE International Conference on Image Processing, 2006, pp. 1-30.

Battery Energy Consumption Footprint of Embedded ...

On the front of global warming issues and energy security, many researchers are engaged in identifying .... H.264 codec on an embedded platform Imote 2 with Linux 2.6.29 Kernel and an Xscale PXA processor [8], .... of carbon footprint of H.264 codec will benefit us to enhance energy efficiency in multimedia computing and ...

1MB Sizes 0 Downloads 50 Views

Recommend Documents

Learning Battery Consumption of Mobile Devices - Research at Google
Social networking, media streaming, text mes- saging, and ... chine Learning, New York, NY, USA, 2016. ... predict the battery consumption of apps used by ac-.

Energy Consumption Data.pdf
Page 1 of 8. Page 1 of 8. Page 2 of 8. Page 2 of 8. Page 3 of 8. Page 3 of 8. Page 4 of 8. Page 4 of 8. Energy Consumption Data.pdf. Energy Consumption Data.

Modeling NoC Traffic Locality and Energy Consumption ...
Jun 13, 2010 - not made or distributed for profit or commercial advantage and that copies bear this ..... algorithm for topology maintenance in ad hoc wireless.

LG RESU 10H Lithium-Ion Battery Energy Storage System 400V ...
LG RESU 10H Lithium-Ion Battery Energy Storage System 400V Datasheet.pdf. LG RESU 10H Lithium-Ion Battery Energy Storage System 400V Datasheet.pdf.

An Investigation in Energy Consumption Analyses and Application ...
estimates of up to 400,000 cellular phones will be sold per day by 2006 [15] and up to 16 million PDAs sold by 2008 [5]. The reason for such a rapid growth is the high demand of portable multimedia applications [57] which have time constraints as one

Area Throughput and Energy Consumption for ...
Department of Wireless Networks, RWTH Aachen University .... where we assumed that the cluster center lies at the origin. ... We call the interval between.

Detecting the footprint of changing atmospheric ...
Abstract. Although atmospheric nitrogen (N) deposition and climate changes are both recognized as major components of .... was used to explore changes in community composition over ...... policies and to provide meaningful management of.