prof lei he ucla [email protected] ee 201c modeling of vlsi circuits and systems
TRANSCRIPT
Prof Lei He
UCLA
EE 201C Modeling of VLSI Circuits and Systems
EE 201C Modeling of VLSI Circuits and Systems
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Chapter 5 Signal and Power IntegrityChapter 5 Signal and Power Integrity
On-chip signal integrityRC and RLC coupling noise
Power integrity
Static noise: IR dropDynamic noise: L di/dt noise
Chapter 6: Beyond die noiseIn-package decap insertionLow frequency P/G resonanceNoise for High-speed signaling
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Reading on Signal IntegrityReading on Signal Integrity
RC couplingJ. Cong, Z. Pan and P. V. Srinivas, "Improved Crosstalk
Modeling for Noise Constrained Interconnect Optimization", ASPDAC, 2001.
RLC couplingJun Chen and Lei He, "Worst-Case Crosstalk Noise for
Non-Switching Victims in High-speed Buses", IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, Volume 24, Issue 8, Aug. 2005.
To be covered by student presentation on May 14
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Reading on Power IntegrityReading on Power Integrity
Static noise: IR drop S. Tan and R. Shi, “Optimization of VLSI Power/Ground (P/G)
Networks Via Sequence of Linear Programmings”, DAC’09
Dynamic noise: L di/dt noise Yiyu Shi, Jinjun Xiong, Chunchen Liu and Lei He, "Efficient
Decoupling Capacitance Budgeting Considering Current Correlation Including Process Variation", ICCAD, San Jose, CA, Nov. 2007.
Supplementary reading: H. Qian, S. R. Nassif, and S. S. Sapatnekar, “Power Grid
Analysis Using Random Walks,” IEEE Trans. on CAD, 2005. Yiyu Shi, Wei Yao, Jinjun Xiong, and Lei He, "Incremental and
On-demand Random Walk for Iterative Power Distribution Network Analysis", ASPDAC 2009
DAC 2009 Best Paper AwardSlides provided by X.D. Tan
Xiang-Dong Tan* and C.-J. Richard Shi
Optimization of VLSI Power/Ground (P/G) Networks Via Sequence of Linear
Programmings
Optimization of VLSI Power/Ground (P/G) Networks Via Sequence of Linear
Programmings
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Outline of PresentationOutline of Presentation
Introduction and motivation
Review of existing algorithms
Relaxed P/G optimization procedure
New P/G optimization algorithm
Experimental results
Summary and future work
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IntroductionIntroduction
...
...
Pad
IR drops:
Voltage difference between power supply pads and individual cell instances.
Electro-migration:
Metal ion mass transport along the grain boundaries when a metallic interconnect is stressed at high current density. Mean Time to Failure (MTF) (Black’s equation):MTF A w l J E kTa ( , ) exp( / )2
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IntroductionIntroduction
A real industrial chip
#cell instances: 0.5M
#P/G resistors: 0.6M
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IntroductionIntroduction
Unrestricted IR drops and current densities in power / ground (P/G) network will cause malfunction and reliability problems in deep sub-micron IC chips. Increased cell delays (timing problem) increased resistance and even opens of P/G wires
Most of P/G designs are done manually.An aggressive design will cause more design iterations and
thus lead to increased design costs. Over conservative P/G network design wastes a lot of
important chip areas.
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MotivationMotivation
Two steps in P/G network design:P/G network construction (P/G routing).Determination of wire segment widths.
Determination of wire segment widths is hard to solve. The problem of determining wire segment widths in a P/G
network subject to reliability constraints is a constrained non-linear optimization problem.
Existing methods are not very efficient.based on the constrained nonlinear programming, can not handle large industrial P/G networks containing
millions of wire segments.
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Outline of PresentationOutline of Presentation
Introduction and motivation
Review of existing algorithms
Relaxed P/G optimization procedure
New P/G optimization algorithm
Experimental results
Summary and future work
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Algorithm ReviewAlgorithm Review
Assumption:Currents (average or maximum) of each individual cell
instance are known a priori before the optimization. computed by using power models of cells in a design
Existing optimization methods differ in the selection of variables.
