International Journal of Engineering Trends and Technology- Volume3Issue1- 2012
CIRCUIT MINIMIZATION IN VLSI USING
PSO & GA ALGORITHMS
RAJDEEP SINGH1, AMIT ARORA2, GURJIT SINGH3, JAGJIT MALHOTRA4
3
CTIT, JALANDHAR
DAVIET, JALANDHAR
Abstract—: Circuit partitioning is the more critical
more is the fitness function the better is the result of
step in the physical design of various circuit in VLSI. In
portioning. Area of each partition is also used as a
this partitioning main objective is to minimize the
constraint to reduce the fabrication cost with minimum
number of cuts. For this PSO algorithm is proposed for
area or as a balance constraint so that partitions are of
the optimization of VLSI inter connection (net list)
almost equal size.
bipartition. Meanwhile, the corresponding evaluation
Number of partitions appears as a constraint as more
function and the operators of crossover and mutation
number of partitions may ease the design but increase
are designed. The algorithm is implemented to test
the cost of fabrication and number of interconnections
various benchmark circuits. Compared with the
between partitions [6].
traditional genetic algorithm (GA) with the same
1,2,4
evaluation function and the same genetic operators
concerned the hybrid PSO and GA algorithm will give
better results.
Keywords: Partioning, Particle Swarm ptimization,
Genetic Algorithm, Hybrid Algorithm.
I. INTRODUCTION
Circuit partitioning/clustering is an important aspect
of VLSI design. It consists of dividing a circuit into
parts, each of which can be implemented as a separate
component (e.g., a chip) that satisfies certain design
constraints [1] [2]. One such constraint is the area of
the component. The limited area of a component forces
the designer to lay out a circuit on several components.
Since crossing components incurs relatively large
delay, such a partitioning could greatly degrade the
performance of a design if not done properly. There
has been a large amount of work done in the area of
circuit partitioning and clustering [3] [4]. In circuit
partitioning, the circuit is divided into two
(bi-partitioning) or more (multi-way partitioning)
parts. In circuit clustering, the circuit is built up cluster
by cluster. To partition designs of large size bottom-up
clustering is often combined with top down
partitioning. The classical objective of partitioning is
to minimize the cut-size, i.e. number of nets spanning
two or more parts [5].
The different objectives that may be satisfied by
partitioning are:
The minimization of the number of cuts: The number
of interconnections among partitions has to be
minimized. Reducing the interconnections not only
reduces the delay but also reduces the interface
between the partitions making it easier for
independent design and fabrication. It is also called the
min cut problem.
To improve the fitness function is the objective of
circuit portioning.Fitness function denotes the
improvement in the parameters of the circuit. The
ISSN: 2231-5381
II. CIRCUIT MINIMIZATION TECHNIQUES
Particle swarm optimization (PSO):
The basic idea of PSO stems from the behavior of
birds, in which each particle or bird keeps track of its
coordinates in the solution space which are associated
with the best solution that is achieved so far by that
particle is called as personal best position (p best) and
the another best value obtained so far by any particle is
called as global best position (g best). Each particle
tries to modify its position using the concept of
velocity. The salient features of PSO are:
1. PSO method is based on researches on swarms such
as fish schooling and bird flocking.
2. It is a history based algorithm such that in each step
the particles use their own behavior associated with the
previous iterations.
3. It is easy to implement. Therefore the computation
time is less.
GA (Genetic Algorithm):
All genetic algorithms work on a population or a
collection of several alternative solutions to the given
problem. Each individual in the population is called a
string or chromosome, in analogy to chromosomes in
natural systems. The population size determines the
amount of information stored by the GA. The GA
population is evolved over a number of generations.
All information required for the creation of appearance
and behavioral features of a living organism is
contained in its chromosomes. GAs are two basic
processes from evolution: inheritance, or the passing
of features from one generation to the next, and
competition, or survival of the fittest, which results in
weeding out the bad features from individuals in the
population. The objective of the GA is then to find an
optimal solution to a problem .Since GA’s are heuristic
procedures, modeled as function optimizers, they are
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International Journal of Engineering Trends and Technology- Volume3Issue1- 2012
average No.of cuts for different circuit series
14
12
10
a v e ra g e N o . o f C u t s
not guaranteed to find the optimum, but are able to find
very good solutions for a wide range of problems
[10].A GA based evolutionary approach for circuit
partitioning giving a significant improvement in result
quality. Comparative evaluation of genetic algorithm
and simulated annealing was done with genetic
algorithm giving better results [11] .A new
hyper-graph partitioning algorithm was proposed.
