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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
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
10
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)
MIC(C1) occurs at T6
11
(Time Unit)
Curr
ent
(mA
)
Cluster 1Cluster 2
Temporal Perspective of Clusters’ MICTemporal Perspective of Clusters’ MIC
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
13
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
14
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
15
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
16
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
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
20
Temporal Perspective of Clusters’ MICTemporal Perspective of Clusters’ MIC
Different MIC(Ci) occurs at different time points
(Time Unit)
Cluster 1Cluster 2
MIC(C2) occurs at T9
one clock cycle
MIC(Ci) waveform
(Curr
ent)
MIC(C1) occurs at T6
21
Temporal Perspective of Clusters’ MICTemporal Perspective of Clusters’ MIC
)(
)(
)(
)(
)(
)(
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
22
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)
23
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
Time-Frame Partitioning for MIC(ST) EstimationTime-Frame Partitioning for MIC(ST) Estimation
24
Time-Frame Partitioning for MIC(ST) EstimationTime-Frame Partitioning for MIC(ST) Estimation
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)
25
Time-Frame Partitioning for MIC(ST) EstimationTime-Frame Partitioning for MIC(ST) Estimation
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!
26
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
– 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)
28
((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!
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
33
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
34
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
35
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