Application of fuzzy inference and genetic algorithms to VLSI floorplanning design

VLSI floorplanning design automation based on soft computing is discussed. Authors have proposed the fusion of fuzzy inference and genetic algorithms for the automation of VLSI floorplanning design. In this paper, the expansion of inference is presented. The fuzzy rules are used to infer the initial position of the on-blocks based on analysis of the accumulated knowledge of the expert design engineer. Only the dominant combinations of place and block are inferred. In the authors' previous work (1998, 1999), blocks deemed suitable candidates for placement at the center, relative to the four corners and side of the chip, are inferred. In addition to those inference, such blocks that are relatively appropriate to be placed along with the four perimeters (edges) of active area of the chip. These inferences are then reflected in the initial population of the genetic algorithms. The rest of the block placement phase is entrusted to the genetic algorithms. Experimental software to implement the proposed approach was developed. The results of the experiments showed a level and quality of placement close to that of the expert design engineer.