On Robustness of EDA Tools

Abstract

It is known that EDA tools produce results of different quality dependent on seemingly neutral details in the input. We bring further results in this direction, which show that the differences can impair any quantitative comparisons of the tools. To gain qualitative insight, we present a stochastic model of result quality based on Gaussian Mixtures. We show on three case studies how these models help to evaluate and improve EDA algorithms.