tackling the battery problem...tackling the battery problem for continuous mobile vision june 11,...
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Tackling the Battery Problemfor Continuous Mobile Vision
June 11, 2013
Victor Bahl
Robert LeKamWa (MSR/Rice), Bodhi Priyantha, Mathai Philipose, Lin Zhong (MSR/Rice)
MIT Technology Review Mobile Summit 2013
resource poverty hurts
Adam & Eve 2000 AD
Hu
ma
n A
tte
ntio
n
courtesy. M. Satya, CMU
technology should reduce the demand on human attention
clever exploitation of {context awareness, computer vision, machine learning, augmented reality} needed to deliver vastly superior mobile user experience
• no “Moore’s Law” for human attention
• being mobile consumes greater human attention
• already scarce resource is further taxed by resource poverty
continuous mobile visionreality vs. movies
Steve Mann (early 90s)
Mission Impossible 4 (2011)
COBOT, CMU (2013)
iRobot (2004)
C-3PO (1977)
Victor Bahl, MSR
perennial challenges
computation
connectivity & bandwidth
battery
Resource constraints prevent today’s mobile apps from reaching their full potential
white space networks, small cell networks, mm-wave networks
Augmented Reality
cloudlets
MSR’s SenseCam for memory assistance
Victor Bahl, MSR
battery trends
0
50
100
150
200
250
91 92 93 94 95 96 97 98 99 00 01 02 03 04 05
Wh
/Kg
Year
Li-Ion Energy Density
CPU performance improvement during same period: 246x
A silver bullet seems unlikely
• Lagged behindoHigher voltage batteries (4.35
V vs. 4.2V) – 8% improvemento Silicon anode adoption (vs.
graphite) – 30% improvement
• Trade-offs o Fast charging = lower capacityo Slow charging = higher
capacity
Victor Bahl, MSR
Single Core
Processor CPU + GPU
~150 mW
Sensors +
Memory + Disk
~ 15 mW
Display
~500 mW
Network Stack(5 min. of usage / hour)
~100 mW
so where is the energy going?
assuming a typical SmartPhone battery of 1500 mAh (~5.5 W)
battery lifetime ~7.25 hours
power consumption of a typical image sensor
5 MP, 5 fps
345 mW
1 MP, 5 fps
250 mW
0.3 MP, 15 fps
245 mW
1 MP, 15 fps
295 mW
0.3 MP, 5 fps
232 mW
0.3 MP, 30 fps
268 mW
0.3 MP, 5 fps
232 mW
low resolution, low frame rate image sensing for vision related tasks can reduce battery life by > 25%
Reduceresolution
Reduce frame rate
state of artEnergy / pixel is inversely proportional to the frame rate & image resolution
0 10 20 300
50
100
150
200
FPSP
ow
er
(mW
)
Video at 0.1 MP power vs. resolution
0 5 10
x 106
0
100
200
300
Pow
er
(mW
)
Npixels
Video at 30 fps
power vs. frame rate
Regardless of image resolution & frame rate, image sensors consume about the same power
Profiled 5 image sensors from 2 manufacturers
Victor Bahl, MSR
digging deeper (1 MP, 5 fps)
Active Period
Idle Period
function of pixel count
& clock speed
function of frame rate
reduce power by reducing pixel readout time
Number of Pixels
divided by
Clock Frequency
reduce this
one pixel is read out per clock period
Victor Bahl, MSR
reducing pixel count (N)
ActiveFrame
Readout
Active
Readout
Po
we
r
Active
Readout Po
we
r
Time Time
Scaled Resolution
(Pixel Skipping)
Region-of-Interest
(Windowing)
reduce power by aggressive use of standby
Turn off sensor during idle period
Idle mode necessary to allow exposure before readout
Active
Readout
Active
Readout
Active
Readout
Active
Readout
Active
Readout
Active
Readout
Standby mode
Idle mode
Best when frame rate and resolution are sufficiently low
reduce power by adjusting clock frequency
Adjust clock frequency to minimize power
Adjust this
frequency
1 fps
5 fps
Power
frequency
At low frame rates, run the clock as slow as possible
Tradeoff
summarizing power reduction techniques
reduce Tactive & increase Tidle
decrease frame rate
reduce total pixel readout time (by reducing N)
adapt clock frequency
Instead of idle-ing put sensor in standby state
reduce Pactive (not covered in this talk, see paper)
applying these techniquesF
ram
e r
ate
(F
PS
)
Resolution (MP)
30
25
20
15
10
5
021 3 4 5
Fra
me
ra
te (
FP
S)
Resolution (MP)
30
25
20
15
10
5
021 3 4 5
Po
we
r (m
W)
350
300
250
200
150
100
50
0
Aggressive Standby &
Clock OptimizationUnoptimized
impact on vision algorithms
480 x 270
Person DetectionImage registration
Image Registration
Success
Person Detection
Success
Actual Power
Reduction with
software assist
Estimated Power
Reduction with
hardware assist
Full Resolution
(129600 pixels)99.9% 94.4% 51% 84%
Frame Rate- 3 FPS 95.7% 83.3% 95% 98%
30% Window
(63504 pixels)96.5% 77.8% 63% 91%
Subsampled by 2
(32400 pixels)91.8% 72.2% 71% 94%
MSR’s Glimpse project
collaborators & references
R. LeKamWa, B. Priyantha, M. Philipose, L. Zhong, P. Bahl, Energy
Characterization and Optimization of Image Sensing Towards
Continuous Mobile Vision, Proceedings of ACM MobiSys 2013, Taipei,
Taiwan, June 26-29, 2013
P. Bahl, M. Philipose, L. Zhong, Cloud-Powered Sight for All: Showing the
Cloud What You See, ACM Mobile Cloud Computing & Services
Workshop, Lake District, U.K. June 25, 2012
Robert Bodhi Matthai Lin
Thanks!
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