Ohm’s Law
RV V
I
l
wii i
i
i
i
1 2 wi
li
Vi1 Vi2I i
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Problem FormulationProblem Formulation
Min-area objective function: (Problem P)
IR drop constraints:
Electro-migration constraints:
Minimum width constraints:
f l wI l
V Vi ii B
i i
i ii B
( , )w V, I
2
1 2
V V V Vi i min maxor
| | | |I w V V li i i i i 1 2
wl I
V Vwi
i i
i i
1 2
min
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Algorithm ReviewAlgorithm Review
Resistance values and branch currents are variables (Chowdhury and Breuer’87)
Both objective function and IR drop constraints are nonlinear
Solution: augmented Lagrangian method
Resistance values are variables (Dutta and Marek-Sadowska’89)
All the constraints are nonlinear Solution: feasible direction method
Nodal voltage and branch current are variables (Chowdhury’89)
Only objective function is nonlinear Solution: linear programming & conjugate gradient
Topology construction (Mitsuhashi and Kuh’92)
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Outline of PresentationOutline of Presentation
Introduction and motivation
Review of existing algorithms
Relaxed P/G optimization procedure
New P/G optimization algorithm
Experimental results
Summary and future work
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Relaxed P/G Optimization AlgorithmRelaxed P/G Optimization Algorithm
Relaxation: current directions are fixed. Nodal voltages and branch currents can be selected as
variables and be optimized separately.
Two optimization steps to solve P (Chowdhury’89) Solve for nodal voltages under fixed branch currents
(problem P1) Solve for branch current under fixed nodal voltages
(problem P2)
Advantage: All the constraints become linear and P2 is a linear
programming problem. P1 is a convex programming problem.
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Problem P1Problem P1
Nonlinear Optimization Problem (P1)Objective function:
Subject to
fV V
i
i ii Bi( ) ,V
1 2
Iili2
V
Vi
i
V
Vmin
max
V Vi i1 2
I
l
wi
i
min
V Vi i1 2 0
I i
IR drop: Electro-migration: Minimum width:
| |V Vi i1 2 l i
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Problem P2Problem P2
Linear Optimization Problem (P2)Objective function:
Subject to
f I ii
i B
( ) ,I
li2
V Vi1 i2
Minimum width: KCL law:
l
V Vw
i1 i2min
i iI
s Ii i
i B j ( )
0
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ObservationsObservations
Solution to P1: First transform P1 into a unconstrained nonlinear
optimization problem by adding a penalty function to the objective function.
Conjugate gradient method was used to solve the unconstrained nonlinear problem.
Disadvantage:Very slow convergence (almost linear)Conjugate gradient directions may deteriorate
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Outline of PresentationOutline of Presentation
Introduction and motivation
Review of existing algorithms
Relaxed P/G optimization procedure
New P/G optimization algorithm
Experimental results
Summary and future work
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New Optimization AlgorithmNew Optimization Algorithm
Basic idea ---- linearize the nonlinear objective function in P1. Define
Linearized objective function:
v sign I V Vi i i i ( )( )1 2 0
fvi
ii B
( )| |
v
g ff
v vvi
ii B
i
ii Bi( ) ( )
( )( )
| | | |v v
v
vv v
0
00
0 02
2
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New Optimization AlgorithmNew Optimization Algorithm
The g(v) makes sense only if
Each product term, h(x) = c/x, in f(x) is a monotonic decreasing function.
g g f f( ) ( ) ( ) ( )v v v v0 0
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14-50
0
50
100
x
1/x and its first order Taylor's expansion at 0.04
h(x) = 1/x
H(x) = 1/x0 -1/(x0^2)*(x-x0)
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Two Optimization ScenariosTwo Optimization Scenarios
(1) All the branch voltage drops increase.
(2) Only some branch voltage drops increase while others decrease or stay unchanged.
