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EE141 1 EECS141 1 Lecture #22 EE141 EE141- Fall 2007 Fall 2007 Digital Integrated Digital Integrated Circuits Circuits Lecture 22 Lecture 22 Domino Logic Domino Logic Power Revisited Power Revisited EE141 2 EECS141 2 Lecture #22 Announcements Announcements Project phase two due next Tuesday EE141 3 EECS141 3 Lecture #22 Class Material Class Material Last lecture Adders Today’s lecture Domino logic Revisit power Reading Chapter 6, Chapter 7 EE141 4 EECS141 4 Lecture #22 Domino Logic Domino Logic EE141 5 EECS141 5 Lecture #22 Domino Logic Domino Logic In 1 In 2 PDN In 3 M e M p Clk Clk Out1 In 4 PDN In 5 M e M p Clk Clk Out2 M kp 1 1 1 0 0 0 0 1 EE141 6 EECS141 6 Lecture #22 Why Named Domino? Why Named Domino? Clk Clk In i PDN In j In i In j PDN In i PDN In j In i PDN In j Like falling dominos!

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EE1411

EECS141 1Lecture #22

EE141EE141--Fall 2007Fall 2007Digital Integrated Digital Integrated CircuitsCircuits

Lecture 22Lecture 22Domino LogicDomino LogicPower RevisitedPower Revisited

EE1412

EECS141 2Lecture #22

AnnouncementsAnnouncementsProject phase two due next Tuesday

EE1413

EECS141 3Lecture #22

Class MaterialClass Material

Last lectureAdders

Today’s lectureDomino logicRevisit power

ReadingChapter 6, Chapter 7

EE1414

EECS141 4Lecture #22

Domino LogicDomino Logic

EE1415

EECS141 5Lecture #22

Domino LogicDomino Logic

In1

In2 PDNIn3

Me

Mp

Clk

Clk Out1

In4 PDNIn5

Me

Mp

Clk

ClkOut2

Mkp

1 → 11 → 0

0 → 00 → 1

EE1416

EECS141 6Lecture #22

Why Named Domino?Why Named Domino?

Clk

Clk

Ini PDNInj

IniInj

PDN Ini PDNInj

Ini PDNInj

Like falling dominos!

EE1417

EECS141 7Lecture #22

Properties of Domino LogicProperties of Domino Logic

Only non-inverting logic can be implementedVery high speed

static inverter can be skewed, only L-H transition criticalInput capacitance reduced – smaller logical effort

EE1418

EECS141 8Lecture #22

Domino Logic LEDomino Logic LE

EE1419

EECS141 9Lecture #22

Domino Logic LE (skewed static gate)Domino Logic LE (skewed static gate)

EE14110

EECS141 10Lecture #22

Buffer Buffer ““AverageAverage”” LELE

EE14111

EECS141 11Lecture #22

Optimal EF/stage with DominoOptimal EF/stage with DominoDomino buffers are faster than static CMOS invertersIs optimal EF/stage for a chain of domino gates still 4?

EE14112

EECS141 12Lecture #22

Designing with Domino LogicDesigning with Domino Logic

Mp

Me

VDD

PDN

Clk

In1In2In3

Out1

Clk

Mp

Me

VDD

PDN

Clk

In4

Clk

Out2

Mr

VDD

Inputs = 0during precharge

Can be eliminated

EE14113

EECS141 13Lecture #22

Footless DominoFootless Domino

The first gate in the chain needs a foot switchPrecharge is rippling – short-circuit current

VDD

Clk Mp

Out1

In1

1 0

VDD

Clk Mp

Out2

In2

VDD

Clk Mp

Outn

InnIn3

1 0

0 1 0 1 0 1

1 0 1 0

EE14114

EECS141 14Lecture #22

Footless DominoFootless Domino

Can mitigate short-circuit current by alternating between footed and unfooted domino

