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IOSR Journal of Computer Engineering (IOSR-JCE)

e-ISSN: 2278-0661, p- ISSN: 2278-8727Volume 16, Issue 2, Ver. I (Mar-Apr. 2014), PP 14-19 14 | Page

An Enhanced Area Reduction Technique for Integrated Circuit
using Genetic Algorithm

Mr. Amarjit Singh, Ms. Harshit
AP, EIE (IET Bhaddal Technical Campus)
AP, ECE (DIET, Kharar)

Abstract: Genetic algorithms are implemented as a computer simulation in which a population of abstract
representations (called chromosomes or the genotype or the genome) of candidate solutions (called individuals,
creatures, or phenotypes) to an optimization problem evolves toward better solutions. In this paper Genetic
Algorithm has been implemented to reduce the area for integrated circuits. Different operators have been
implemented along with the proposed algorithm for area reduction of integrated circuits. It has been found that
the proposed algorithm gave better results as compared to other operators in order to minimize the area.
Genetic algorithm with proposed operator uses the least CPU time and the least number of iterations to reduce
the area of integrated circuit.
Keywords: Genetic Algorithm, Integrated Chip

I. Introduction
creatures, or phenotypes) to an optimization problem evolves toward better solutions. Traditionally, solutions
are represented in binary as strings of 0s and 1s, but other encodings are also possible. The evolution usually
starts from a population of randomly generated individuals and happens in generations. In each generation, the
fitness of every individual in the population is evaluated, multiple individuals are selected from the current
population based on their fitness, and modified (recombined and possibly randomly mutated) to form a new
population. The new population is then used in the next iteration of the algorithm. Commonly, the algorithm
terminates when either a maximum number of generations has been produced, or a satisfactory fitness level has
been reached for the population. If the algorithm has terminated due to a maximum number of generations, a
satisfactory solution may or may not have been reached [8].
This Paper describes the implementation of the Genetic Algorithm for the components placement. The
major concerns are chip area minimization and interconnection wire length minimization. Unlike the other
placement algorithm that applies transformations on the physical layout; the genetic algorithm applies
transformation on the chromosomal representation of the physical layout. The algorithm works on a set of
configurations constituting a constant size population. The transformations are performed through crossover
operators that generate new configurations assimilating the characteristics of the pair of configurations existing
in the current population. Mutation and Inversion operators are also used to increase the diversity of the
population, and avoid convergence at local optima. Due to the simultaneous optimization of a large population
of configurations, there is a logical concurrency in the search of the solution space which makes genetic
Algorithm an extremely efficient optimizer. Three efficient crossover techniques will be compared, and the
parameters, crossover rate have been optimized for the components placement problem.
Genetic algorithm is a powerful optimization algorithm, which starts with an initial set of random
configurations and uses a process similar to biological evolution to improve upon them. The set of
configurations the genetic algorithm operates upon is called the population. Each individual in the population is
a string of symbols, usually but not necessarily a binary bit string representing a solution to the optimization
problem. I will use alphabet symbols for the standard components placement problem. During each iteration
called a generariorz, the individuals in the current population are evaluated using some measure of fitness.
Based on this value, individuals are selected from the population two at a time as parents. The fitter individuals
have a higher probability of being selected. A number of genetic operators are applied to the parents to generate
new individuals, called offspring, by combining the features of both parents. The three genetic operators
commonly used by the genetic algorithm are crossover, mutation, and inversion, which are derived by analogy
from the biological process of evolution. The offspring are next evaluated, and a new generation is formed by
selecting some of the parents and offspring and rejecting others so as to keep the population size constant [8] .

An Enhanced Area Reduction Technique for Integrated Circuit using Genetic Algorithm 15 | Page
I ntroduction to I ntegrated circuit
made much smaller than a discrete circuit made from independent components. Integrated circuits are used in
possible by the low cost of producing integrated circuits.ICs can be made very compact, having up to several
conducting line in a circuit (the line width) can be made smaller and smaller as the technology advances; in
2008 it dropped below 100 nanometers and many manufacturers have been attempting to reduce it to tens of
nanometers. ICs were made possible by experimental discoveries showing that semiconductor devices could
of standardized Integrated Circuits in place of designs using discrete transistors. There are two main advantages
less material is needed to construct a packaged IC die than to construct a discrete circuit. Performance is also
few square millimeters to around 450 mm
, with up to 9 million transistors per Mm square.

