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- Khushro Shahookar, Pinaki Mazumder
- ACM Comput. Surv.
- 1991

VLSI cell placement problem is known to be NP complete. A wide repertoire of heuristic algorithms exists in the literature for efficiently arranging the logic cells on a VLSI chip. The objective of this paper is to present a comprehensive survey of the various cell placement techniques, with emphasis on standard cell and macro placement. Five major… (More)

- Khushro Shahookar, Pinaki Mazumder
- IEEE Trans. on CAD of Integrated Circuits and…
- 1990

representation of the physical layout. The algor i thm works on a set o f configurations constituting a constant size population. The transformations are performed through crossover operators that generate a new configuration assimilating the characteristics o f a pair o f configurations existing in the current population (similar to biological… (More)

- Heming Chan, Pinaki Mazumder, Khushro Shahookar
- Integration
- 1991

The genetic algorithm has been applied to the VLSI module placement problem. This algorithm is an iterative, evolutional approach. A placement configuration is represented by a set of primitive features such as location and orientation, and the features are arranged in the form of a two-dimensional bitmap chromosome. The representation is flexible, and can… (More)

- Khushro Shahookar, Pinaki Mazumder
- EURO-DAC
- 1990

This paper describes the implementation of the Genetic Algorithm for Standard-cell <b>P</b>lacement, <b>GASP-1</b>. As opposed to simulated annealing, which normally uses pairwise interchange for transforming the layout configuration, in a genetic algorithm, the <i>crossover operator</i> is used to combine two current configurations to generate a new… (More)

- Khushro Shahookar, W. Khamisani, Pinaki Mazumder, Sudhakar M. Reddy
- VLSI Design
- 1993

- Khushro Shahookar, Pinaki Mazumder
- VLSI Design
- 1995

- K. Shahookar, P. Mazurnder, S. M. Reddy
- 2004

The paper presents a novel implementation of the genetic algorithm in combination with beam search for gate matrix layout. This is a permutation problem, for which the traditional genetic crossover operator results in repetition of gates, and therefore the GA is not applicable without modification. However, the GA is a very efficient stochastic optimisation… (More)

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