This document provides an introduction to VLSI physical design automation and partitioning. It discusses the importance of partitioning large circuits into smaller subcircuits for manageable design. The objectives of partitioning are to minimize the number of partitions and interconnections between partitions. Common partitioning algorithms discussed include min-cut bipartitioning, Kernighan-Lin iterative improvement algorithm, and other methods like ratio cut, genetic algorithms, and simulated annealing. Partitioning is an essential step in the physical design flow and impacts circuit performance and layout costs.

1. VLSI Physical Design Automation
Introduction , partitioning
Sushil kundu
Roll No:20084056
Registration No: 1954
Dept. of Applied Electronics
& Instrumentation Engg.,
University Institute of
Technology burdwan

2. Intended Audience
• VLSI CAD (also known as EDA – electronic design
automation) students, in particular for chip implementation
(physical design)
• Circuit designers to understand how tools work behind
the scene
• Process engineers to tune process that is more
circuit/physical design friendly
• Mathematical/Computer Science majors who want to find
tough problems to solve
– Lots of VLSI physical design problems can be
formulated into combinatorial optimization or
mathematical programming problems.
– Actually, most CAD problems are NP-complete ->
heuristics

3. Objective of this Lecture
To review the materials used in fabrication of VLSI devices.
To review the structure of devices and process involved in
fabricating different types of VLSI circuits.
To review the basic algorithm concepts.
 Understand the process of VLSI layout design.
 Study the basic algorithms used in layout design of VLSI
circuits.
 Learn about the physical design automation techniques used
in the best-known academic and commercial layout systems.

4. Physical Design
• Converts a circuit description into a geometric
description.
– This description is used for fabrication of the chip.
• Basic steps in the physical design cycle:
1. Partitioning
2. Floorplanning
3. placement
4. Routing
5. Compaction

5. So, what is Partitioning?
System Level Partitioning System
PCBs
Board Level Partitioning
Chips
Chip Level Partitioning
Subcircuits
/ Blocks
6

6. Why partition ?
• Ask Lord Curzon 
– The most effective way to solve problems of high
complexity : Parallel CAD Development
• System-level partitioning for multi-chip designs
– Inter-chip interconnection delay dominates system
performance
• IO Pin Limitation
• In deep-submicron designs, partitioning defines local
and global interconnect, and has significant impact on
circuit performance
7

7. Importance of Circuit Partitioning
 Divide-and-conquer methodology
 The most effective way to solve problems of high complexity
E.g.: min-cut based placement, partitioning-based test
generation,…
 System-level partitioning for multi-chip designs
inter-chip interconnection delay dominates system
performance.
 Circuit emulation/parallel simulation
partition large circuit into multiple FPGAs (e.g. Quickturn), or
multiple special-purpose processors (e.g. Zycad).
 Parallel CAD development
Task decomposition and load balancing
 In deep-submicron designs, partitioning defines local and
global interconnect, and has significant impact on circuit
performance
…… ……

8. Objectives
• Since each partition can correspond to a chip, interesting
objectives are:
– Minimum number of partitions
• Subject to maximum size (area) of each partition
– Minimum number of interconnections between partitions
• Since they correspond to off-chip wiring with more
delay and less reliability
• Less pin count on ICs (larger IO pins, much higher
packaging cost)
– Balanced partitioning given bound for area of each
partition
9

9. Partitioning:
 Partitioning is the task of dividing a circuit into smaller parts .
The objective is to partition the circuit into parts, so that the size
of each component is within prescribed ranges and the number of
connections between the components is minimized .
 Different ways to partition correspond to different circuit
implementations . Therefore, a good partitioning can significantly
improve circuit performance and reduce layout costs .
• Decomposition of a complex system into smaller subsystems
– Done hierarchically
– Partitioning done until each subsystem has manageable size
– Each subsystem can be designed independently
• Interconnections between partitions minimized
– Less hassle interfacing the subsystems
– Communication between subsystems usually costly

10. Partitioning of a Circuit
Input size: 48
Cut 1=4 Cut 2=4
Size 1=15 Size 2=16 Size 3=17

11. Hierarcahical Partitioning
• Levels of partitioning:
– System-level partitioning:
Each sub-system can be designed as a single PCB
– Board-level partitioning:
Circuit assigned to a PCB is partitioned into sub-circuits
each fabricated as a VLSI chip
– Chip-level partitioning:
Circuit assigned to the chip is divided into manageable sub-
circuits
NOTE: physically not necessary

12. Delay at Different Levels of Partitions
x
A
B
D
C
10x
PCB1 PCB2
20x
13

13. Partitioning: Formal Definition
• Input:
– Graph or hypergraph
– Usually with vertex weights
– Usually weighted edges
• Constraints
– Number of partitions (K-way partitioning)
– Maximum capacity of each partition
OR
maximum allowable difference between partitions
• Objective
– Assign nodes to partitions subject to constraints
s.t. the cutsize is minimized
• Tractability
- Is NP-complete 
14

14. Circuit Representation
• Netlist: B
– Gates: A, B, C, D
A
– Nets: {A,B,C}, {B,D}, {C,D}
C D
• Hypergraph:
– Vertices: A, B, C, D
– Hyperedges: {A,B,C}, {B,D}, {C,D}
B
– Vertex label: Gate size/area
A
– Hyperedge label:
Importance of net (weight)
C D

