NTHU-CS VLSI/CAD LAB TH EDA De-Shiuan Chiou Da-Cheng Juan Yu-Ting Chen Shih-Chieh Chang Department...

NTHU-CS VLSI/CAD LAB

TH EDATH EDA

De-Shiuan ChiouDe-Shiuan ChiouDa-Cheng JuanDa-Cheng JuanYu-Ting ChenYu-Ting Chen

Shih-Chieh ChangShih-Chieh Chang

Department of CS, National Tsing Hua University, Taiwan

Fine-Grained Sleep Transistor Sizing Algorithm for Leakage Power Minimization

Fine-Grained Sleep Transistor Sizing Algorithm for Leakage Power Minimization

2

OutlineOutline

Sleep Transistor Sizing ProblemSleep Transistor Sizing Problem

MIC Estimation MechanismMIC Estimation Mechanism

Partitioned Time-Frame for MIC EstimationPartitioned Time-Frame for MIC Estimation

Experimental Results and ConclusionsExperimental Results and Conclusions

3

Power GatingPower Gating

Leakage increases exponentiallyLeakage increases exponentially– reach 50% of total power in 65nm technologyreach 50% of total power in 65nm technology

Power GatingPower Gating– One of the most effective ways to reduce leakage reduce leakage

Low Vth Logic Device

VDD

GNDuse high Vth Sleep Transistorto reduce the leakage current

SLVGND

GND

4

C1 C2 C3

Implementation of Power GatingImplementation of Power Gating

Distributed Sleep Transistor Network (DSTN)Distributed Sleep Transistor Network (DSTN)

VDD

VGND

Low Vth Logic Device

SL SL SL

5

Leakage SavingLeakage Saving

In standby mode:In standby mode:– Leakage: Leakage: proportionalproportional to the ST’s size to the ST’s size– Small ST to reduce leakageSmall ST to reduce leakage

Ileakage

VDD

VGND

Ileakage Ileakage

6

Voltage Drop across the STVoltage Drop across the ST

In active mode:In active mode:– Voltage drop across a ST degrades the speedVoltage drop across a ST degrades the speed– Voltage drop: Voltage drop: inversely proportionalinversely proportional to the ST’s size to the ST’s size– Large ST to bound the voltage dropLarge ST to bound the voltage drop

VST

VDD

VGND

VST VST

7

VST* VST*

Sleep Transistor (ST) SizingSleep Transistor (ST) Sizing

Dilemma scenario:Dilemma scenario:– LargeLarge ST to bound the voltage drop. (active mode) ST to bound the voltage drop. (active mode)– SmallSmall ST to reduce leakage. (standby mode) ST to reduce leakage. (standby mode)

=>objective: =>objective: minimize ST size (leakage) under a specified minimize ST size (leakage) under a specified voltage drop constraint, voltage drop constraint, VVSTST**

VST

VDD

VGND

VST VSTVST*

8

C1 C2 C3

Estimate Voltage Drop by MICEstimate Voltage Drop by MIC

Maximum Instantaneous Current (MIC)Maximum Instantaneous Current (MIC) through the ST through the ST– determines the worst case voltage dropdetermines the worst case voltage drop

Estimating the upper bound of MIC(ST)Estimating the upper bound of MIC(ST)– for sizing ST appropriately to meet voltage drop constraintfor sizing ST appropriately to meet voltage drop constraint

MIC(ST1)

VDD

VGNDMIC(ST2) MIC(ST3)

MIC(ST): MIC across a ST.

9

C1 C2 C3

Estimate Voltage Drop by MICEstimate Voltage Drop by MIC

MICMIC((CC) (MIC of a cluster) is easy to measure) (MIC of a cluster) is easy to measure Due to current balancing effectDue to current balancing effect

– MICMIC((STST) (MIC through the ST) is hard to predict) (MIC through the ST) is hard to predict

MIC(ST1)

VDD

VGNDMIC(ST2) MIC(ST3)

MIC(C1)

Finding the MIC of a cluster is

fast

Finding the MIC across a ST is time-

consuming

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Temporal Perspective of Clusters’ MICTemporal Perspective of Clusters’ MIC

Traditional ways Traditional ways – use the use the entire clock period’s MICentire clock period’s MIC

to determine the ST sizeto determine the ST size

(Time Unit)

Cluster 1Cluster 2

MIC(C2) occurs at T9

one clock cycle

MIC(Ci) waveform

(Curr

ent)


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(Time Unit)

Curr

ent

(mA

)

Cluster 1Cluster 2


one clock cycle

MIC(Ci) waveform

Smaller time frames leads to:Smaller time frames leads to:– a more accurate MIC estimationa more accurate MIC estimation– high computation complexityhigh computation complexity

12

DifficultiesDifficulties

Current balancing effectCurrent balancing effect complicates the sizing problem complicates the sizing problem