II. Literature Review
Goldberg, Genetic algorithms (GAS) are biologically inspired search heuristics that have been applied to a wide
variety of problems in science, engineering, business and other fields. Genetic algorithms start with a population
of randomly (or heuristically) generated candidate points in the search space. Each candidate solution is coded
(following some predetermined encoding scheme) to rep resent some underlying parameter set. (Binary coding
is one of the most popular encoding strategies.) The algorithm operates in a number of iterations, in an attempt
to improve upon the trial solutions. In each iteration (an iteration is called a "generation" in the GA parlance),
several probabilistic operators are applied to the trial solutions with a view to creating (possibly) better
solutions. The algorithm terminates when either an optimal / near-optimal solution has been found or a specified
number of generations have been completed. It is to be noted that the genetic algorithm is a "weak" method,
with no guarantee of finding the optimum solution n a particular run [4, 5].
Shahookar, K.; Mazumder, P, The genetic algorithm that was originally invented by Holland for adaptive
searching in AI has been employed in this research for the placement of standard cells. Unlike simulated
annealing that uses pair wise exchange for evolving a new configuration from the current one, the genetic
algorithm uses three powerful operators to guide its search through the solution space concurrently, by
considering a set of configurations at a time. The placement problem is represented in the form of a genetic
code, which is progressively refined, and improved by the operators. This is a major deviation from the
conventional placement algorithms that directly apply the transformations to the physical layout. However, this
feature of the genetic algorithm is also a potential problem, and unless clever representation is devised, the
algorithm may prove inefficient. In this research, the standard components placement problem has been
represented as an effective genetic code, and three powerful crossover operators have been applied to generate
new configurations. Two other genetic operators- inversion and mutation-have also been applied to obtain a
highly efficient placement algorithm which requires less iteration to converge to a high quality placement. This
has been made possible by applying a Meta genetic process which optimizes the controlling parameters of the
genetic algorithm. At the same time we are also experimenting with ways to further improve the search
efficiency of the genetic algorithm by improving the algorithm used for crossover, and by varying crossover and
mutation rates during the optimization. With these improvements we expect to speedup the algorithm
considerably. We are also developing an efficient genetic representation of the standard cell routing problem.
The overall objective will be to develop theoretical understanding of genetic algorithms for CAD applications
and specifically to design a highly efficient tool for standard cell placement and routing [8].
Marcus Hutter and Shane Legg, In evolutionary algorithms, the fitness of a population increases with time by
mutating and recombining individuals and by a biased selection of fitter individuals. The right selection pressure
is critical in ensuring sufficient optimization progress on the one hand and in preserving genetic diversity to be
able to escape from local optima on the other hand. Motivated by a universal similarity relation on the
individuals, we propose a new selection scheme, which is uniform in the fitness values. It generates selection
pressure toward sparsely populated fitness regions, not necessarily toward higher fitness, as is the case for all
An Enhanced Area Reduction Technique for Integrated Circuit using Genetic Algorithm 16 | Page
other selection schemes. We show analytically on a simple example that the new selection scheme can be much
more effective than standard selection schemes. We also propose a new deletion scheme which achieves a
similar result via deletion and show how such a scheme preserves genetic diversity more effectively than
standard approaches. We compare the performance of the new schemes to tournament selection and random
deletion on an artificial deceptive problem and a range of NP hard problems: traveling salesman, set covering,
and satisfiable [7].
GADO explores the space of all possible configurations (of a set of building blocks) given only a behavioral
description of the circuit. The search space includes all possible electrical connectivity and layout and was
accomplished in 3 hours on a single processor. The same result may be obtained in a fraction of the time by
adding multiple processors since genetic algorithms are easily implemented on parallel architectures or a
network of workstations [1,2,3].Optimization occurs across placement, routing and compaction phases and the
GA converges on the simplest design that satisfies all constraints as well as the optimization criterion.
Ray-I Gang and Pei-Yung Hsiao describes a new self-organizing map called force directed self-organizing
map (FDSOM) which can be used in VLSI cell placement with various constraints on their connection and
dimension such that the total wire length and area of the resulting placement are minimized. This procedure
combines ideas from a force directed relaxation and the self organization algorithm proposed by Kohonen. It is
specially suited for such a self-organization problem that those (input) sample vectors arc not easily available.
With this property, it CM therefore be used in CAM or any other computational task. All these processes arc
convergent in a reasonable number of iterations. It was found that the proposed approach is competitive with the
state-of-the-art algorithms and uses fewer nodes and connection weights [6].

