A new a priori net length estimation technique for integrated circuits using radial basis functions

Abstract

Placement is a stage in the design of digital circuits where the locations of the circuit components are determined, while minimizing the total length of wires connecting them. A priori individual length estimates can be used to improve the quality of a placement solution. However, finding such estimates is a daunting task. A technique based on Radial Basis Functions (RBFs) is developed in this paper. Unlike polynomials, the RBFs provide flexible basis elements with only local support, which greatly enhances both their robustness and their ability to fit highly non-linear data sets. Today’s placement problems deal with a very large number of components making it impossible to apply traditional RBF modeling techniques. Thus specialized methods for determining the RBF centers and shape parameters are developed. The proposed technique is tested on popular benchmark circuits, and shows improvements of up to 24% over the best existing model for mixed-size circuits.