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Trading power and performanceto achieve optimal thermal designfor battery-powered devicesMark Benson

CONFIDENTIAL 2

Mark Benson, Director of Software Strategy, Logic PD

History of Logic PD• 1960’s Founded as Polivka Logan• 1980’s Added Mechanical Eng• 1990’s Added Software, Electrical

Eng• 2000’s Added Products,

Manufacturing

Products and Services• Product Design• Product Engineering• Embedded Products• Manufacturing

Industries • Industrial, Medical, Aerospace,

Military

Employees• 130 design consultants• 400 operations staff

Geographies• Minneapolis, Boston, San Diego

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Agenda1. Introduction to thermal problems that

exist in small battery-powered devices2. Summary of software techniques for

managing thermal performance3. Case study: Kindle Fire4. Conclusion summary and areas for future

study and research

Gist: dynamic scaling is key, and we need more software tools and research to put this fact in the forefront of the minds of software and systems engineers

1970 1975 1980 1985 1990 1995 2000 2005 20100.1

110

1001000

10000100000

100000010000000

Intel CPU Trends

Transistors (000) Clock Speed (MHz)Power (W)

We’ve got problems*

* Source: http://www.gotw.ca/publications/concurrency-ddj.htm

Thermodynamic issues

Signal integrity issues

Portable devices make it worseProblems• High ambient

temperatures• Small thermal mass• Sealed enclosures

Solutions1. Don’t generate heat2. Remove existing heat3. Define your environment

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Safety

Reliability

Heat and power are related

• TI DM3730 running at 600 MHz

• Math-heavy test (100% ARM load)

• Heat is a function of power8

0 27 54 81 108135162189216243270297324351378405300

350

400

450

500

42434445464748

Power (mW) Temp (°C)

Seconds

Pow

er (

mW

)

Tem

p (°

C)

Heat Power

Performance

Pow

erDynamic power (Pdynamic = CfV2)

1. Scaling the curve2. Moving the curve3. Finding alternative

curves

Notdesirable

Lightweightprocessing

Heavyweightprocessing

Dynamicscaling

Performance

Pow

er

• Processor provides: power states, DVFS• You provide: operating points (DVFS

policies)• You provide: suspend peripheral

coordination

Scaling the curve: power states

OffSuspend

Running

OPP1OPP2

OPP3OPP4

OPP5OPP6

Power States Operating PointsOPP = {f, V}

DVFS Engine

Feeds

• Deeper sleep consumes less power• Deeper sleep takes longer to wake up

• Each design (HW + SW) creates unique profile

Scaling the curve: wake time

Off Suspend Idle Running

Power / stateWake time / state

Power State

Pow

er

Tim

e

Scaling the curve: boot quickly

Xloader Uboot Kernel Shell GUI Framework

Xloader Kernel Shell

GUI Framework

50s

10s

Drivers /dev30s

Normal boot

Fast boot

Pow

er

Performance

Spend moretime here

Get heremore quickly

Moving the curve: voltage tuning• Some SoC’s have per-chip voltage calibrations• Examples: SmartReflex™, power/clock gating • These often require companion chips (PMIC)

Voltage tuning offVoltage tuning on

Performance

Pow

er

Goal: lower power at equivalent performance

Finding alternative curves• Applications processors have many curves

• Make sure your app uses the right curves• Use dynamic scaling to your advantage

ARMDSPISP

Performance

Pow

er

1. Image resizing, color conversion, AWB, AE, AF2. Audio/video codecs, data processing3. GUI, communications stacks

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Kindle Fire Case Study

Case study: Kindle Fire• TI OMAP 4430, 7” 600x1024 display• Wi-Fi 802.11n, USB 2.0, 8 GB of storage• Android Gingerbread 2.3, 4400 mAh battery

Kindle Fire: dynamic scaling• Dynamic scaling is used (Pdynamic = CfV2)• System under full load (ANTuTu Benchmark*)

Cores: 1 @ 600 MHzPower: 2158 mWTemp: 43.5º C

Cores: 2 @ 300 MHzPower: 1930 mWTemp: 42º C

750

1500

2000

2500

3000

4000

5000

1600

2000

2400

2800

3200

3600

1 core (load)

Performance (DMIPS)

Pow

er (

mW

) Under load, multicoreperforms better

* ANTuTu Benchmarkhttp://www.antutulabs.com

Kindle Fire: dynamic scaling• When idling, the opposite is true• During idle, it’s more efficient to turn off a core

Cores: 1 @ 600 MHzPower: 1611 mWTemp: 38.5º C

Cores: 2 @ 300 MHzPower: 1729 mWTemp: 40º C

750

1500

2000

2500

3000

4000

5000

15001600170018001900200021002200

1 core (idle)

Performance (DMIPS)

Pow

er (

mW

)

When idling, single coreperforms better

Kindle Fire: voltage tuning• Adjusts voltage based on per-chip calibration• Benefits occur across all power states

Freq: 800 MHzSmartReflex™: offPower: 3097mWTemp: 50º C

Freq: 800 MHzSmartReflex™: onPower: 2690 mWTemp: 45º C

300 600 800 10001500

2000

2500

3000

3500

4000

SR off (load) SR on (load)

Performance (MHz)

Pow

er (

mW

)

SmartReflex™ movesthe curve

Off Suspend Idle Load0

5001000150020002500300035004000

0100002000030000400005000060000

Power / state Wake time / statePower State

Pow

er (m

W)

Tim

e (m

s)

Kindle Fire: wake time

Suspend62 mW31º C180 ms wake

Idle1721 mW34.2º C0 ms wake

Load3431 mW51.1º C0 ms wake

• Suspend: great power savings plus reasonable wake time

Off0 mW25º C52000 ms wake

Kindle Fire: summary• Kindle Fire uses:

– Dynamic scaling– Voltage tuning (not all SoCs use this)– Low-power suspend– Most/all features of the chip

(entitlement)• You should too

Conclusion• Summary

– Lower power required = lower heat generated

– Remember law of dynamic power (Pdynamic = CfV2)

– Use all parts of the chip available to you• Areas for future research

– Software tools that enable thermal/power visibility

– Tools and methods for limiting run-time power

– Software for auto-managing dynamic power22

It’s all about the user

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Thank You

Mark Bensonmark.benson@logicpd.com