15. Circuit Partitioning: Formulation
Bi-partitioning formulation:
Minimize interconnections between partitions
c(X,X’)
X X’
• Minimum cut: min c(x, x’)
• minimum bisection: min c(x, x’) with |x|= |x’|
• minimum ratio-cut: min c(x, x’) / |x||x’|
16

16. A Bi-Partitioning Example
a c 100 e
100 100
100 100
9
min-cut
4
b 10 d 100 f
mini-ratio-cut min-bisection
Min-cut size=13
Min-Bisection size = 300
Min-ratio-cut size= 19
Ratio-cut helps to identify natural clusters
17

17. Iterative Partitioning Algorithms
• Greedy iterative improvement method (Deterministic)
– [Kernighan-Lin 1970]
• Simulated Annealing (Non-Deterministic)
18

18. Restricted Partition Problem
• Restrictions:
– For Bisectioning of circuit
– Assume all gates are of the same size
– Works only for 2-terminal nets
• If all nets are 2-terminal, hypergraph  graph
b b
a a
c d c d
Hypergraph Graph
Representation Representation
19

19. Problem Formulation
• Input: A graph with
– Set vertices V (|V| = 2n)
– Set of edges E (|E| = m)
– Cost cAB for each edge {A, B} in E
• Output: 2 partitions X & Y such that
– Total cost of edge cuts is minimized
– Each partition has n vertices
• This problem is NP-Complete!!!!!
20

20. A Trivial Approach
• Try all possible bisections and find the best one
• If there are 2n vertices,
# of possibilities = (2n)! / n!2 = nO(n)
• For 4 vertices (a,b,c,d), 3 possibilities
1. X={a,b} & Y={c,d}
2. X={a,c} & Y={b,d}
3. X={a,d} & Y={b,c}
• For 100 vertices, 5x1028 possibilities
• Need 1.59x1013 years if one can try 100M
possbilities per second
21

21. Definitions
• Definition 1: Consider any node a in block X. The
contribution of node a to the cutset is called the external cost
of a and is denoted as Ea, where
Ea =Σcav (for all v in Y)
• Definition 2: The internal cost Ia of node a in X is defined
as follows:
Ia =Σcav (for all v in X)

22. Example
• External cost (connection) Ea = 2
• Internal cost Ia = 1
X b
Y
c
a d

23. Idea of KL Algorithm
• Da = Decrease in cut value if moving a = Ea-Ia
– Moving node a from block X to block Y would decrease the
value of the cutset by Ea and increase it by Ia
X b
Y X b
Y
c
c
a d a d
Da = 2-1 = 1
Db = 1-1 = 0

24. Idea of KL Algorithm
• Note that we want to balance two partitions
• If switch A & B, gain(A,B) = DA+DB-2cAB
– cAB : edge cost for AB
X B
Y X B Y
C C
D
A A D
gain(A,B) = 1+0-2 = -1

25. Idea of KL Algorithm
• Start with any initial legal partitions X and Y
• A pass (exchanging each vertex exactly once) is described
below:
1. For i := 1 to n do
From the unlocked (unexchanged) vertices,
choose a pair (A,B) s.t. gain(A,B) is largest
Exchange A and B. Lock A and B.
Let gi = gain(A,B)
2. Find the k s.t. G=g1+...+gk is maximized
3. Switch the first k pairs
• Repeat the pass until there is no
improvement (G=0)

26. Example
X Y X Y
1 4 4 1
2 5
2 5
3 6 3 6
Original Cut Value = 9 Optimal Cut Value = 5
A good step-by-step example in SY book

27. Time Complexity of KL
• For each pass,
– O(n2) time to find the best pair to exchange.
– n pairs exchanged.
– Total time is O(n3) per pass.
• Better implementation can get O(n2log n) time per pass.
• Number of passes is usually small.

28. Recap of Kernighan-Lin’s Algorithm
 Pair-wise exchange of nodes to reduce cut size
 Allow cut size to increase temporarily within a
pass
Compute the gain of a swap
Repeat Perform a feasible swap of max gain
Mark swapped nodes “locked”; u v a
Update swap gains;
Until no feasible swap; v u
Find max prefix partial sum in gain sequence g1,
locked
g2, …, gm
Make corresponding swaps permanent.
 Start another pass if current pass reduces the
cut size
(usually converge after a few passes)

29. Other Partitioning Methods
• KL and FM have each held up very well
• Min-cut / max-flow algorithms
– Ford-Fulkerson – for unconstrained
partitions
• Ratio cut
• Genetic algorithm
• Simulated annealing

30. References and Copyright
 Textbooks referred (none required)
 [Mic94] G. De Micheli
“Synthesis and Optimization of Digital Circuits”
McGraw-Hill, 1994.
 [CLR90] T. H. Cormen, C. E. Leiserson, R. L. Rivest
“Introduction to Algorithms”
MIT Press, 1990.
 [Sar96] M. Sarrafzadeh, C. K. Wong
“An Introduction to VLSI Physical Design”
McGraw-Hill, 1996.
 [She99] N. Sherwani
“Algorithms For VLSI Physical Design Automation”
Kluwer Academic Publishers, 3rd edition, 1999.

31. THANK YOU