Time-frame partitioningTime-frame partitioning leads to high computation complexity leads to high computation complexity

MIC MIC MIC

MIC

one clock cycle

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ContributionsContributions

A more accurate MIC prediction in a A more accurate MIC prediction in a temporal perspectivetemporal perspective

A A variable-length variable-length partitioning to reduce computation partitioning to reduce computation complexitycomplexity

Heuristics to minimize the size of sleep transistorsHeuristics to minimize the size of sleep transistors

Achieving 21% reduction in sleep transistor areaAchieving 21% reduction in sleep transistor area

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OutlineOutline





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Resistance NetworkResistance Network

I(ST1) I(ST2) I(ST3)

I(C1) I(C2) I(C3)

R(ST1) R(ST2) R(ST3)

RV RV

C1 C2 C3

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The discharging ratio can be calculated byThe discharging ratio can be calculated by– Kirchhoff’s Current LawKirchhoff’s Current Law– Ohm’s LawOhm’s Law

Discharging RatioDischarging Ratio

9 8 10

2 2

C1 C2 C3

0.43 I(C1) 0.34 I(C2) 0.23 I(C3)

I(C1)

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Discharging Matrix ΨDischarging Matrix Ψ

)(

)(

)(

)(

)(

)(

3

2

1

3

2

1

CI

CI

CI

Ψ

STI

STI

STI

→

333231

232221

131211

ψψψ

ψψψ

ψψψ

Ψwhere

I(ST1) I(ST2) I(ST3)

I(C1) I(C2) I(C3)

C1 C2 C3

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MIC(ST) Estimation MechanismMIC(ST) Estimation Mechanism

)(

)(

)(

)(

)(

)(

3

2

1

3

2

1

CMIC

CMIC

CMIC

Ψ

STMIC

STMIC

STMIC

→

MIC(ST1) MIC(ST2) MIC(ST3)

MIC(C1) MIC(C2) MIC(C3)

C1 C2 C3

333231

232221

131211

ψψψ

ψψψ

ψψψ

Ψwhere

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OutlineOutline





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Different MIC(Ci) occurs at different time points

(Time Unit)

Cluster 1Cluster 2


one clock cycle

MIC(Ci) waveform

(Curr

ent)


21


)(

)(

)(

)(

)(

)(

3

2

1

3

2

1

CMIC

CMIC

CMIC

Ψ

STMIC

STMIC

STMIC

Different MIC(Ci) occurs at different time points within a clock period

Traditional way to estimate MIC(STi) is over pessimistic

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Time-Frame Partitioning for MIC(ST) EstimationTime-Frame Partitioning for MIC(ST) Estimation

Expand MIC(Ci) into MIC(Ci,Tj)

(Time Frame)

Cluster 1Cluster 2

one clock cycle

MIC(Ci,Tj) waveform

(Curr

ent)

MIC(C1,T1)

MIC(C2,T1)

MIC(C1,T3)

MIC(C2,T3)

MIC(C1,T6)

MIC(C2,T6)

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For each time frame Tj, use MIC(Ci,Tj) to obtain MIC(STi,Tj)

( , ) ( , )

( , ) ( , )

( , ) ( , )

1 1 1 1

2 1 2 1

3 1 3 1

MIC ST T MIC C T

MIC ST T Ψ MIC C T

MIC ST T MIC C T


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For ST1, the maximum MIC(ST1,Tj) among all Tj is the upper bound of MIC(ST1) after partitioning

Cluster 1Cluster 2

(Time Frame)

one clock cycle

MIC(STi,Tj) waveform

MIC(ST1)

ST 1ST 2

(Curr

ent)

MIC(ST2)

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Cluster 1Cluster 2

(Time Frame)

one clock cycle

MIC(STi,Tj) waveform

MIC(ST1)

ST 1ST 2

MIC(ST2)

(Curr

ent)

ORIGINAL_MIC(ST1

) 37% larger!

ORIGINAL_MIC(ST2

)27% larger!

Time-Frame Partitioning leads to a better MIC(ST) estimation!

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Reduce the Computation ComplexityReduce the Computation Complexity

Increase the number of time frames leads toIncrease the number of time frames leads to– more accurate voltage drop estimationmore accurate voltage drop estimation– high computation complexityhigh computation complexity

Reduce the computation complexity:Reduce the computation complexity:– dominated time-frame removaldominated time-frame removal– variable length time-frame partitioningvariable length time-frame partitioning

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Dominated Time-Frame RemovalDominated Time-Frame Removal

TT33 is dominated by is dominated by TT66

– MICMIC((CC11,T,T66)) > MIC > MIC((CC11,T,T33))– MICMIC((CC22,T,T66)) > MIC > MIC((CC22,T,T33))

NeglectNeglect T T33 and all dominated time and all dominated time framesframes

Cluster 1Cluster 2

MIC(C1,T6)

MIC(C1,T3)

MIC(C2,T6)

MIC(C2,T3)

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((TTbb dominates dominates TTcc ) and () and (TTbb dominates dominates TTdd))=> the estimated upper bound will be smaller=> the estimated upper bound will be smaller

If all the If all the MICMIC((CCii) are separated, the ) are separated, the MICMIC((STSTii) can be better ) can be better estimated!estimated!

