Post on 31-May-2020
Optimization of MPSoc System Solution ProposalSelected task mapping technique based on power usage assessment
Ju
2015 Q.E.
Contents
1.Background
2.Analysis
3.Proposed SystemOverview of Selected Task Mapping Technique
1) End-User Power Usage Assessment
2) Establishment of Strategies for Each Applications
3) Simulation Result
4.Conclusion
Contents
1.Background
2.Analysis
3.Proposed SystemOverview of Selected Task Mapping Technique
1) End-User Power Usage Assessment
2) Establishment of Strategies for Each Applications
3) Simulation Result
4.Conclusion
Why MPSoC?
- Limitation of Single Core
- Improve Overall Performance
MPSoC System
Power Consumption vs Performance
- Second Ranked in the power consumption share
- Limited Battery Capacity
- High System Requirement
State-of-art MPSoC
MPSoc in SmartPhone
MPSoC with big.LITTLE
- Performance driven big core
- Power efficiency driven LITTLE core
MPSoC with big.LITTLE
- Samsung Exynos Octa Die
Contents
1.Background
2.Analysis
3.Proposed SystemOverview of Selected Task Mapping Technique
1) End-User Power Usage Assessment
2) Establishment of Strategies for Each Applications
3) Simulation Result
4.Conclusion
1) Clustered Switching
2) In-Kernel Switcher
3) Heterogeneous Multi-Processing
Run-State Migration: Specific Usage Method of big.LITTLE architecture
1) Clustered Switching
Only one Core is Active and Another Core is Inactive
2) In-Kernel Switcher
Each Four Virtual Core Act as One core
3) Heterogeneous Multi-Processing
The most Advanced StrategyAll Tasks can migrate to all cores
HMP - Task Mapping ProblemFocused Issue
Task Mapping Problem
• Optimization Scheduling ->
NP-hard problem
Contents
1.Background
2.Analysis
3.Proposed SystemOverview of Selected Task Mapping Technique
1) End-User Power Usage Assessment
2) Establishment of Strategies for Each Applications
3) Simulation Result
4.Conclusion
Overview of Selected Task Mapping Technique
Proposed System
Motivation• Application Usage Assessment
• Top 5 has overall 88% share
• Focus on only 5 application
can make great efficiency
Motivation
Design-Time Mapping
Real-Time Mapping
Hybrid Mapping• Design-time in each App
• Real-time Selection of App
19
Contents
1.Background
2.Analysis
3.Proposed SystemOverview of Selected Task Mapping Technique
1) End-User Power Usage Assessment
2) Establishment of Strategies for Each Applications
3) Simulation Result
4.Conclusion
End-User Power Usage AssessmentPEOPLE
TOP 5 BATTERY USAGE (%)1ST 2ND 3RD 4TH 5TH SUM
A 35 28 10 7 7 87
B 43 18 9 9 5 84
C 27 23 12 6 6 74
D 18 17 16 14 10 75
E 21 16 14 14 13 78
F 39 20 20 9 4 92
G 30 14 13 11 9 77
H 30 19 17 13 6 85
I 55 17 10 6 5 93
J 33 26 21 9 2 91
K 37 18 13 8 4 80
L 35 18 16 13 6 88
M 33 29 21 5 5 93
N 26 12 11 7 6 62
O 40 13 8 8 8 77
P 35 20 19 13 7 94
Top 5 Battery
Usage Proportion
Of Applications
(N = 16).
