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