III. Problem Formulation
The design of electronic device is a process of making consecutive specification, functional design, physical
design, and fabrication. The layout defines the positions of the components.
As every fourteen to twenty-four months technology is being doubled. So there is requirement of high
rate of expansion of electronic circuits. Which leads to change in chip design?
The demand for smaller, faster, and lower cost electronics miniaturization integrated circuit increases
Chip Area Minimization leads to minimization inter connection wire length so that components are
placed optimally with respect to each other.
For the compact devices minimization of inter connection wire length is needed to place components
optimally on chip.

IV. Objectives
The major objectives are:-
Insertion of components and there details as width, length and area.
Implementation of existing Genetic Algorithms crossover operator examples (PMX, Order, Cyclic)
Implementation of proposed crossover operator
Comparison of result obtained from existing crossover operators and proposed on bases of Parameters
such as CPU time, Area, Number of iteration

V. Proposed Methodology
Genetic algorithms are a class of search techniques inspired from the biological process of evolution by
means of natural selection. GA is an iterative procedure that consists of a constant-size population of
individuals, each one represented by a finite string of symbols, known as the genome, encoding a possible
solution in a given problem space. This space, referred to as the search space, comprises all possible solutions to
the problem at hand. Generally speaking, the genetic algorithm is applied to spaces w hich are too large to be
exhaustively searched
An Enhanced Area Reduction Technique for Integrated Circuit using Genetic Algorithm 17 | Page

Figure:1 Genetic Optimization Algorithms
Steps to proceed for Genetic Algorithm:
1. Initial generation of population.
2. Selection of population for reproduction based on Fitness Function.
3. Creation of new population by using Crossover and mutation.
4. Evaluation of new population.
5. Replacement of old population by new generation.

VI. Results And Discussions
The analysis has been done for three different values of number of population and number of components. The
results for four operators with respect to area of chip, number of iterations and CPU time are as follow:
For Population = 2100 and number of components = 132

Figure:2 Area of Chip vs. Operators
Figure:3 No. of Iteration vs. Operators
An Enhanced Area Reduction Technique for Integrated Circuit using Genetic Algorithm 18 | Page

Figure:4 CPU Time vs. Operators
These results can be tabulated as shown in table1. Similarly the results for population = 200, components =80
and for population = 10000 and component = 50 are tabulated as shown in table 2&3 respectively.
Here we can see that the proposed algorithm is far much better as compare to other operators, as far as area of
chip, no. of iterations and CPU time is concerned. This algorithm provides the least area of chip along with least
no. of iterations and also the least CPU time for processing, as analyzed in every case.

Table1. Results for population=2100 and components=132.

Table2. Results for population=200 and components=80.

Table3. Results for population=10000 and components=50.

VII. Conclusion
In this paper Genetic Algorithm is used for reducing the area of integrated chip by proposed cross-over
operator. We can conclude that, if the circuit configuration is pre-determined the software can optimize the
device sizes in order meet a vector of objectives. However this is a general method and can be used for any
analog circuit.

Area of Chip
No. of
CPU Time
Ordered 124 1924 238576 41000 412
PMX 124 1859 230516 41000 296
Cyclic 130 1765 229450 40800 307
Proposed 104 1765 183560 40600 289
No. of Populations=2100
No. of Components=132
Area of Chip
No. of
CPU Time
Ordered 121 1159 140239 4010000 295
PMX 121 1147 138787 4010000 247
Cyclic 130 1097 142610 4008000 284
Proposed 92 1097 100924 4006000 207
No. of Populations=200
No. of Components=80
Area of Chip
No. of
CPU Time
Ordered 104 603 62712 100050000 115
PMX 104 628 65312 100050000 124
Cyclic 130 664 86320 100040000 117
Proposed 92 603 55476 100030000 110
No. of Populations=10000
No. of Components=50
An Enhanced Area Reduction Technique for Integrated Circuit using Genetic Algorithm 19 | Page
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