Variable Length Time-Frame PartitioningVariable Length Time-Frame Partitioning

Ta

uniform two-way partition variable length two-way partition

Tb TdTc

MIC(C1,Tb)

MIC(C2,Tb)

MIC(C1,Td)

MIC(C2,Td)

MIC(C1,Tc)

MIC(C2,Tc)

(1) (2)

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Problem Formulation of ST SizingProblem Formulation of ST Sizing

Inputs:Inputs:1.1. Voltage-drop constraintVoltage-drop constraint

2.2. MICMIC((CCii,,TTjj): Clusters’ MIC information): Clusters’ MIC information

Objective: minimize the total ST widthObjective: minimize the total ST width

Voltage drops must meet the constraintVoltage drops must meet the constraint

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ST Sizing AlgorithmST Sizing Algorithm

99 99 9999

1. Initialize ST size with a large value.

MIC(STi,Tj)= ． MIC(Ci,Tj)V(STi,Tj)=MIC(STi,Tj) ． R(STi

)

3. Update MIC(STi,Tj) and voltage drops.

Ψ

Return ST size

Yes

Voltage drops ok?

0.38 0.30 0.21 0.18

0.27 0.30 0.21 0.18

0.21 0.24 0.35 0.28

0.14 0.16 0.23 0.36

=Ψ

2. Update the discharging matrix.

No

4. Resize ST with the worst drop.

99 73 9999

kV

TSTMICW

ST

jiST )

*

),((*

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OutlineOutline





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Environment SetupEnvironment Setup

TSMC 130nm CMOS technologyTSMC 130nm CMOS technology

Vdd = 1.3 voltVdd = 1.3 volt

Specified tolerable IR drop: Specified tolerable IR drop: 5% of the ideal supply voltage5% of the ideal supply voltage

MICMIC((CCii,T,Tjj) is obtained via 10,000-random-pattern ) is obtained via 10,000-random-pattern PrimePower simulationsPrimePower simulations

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Implementation FlowImplementation Flow

RTL netlist

SDF file

Gate Positioning

Gate location

VCD Partitioning

Partitioned VCD file

: Our tools

: Commercial tools

Synthesis

Gate-level netlist

MIC Estimation

V-length Partitioning (Optional)

ST sizeST Sizing

Simulation

VCD file

Placement

DEF file

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Experimental ResultsExperimental Results

Avg.

AES

des

t481

i8

frg2

dalu

C7552

C5315

C3540

C1355

C880

C499

C432

Circuit

1 8.09 1.06 1 1.26 1.70

35242837928137272293396544378

1180832181457850976611804

1514162895402502473899405

1080772081417836993113247

1367012255223228353632

48338162283211029043468

28961721625621242692950041016

21901383019534187852377329794

9421685620282186502302029808

422251411496105911305619352

3452561967692331129615050

568364472296684834710741

495426270866775849112817

V-TPTPV-TPTP[2][8]

Runtime (Sec.)Total Area (Width in μm)

Previous works: [2] Chiou et al. DAC’06, [8] Long et al. DAC’03

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ConclusionsConclusions

Propose an efficient sleep transistor sizing method Propose an efficient sleep transistor sizing method for DSTN power gating designsfor DSTN power gating designs

Present theorems based on Present theorems based on temporal perspectivetemporal perspective for for estimating a tight upper bound of voltage dropestimating a tight upper bound of voltage drop

Achieving 21% size (leakage) reductionAchieving 21% size (leakage) reduction

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Thank You!Thank You!

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Relations between Relations between WWSTST, and , and VVSTST..

Sleep Transistors operate in Sleep Transistors operate in linear regionlinear region in active in active mode.mode.

kV

STIW

STST )

)((

VDD

VGND

GND

I(ST)

I(ST): the current through the sleep

transistor

VST

VST: the voltage drop across the sleep transistor

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Determine the Determine the minimum required sizeminimum required size ( (WWSTST** ) ) based on:based on:1.1. MICMIC((STST))

2.2. VVSTST**:: IR-drop constraintIR-drop constraint

kV

STMICW

STST )

*

)((*

VDD

VGND

GND

MIC(ST)

MIC(ST): Maximum Instantaneous Current (MIC) through STk

V

STIW

STST )

)((

Smaller MIC(ST) leads to a better ST size!

NTHU-CS VLSI/CAD LAB TH EDA De-Shiuan Chiou Da-Cheng Juan Yu-Ting Chen Shih-Chieh Chang Department...

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