Neural clustering algorithms for classification and pre-placement of VLSI cells

Abstract

The authors present modifications to Kohonen autoassociative maps to increase their efficiency for clustering and decrease their sensitivity to initial conditions. A new update rule is described for the classification for similarity. Some test results are presented for comparison between different algorithms. The new neural network algorithm was applied to the problem of preplacement of VLSI cells with improvement in the quality of the solution and computational time.<>