End-User Power Usage Assessment
1st Usage 5th Usage Total Usage
Mean 33.6 6.4 83.1
SD 8.8 2.6 9DATA DESCRIPTIVE STATISTICS (N=16, VALUE BETWEEN 0 AND 100 ON A PERSENTAGE SCALE)
• Only few applications influenced their
tendency of power usage
22
Contents
1.Background
2.Analysis
3.Proposed SystemOverview of Selected Task Mapping Technique
1) End-User Power Usage Assessment
2) Establishment of Strategies for Each Applications
3) Simulation Result
4.Conclusion
Establishment of Strategies for Each Applications
Facebook4.5 screen
13.8 background
CoC3.9 screen
4min background
Friends Pop3.1 creen
3min background
KakaoTalk2 screen
32.8 background
Establishment of Strategies for Each Applications
• Trade-Off energy or speed
• Previously calculated design-time solution
Facebook4.5 screen
13.8 background
CoC3.9 screen
4min background
KakaoTalk2 screen
32.8 background
Multimedia modeor
Fix Little cluster Dynamic mode Fix Little cluster
25
Contents
1.Background
2.Analysis
3.Proposed SystemOverview of Selected Task Mapping Technique
1) End-User Power Usage Assessment
2) Establishment of Strategies for Each Applications
3) Simulation Result
4.Conclusion
Simulation Result
• ODROID-XU3
• Exynos5422 Cortex-A15 2.0Ghz quad core as big
core and Cortex-A7 quad core as LITTLE core
Simulation Result
• Current sensing module
• MCU : mbed LPC1768
• Sensor : WCS2210
Simulation Result
• Current sensing module
• MCU : mbed LPC1768
• Sensor : WCS2210
Start Time
Time current
Simulation Result
Facebook CoC3D Racing
GameHD Video
• Focused Applications
1 minuteView
timeline
1 minute play
game
1 minute play
game
1 minuteplay
Simulation Result
• Rebuild and flash the Kernel of Android for setting
the cpus
• Easily change because it is based on linux and
opensource
Simulation Result• CPU configurations
big : 4LITTLE : 3
big : 4LITTLE : 2
big : 4LITTLE : 1
big : 4LITTLE : 0
(CANNOT BY OS)
big : 3LITTLE : 4
big : 2LITTLE : 4
big : 1LITTLE : 4
big : 0LITTLE : 4
big : 4LITTLE : 4
(NORMAL)
big : 3LITTLE : 3
big : 2LITTLE : 2
big : 1LITTLE : 1
Simulation Result• IDLE
big : 4LITTLE : 3
2.05W
big : 4LITTLE : 2
2.40W
big : 4LITTLE : 1
1.97W
big : 4LITTLE : 0
(CANNOT BY OS)
big : 3LITTLE : 4
2.01W
big : 2LITTLE : 4
1.97W
big : 1LITTLE : 4
2.04W
big : 0LITTLE : 4
0.96W
big : 4LITTLE : 4
(NORMAL)2.00W
big : 3LITTLE : 3
2.00W
big : 2LITTLE : 2
1.94W
big : 1LITTLE : 1
1.93W
Simulation Result• Facebook – 1minute view timeline
big : 4LITTLE : 3
4.72W
big : 4LITTLE : 2
4.04W
big : 4LITTLE : 1
3.16W
big : 4LITTLE : 0
(CANNOT BY OS)
big : 3LITTLE : 4
4.06W
big : 2LITTLE : 4
4.78W
big : 1LITTLE : 4
3.98W
big : 0LITTLE : 4
1.73W
big : 4LITTLE : 4
(NORMAL)3.76W
big : 3LITTLE : 3
3.77W
big : 2LITTLE : 2
3.62W
big : 1LITTLE : 1
2.82W
Simulation Result• Facebook – 1minute view timeline
big : 4LITTLE : 3
4.72W
big : 4LITTLE : 2
4.04W
big : 4LITTLE : 1
3.16W
big : 4LITTLE : 0
(CANNOT BY OS)
big : 3LITTLE : 4
4.06W
big : 2LITTLE : 4
4.78W
big : 1LITTLE : 4
3.98W
big : 0LITTLE : 4
1.73W
big : 4LITTLE : 4
(NORMAL)3.76W
big : 3LITTLE : 3
3.77W
big : 2LITTLE : 2
3.62W
big : 1LITTLE : 1
2.82W
Selected
Simulation Result• COC – 1minute game playing
big : 4LITTLE : 3
3.94W
big : 4LITTLE : 2
3.62W
big : 4LITTLE : 1
3.88W
big : 4LITTLE : 0
(CANNOT BY OS)
big : 3LITTLE : 4
3.72W
big : 2LITTLE : 4
3.92W
big : 1LITTLE : 4
3.72W
big : 0LITTLE : 4
2.00W
