A novel approach to the placement and routing problems for field programmable gate arrays

This paper presents an artificial neural network (ANN) based parallel evolutionary solution to the placement and routing problems for field programmable gate arrays (FPGAs). The concepts of artificial neural networks are utilized for guiding the parallel genetic algorithm to intelligently transform a set of initial populations of randomly generated solutions to a final set of populations that contain solutions approximating the optimal one. The fundamental concept of this paper lies in capturing the various intuitive strategies of the human brain into neural networks, which may help the genetic algorithm to evolve its population in a more lucrative manner. A carefully chosen fitness function acts in the capacity of a yardstick to appraise the quality of each “chromosome” to aid the selection phase. In conjunction with the migration phase and the chosen fitness function various genetic operators are employed, to expedite the transformation of the initial population towards the final solution. The suggested algorithms have been implemented on a 12-node SGI Origin-2000 platform using the message passing interface (MPI) standard and the neural network utilities provided by MAT Lab software. The results obtained by executing the same are extremely encouraging, especially for circuits with very large number of nets.