Combine two scenarios, we have
f f g g( ) ( ) ( ) ( )v v v v0 0 and
We always have
v v vi i i0 02 0 1 ( ) ,
v vi i0
g g f f( ) ( ) ( ) ( )v v v v0 0
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New Optimization Problem P3New Optimization Problem P3
Minimize Linearized objective function
Subject to
g VV V V V
V Vi
i ii B
i
i ii i
i B
( )( )
( )
2
10
20
10
20 1 2
V
Vi
i
V
Vmin
max
V Vi i1 2
I
l
wi
i
min
IR drop: Electro-migration: Minimum width:
| |V Vi i1 2 l i
Extra constraint:
sign I V V sign I V Vi i i i i i( )( ) ( )( )10
20
1 2
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Sequence of Linear ProgrammingsSequence of Linear Programmings
New P/G Optimization Algorithm
1. Obtain an initial solution for a given P/G network
2. Build all the constraints for Problem P3
3. Solve P3 by sequence of linear programmings and record the result as
4. Build all the constraints based on of step (3) for the problem P2
5. Solve P2 by a linear programming and record the result as
6. Stop if improvement over previous result is small. Otherwise, goto step (2)
V , Ik k
V k 1
Ik 1
V k 1
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Theoretical ResultTheoretical Result
Theorem: There exists a so that step (3) always converges to the global minimum in the convex problem space of P1.
xmin
x0x1
x2
x3
x4
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Practical ConsiderationsPractical Considerations
Selection of At the beginning, solution space in P3 should be as
large as possible, so should be small. It should be close to 1 in later course of sequence of
linear programmings.
Numerical Stability Power networks are converted ground networks to
improve the numerical stability. Voltage drop in a ground network has to be
represented by by a power network with 5V supply voltage.
Power networks can be easily converted into ground networks.
2 5 10 5.
4 999975.
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Outline of PresentationOutline of Presentation
Introduction and motivation
Review of existing algorithms
Relaxed P/G optimization procedure
New optimization algorithm
Experimental results
Summary and future work
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Experimental ResultsExperimental Results
Sequence of linear programming and Conjugate gradient method were performed on 4 oversized p/g networks on SUN Untra-I with 169 MHz.
Ckt #node #bch New Algorithm Conjugate Gradient SP#it time rd% #it time rd%
p4x4 17 23 4 0.43 95.1 21 78.7 94.2 183.0p20x20 402 439 3 12.6 91.8 255 36147.1 90.8 2868.8p3x500 1502 1505 2 37.6 47.8 67 2135.4 26.8 >56.8g300x10 3002 3599 2 609.9 93.7 137 15192.1 78.4 >25.0
p100X100 10002 10199 4 1325.6 80.7 117 41716.8 48.9 >31.5
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Comparison in CPU timeComparison in CPU time
p4x4 p20x20 p3x500 p300x10 p100x100
01
00
00
20
00
03
00
00
40
00
05
00
00
p4x4 p20x20 p3x500 p300x10 p100x100
CPU time bynew algorithm
CPU time byCG
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Comparison in PerformanceComparison in Performance
p4x4 p20x20 3x500 g300x10 p100x1000
10
20
30
40
50
60
70
80
p4x4 p20x20 3x500 g300x10 p100x100
reduced to (%)by newalgorithm
reduced to (%)by CG
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Experimental ResultsExperimental Results
1 2 3 4 5 61
1.005
1.01
1.015
1.02
1.025
1.03
# iteration
f(x)
/f_m
in(x
)scaled cost vs #iterations for new algorithm
The cost reduction versus the number of iterations (p4x4)
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Experimental ResultsExperimental Results
0.4 0.6 0.8 13
3.5
4
4.5
5
5.5
6
6.5
7
xi
# o
f lin
ea
r p
rog
ram
min
gs
# of linear programmings vs xi
0.4 0.6 0.8 1424
426
428
430
432
434
436
438
440
442
xi
co
st
cost vs xi
Effect of on the performance of the new algorithm ( )xi
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Outline of PresentationOutline of Presentation
Introduction and motivation
Review of existing algorithms
Relaxed P/G optimization procedure
New optimization algorithm
Experimental results
Summary and future work
35
SummarySummary
A new method based sequence of linear programmings was proposed to determine the widths of P/G network segments subject to reliability constraints.
We showed theoretically that new method is capable of finding solution as good as that by the best-known method.
Experimental results demonstrated that new method is orders-of-magnitude faster than the best-known method with constantly better solution quality.