EE14115

EECS141 15Lecture #22

Footless DominoFootless Domino

To eliminate the short-circuit current, can delay the clock for each stage

VDD

Clk Mp

Out1

In1

1 0

VDD

Clk Mp

Out2

In2

VDD

Clk Mp

Outn

InnIn3

1 0

0 1 0 1 0 1

1 0 1 0

EE14116

EECS141 16Lecture #22

Differential (Dual Rail) DominoDifferential (Dual Rail) Domino

A

B

Me

Mp

Clk

ClkOut = AB

!A !B

MkpClk

Out = ABMkp Mp

Allows inverting gates to be built

1 0 1 0

onoff

EE14117

EECS141 17Lecture #22

npnp--CMOSCMOS

In1

In2 PDNIn3

Me

Mp

Clk

Clk Out1

In4 PUNIn5

Me

MpClk

Clk

Out2(to PDN)

1 → 11 → 0

0 → 00 → 1

Only 0 → 1 transitions allowed at inputs of PDN Only 1 → 0 transitions allowed at inputs of PUN

EE14118

EECS141 18Lecture #22

NORA LogicNORA Logic

In1

In2 PDNIn3

Me

Mp

Clk

Clk Out1

In4 PUNIn5

Me

MpClk

Clk

Out2(to PDN)

1 → 11 → 0

0 → 00 → 1

to otherPDN’s

to otherPUN’s

Fast, but EXTREMELY sensitive to noise!

EE14119

EECS141 19Lecture #22

Power Power RevisitedRevisited

EE14120

EECS141 20Lecture #22

Transition Activity and PowerTransition Activity and PowerEnergy consumed in N cycles, EN:

EN = CL • VDD2 • n0→1

n0→1 – number of 0→1 transitions in N cycles

fVCN

nfNEP DDLN

NNavg ⋅⋅⋅⎟

⎠⎞

⎜⎝⎛=⋅= →

∞→∞→

210limlim

fN

nN

⋅= →

∞→→10

10 limα

fVCP DDLavg ⋅⋅⋅= →2

10α

EE14121

EECS141 21Lecture #22

“Dynamic” or timing dependent component

Type of Logic Function (NOR vs. XOR)“Static” component (does not account for timing)

Circuit Topology

Type of Logic Style (Static vs. Dynamic)

Signal StatisticsInter-signal Correlations

Signal Statistics and Correlations

Factors Affecting Transition ActivityFactors Affecting Transition Activity

EE14122

EECS141 22Lecture #22

Type of Logic Function: NORType of Logic Function: NOR

011001010100

OutBA

Example: Static 2-input NOR Gate

Assume signal probabilitiespA=1 = 1/2pB=1 = 1/2

Then transition probabilityp0→1 = pOut=0 x pOut=1

= 3/4 x 1/4 = 3/16

α0→1 = 3/16

If inputs switch every cycle

EE14123

EECS141 23Lecture #22

Type of Logic Function: NANDType of Logic Function: NAND

011101110100

OutBA

Example: Static 2-input NAND Gate

Assume signal probabilitiespA=1 = 1/2pB=1 = 1/2

Then transition probabilityp0→1 = pOut=0 x pOut=1

=

α0→1 =

If inputs switch every cycle

EE14124

EECS141 24Lecture #22

Type of Logic Function: XORType of Logic Function: XOR

011101110000

OutBA

Example: Static 2-input XOR Gate

Assume signal probabilitiespA=1 = 1/2pB=1 = 1/2

Then transition probabilityp0→1 = pOut=0 x pOut=1

= 1/2 x 1/2 = 1/4

α0→1 = 1/4

If inputs switch in every cycle

EE14125

EECS141 25Lecture #22

Power Consumption of Dynamic GatesPower Consumption of Dynamic Gates

In1

In2 PDNIn3

Me

Mp

CLK

CLKOut

CL

Power only dissipated when previous Out = 0

EE14126

EECS141 26Lecture #22

Dynamic Power Consumption is Dynamic Power Consumption is Data DependentData Dependent

011001010100

OutBA

Dynamic 2-input NOR Gate

Assume signal probabilitiesPA=1 = 1/2PB=1 = 1/2

Then transition probabilityP0→1 = Pout=0 x Pout=1

= 3/4 x 1 = 3/4

Switching activity always higher in dynamic gates!P0→1 = Pout=0

EE14127

EECS141 27Lecture #22

Vdd

I

I

Vdd

IN

INB

OUTB OUT

Guaranteed transition for every operation!