Hybrid PSO and GA introduction:
Hybridization of evolutionary algorithms with local
search has been investigated in many studies [11] Such
a hybrid model is often referred to as a mimetic
algorithm. Two global optimization algorithms GA
and PSO are combined. Since PSO and GA both work
with a population of solutions combining the searching
abilities of both methods seems to be a good approach.
Originally PSO works based on social adaptation of
knowledge and all individuals are considered to be of
the same generation. On the contrary, GA works based
on evolution from generation to generation so the
changes of individuals in a single generation are not
considered. Through GA & PSO have their specific
advantage have their specific advantage when solving
different algorithm, it is necessary to obtain both their
individual feature by combining the two algorithms.
The performance of algorithm is described as follow
:
8
6
4
2
average no.of cuts
0
0
10
20
30
40
Circuit Series
50
60
Table 2 Average time of iterations for different
netlist
Load the circuit net list data
Select N particles with different
velocities and positions
Apply the PSO algorithm on the
circuit
Calculate two best values of P best
Apply GA operator crossover
Calculate the final value as Global best
Calculate the minimum number of cut
Figure 1 Flowchart of Hybrid PSO_GA Algorithm
Figure 3. Average iteration time vs Number of
Nodes
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International Journal of Engineering Trends and Technology- Volume3Issue1- 2012
Table 1 Average Results of partitioning with Hybrid PS0
and Genetic Algorithms with different iterations:
Circuit
Series of
differen
t net list
SPP
N-10
Series
SPP
N-15
Series
SPP
N-20
Series
SPP
N-25
Series
SPP
N-30
Series
SPP
N-35
Series
SPP
N-40
Series
SPP
N-45
Series
SPP
N-50
Series
SPP
N-55
Series
SPP
N-60
Series
SPP
N-65
Series
Number
of
Nodes
Number
of Files
10
483
Number
of
Nodes
Number
of Files
10
483
Average
iteration
time(in
seconds)
0.000208
15
184
0.000241
20
121
0.000276
25
107
0.000146
30
52
0.000107
35
31
0.000437
40
41
0.000177
45
28
0.000200
50
24
0.000218
55
20
0.000255
8.585366
Circuit
Series of
different
net list
SPP
N-10 Series
SPP
N-15 Series
SPP
N-20 Series
SPP
N-25 Series
SPP
N-30 Series
SPP
N-35 Series
SPP
N-40 Series
SPP
N-45 Series
SPP
N-50 Series
SPP
N-55 Series
60
9
0.000276
7.964286
SPP
N-60 Series
SPP
N-65 Series
65
7
0.000304
Minimum
number
of
interconnection
s
1.471074
15
184
1.630435
20
121
3
25
107
3.990654
30
52
4.884615
35
40
45
31
5.903225806
41
28
50
24
10.91667
55
20
12.9
60
9
12.11111
65
Table 2 Average time of iterations for different netlist
7
between circuit series and average no of time for the best
of first 50 iteration.
10.71429
III. EXPERIMENTAL RESULT
The Proposed Algorithm is tested on 11 net lists to
demonstrate the effect of iteration by using hybrid PSO
and GA algorithm on partitioning. The result of
partitioning with PSO and GA is given in table 1 HereThe
the
no of particles are taken as 5. Fig 2 shows the plot between
the no of cuts and netlist series. Fig 3 shows the average
84.06
time taken by 11 net lists. The figure 3shows graph
ISSN: 2231-5381
IV CONCLUSION
In this Paper, Hybrid PSO and GA algorithm is
applied to VLSI circuit partitioning problem. By
applying the Hybrid PSO and GA algorithms, we
are getting an sum of average number of cuts 84.06
which is better by as compared to the results of GA
algorithm which was an average of 106.3. So, by
this paper, we have concluded that the Hybrid PSO
and GA method is better than GA for minimizing
the number of cuts in the different circuit series
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International Journal of Engineering Trends and Technology- Volume3Issue1- 2012
Acknowledgments
The author would like to thank Mr. Gaurav soni
(Assistant Professor) of A.C.E.T, Amritsar for
bringing this publication and for helpful comments for
on an earlier draft version.
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