big : 4LITTLE : 4
(NORMAL)3.55W
big : 3LITTLE : 3
3.42W
big : 2LITTLE : 2
3.46W
big : 1LITTLE : 1
3.53
Simulation Result• COC – 1minute game playing
big : 4LITTLE : 3
3.94W
big : 4LITTLE : 2
3.62W
big : 4LITTLE : 1
3.88W
big : 4LITTLE : 0
(CANNOT BY OS)
big : 3LITTLE : 4
3.72W
big : 2LITTLE : 4
3.92W
big : 1LITTLE : 4
3.72W
big : 0LITTLE : 4
2.00W
big : 4LITTLE : 4
(NORMAL)3.55W
big : 3LITTLE : 3
3.42W
big : 2LITTLE : 2
3.46W
big : 1LITTLE : 1
3.53W
Selected
Simulation Result• 3D Racing – 1minute game playing
big : 4LITTLE : 3
8.10W
big : 4LITTLE : 2
7.89W
big : 4LITTLE : 1
7.24W
big : 4LITTLE : 0
(CANNOT BY OS)
big : 3LITTLE : 4
7.66W
big : 2LITTLE : 4
7.84W
big : 1LITTLE : 4
7.46W
big : 0LITTLE : 4
3.40W
big : 4LITTLE : 4
(NORMAL)7.59W
big : 3LITTLE : 3
7.56W
big : 2LITTLE : 2
7.66W
big : 1LITTLE : 1
6.81W
Simulation Result• 3D Racing – 1minute game playing
big : 4LITTLE : 3
8.10W
big : 4LITTLE : 2
7.89W
big : 4LITTLE : 1
7.24W
big : 4LITTLE : 0
(CANNOT BY OS)
big : 3LITTLE : 4
7.66W
big : 2LITTLE : 4
7.84W
big : 1LITTLE : 4
7.46W
big : 0LITTLE : 4
3.40W
big : 4LITTLE : 4
(NORMAL)7.59W
big : 3LITTLE : 3
7.56W
big : 2LITTLE : 2
7.66W
big : 1LITTLE : 1
6.81W
Selected
Simulation Result• HD video – 1minute movie playing
big : 4LITTLE : 3
2.37W
big : 4LITTLE : 2
2.20W
big : 4LITTLE : 1
2.34W
big : 4LITTLE : 0
(CANNOT BY OS)
big : 3LITTLE : 4
2.26W
big : 2LITTLE : 4
2.36W
big : 1LITTLE : 4
2.24W
big : 0LITTLE : 4
1.12W
big : 4LITTLE : 4
(NORMAL)2.05W
big : 3LITTLE : 3
1.95W
big : 2LITTLE : 2
2.31W
big : 1LITTLE : 1
2.06W
Simulation Result• HD video – 1minute movie playing
big : 4LITTLE : 3
2.37W
big : 4LITTLE : 2
2.20W
big : 4LITTLE : 1
2.34W
big : 4LITTLE : 0
(CANNOT BY OS)
big : 3LITTLE : 4
2.26W
big : 2LITTLE : 4
2.36W
big : 1LITTLE : 4
2.24W
big : 0LITTLE : 4
1.12W
big : 4LITTLE : 4
(NORMAL)2.05W
big : 3LITTLE : 3
1.95W
big : 2LITTLE : 2
2.31W
big : 1LITTLE : 1
2.06W
Selected
Simulation Result• Normal Configuration
Facebook CoC3D Racing
GameHD Video
1 minuteView
timeline
1 minute play
game
1 minute play
game
1 minuteplay
• 3.76W+3.55W+7.59W+2.05W = 16.95Wm
big : 4LITTLE : 4
(NORMAL)
Simulation Result• Proposed Selecting Configuration
Facebook CoC3D Racing
GameHD Video
1 minuteView
timeline
1 minute play
game
1 minute play
game
1 minuteplay
• 2.82W + 3.42W + 7.24W + 1.95W = 15.43W 9%↓
big : 3LITTLE : 3
1.95W
big : 4LITTLE : 1
7.24W
big : 3LITTLE : 3
3.42W
big : 1LITTLE : 1
2.82W
43
Contents
1.Background
2.Analysis
3.Proposed SystemOverview of Selected Task Mapping Technique
1) End-User Power Usage Assessment
2) Establishment of Strategies for Each Applications
3) Simulation Result
4.Conclusion
Conclusion
The selected design-time task mapping solution
via power usage assessment is proposed and it
selects only top 5 applications of power
consumption applying optimal design-time task
mapping solutions.
It is large difference with other studied because it
uses end-user’s information in SW scheduling
level. 44
Limitations and Future works
45
The proposed solution was controlling task
scheduler via each applications but I simulated
the controlling MPSoC CPU configurations.
This is due to limitation of changing schedule
algorithm
Future work should find the suitable task
algorithm for each applications and confirm the
validity.
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Q & A