α0->1 = 1

DualDual--Rail DominoRail Domino

EE14128

EECS141 28Lecture #22

ClockClock

Always switchesOften consumes 25-50% of total powerClock gating commonly employed

EE14129

EECS141 29Lecture #22

Problem: Problem: ReconvergentReconvergent FanoutFanout

A

B

X

Z

Reconvergence

P(Z = 1) = P(B = 1) . P(X = 1 | B=1)

Becomes complex and intractable fast

EE14130

EECS141 30Lecture #22

InterInter--Signal CorrelationsSignal Correlations

Logic withoutreconvergent fanout

Logic with reconvergent fanout

A

BZ

CA

Z

C

B

p0→1 = (1 – pApB) pApBP(Z = 1) = p(C=1 | B=1) p(B=1)

p0→1 = 0

Need to use conditional probabilities to model inter-signal correlationsCAD tools best for performing such analysis

EE14131

EECS141 31Lecture #22

GlitchingGlitching in Static CMOSin Static CMOSA

B

X

CZ

ABC 101 000

X

Z

Gate DelayAlso known as

dynamic hazards

The result is correct,but there is extra power dissipated

EE14132

EECS141 32Lecture #22

Example: Chain of NAND GatesExample: Chain of NAND Gates1

Out1 Out2 Out3 Out4 Out5

0 200 400 6000.0

1.0

2.0

3.0

Time (ps)

Volta

ge (V

)

Out8Out6

Out2

Out6

Out1

Out3

Out7

Out5

EE14133

EECS141 33Lecture #22

Principles for Power ReductionPrinciples for Power ReductionMost important idea: reduce wasteExamples:

Don’t switch capacitors you don’t need to– Clock gating, glitch elimination, logic re-structuring

Don’t run circuits faster than needed– Power α VDD

2 – can save a lot by reducing supply for circuits that don’t need to be as fast

– Parallelism falls into this category

Let’s say we do a good job of that – then what?

EE14134

EECS141 34Lecture #22

Energy Energy –– Performance SpacePerformance Space

Plot all possible designs on a 2-D planeNo matter what you do, can never get below/to the right of the solid line

This line is called “Pareto Optimal Curve”Usually (always) follows law of diminishing returns

Performance

Ener

gy/o

p

EE14135

EECS141 35Lecture #22

Optimization PerspectiveOptimization Perspective

Instead of metrics like EDP, this curve often provides information more directly

Ex1:What is minimum energy for XX performance?Ex2: Over what range of performance is a new technique (dotted line) actually beneficial?

Performance

Ener

gy/o

p

EE14136

EECS141 36Lecture #22

Key ObservationKey ObservationDefine the Energy/Performance sensitivity of parameter, for example:

At optimal point, sensitivities to all parameters should be the same (ignoring constraints)

Must equal slope of the Pareto optimal curveOtherwise, could trade one parameter for another and end up with lower energy at same performance

T

TV

T

Energy VSPerf V

∂ ∂=∂ ∂DD

DDV

DD

Energy VSPerf V

∂ ∂=∂ ∂

EE14137

EECS141 37Lecture #22

Why This Matters for Circuit DesignWhy This Matters for Circuit DesignDon’t really believe that should use sensitivities to calculate how to design your circuits:

Except for simple examples, CAD tools much better at doing that

Real reasons:Sensitivities tell you where in the space you are sitting:

– High sensitivity – pushing performance hard– Low sensitivity – not much lower energy to be gotten

Get rough idea of what happens if changed operating point by a small amountMore in EE241

EE14138

EECS141 38Lecture #22

Next LectureNext Lecture

Sequential logic