More Effective Power Network Prototyping by Analytical and Centroid Learning

Abstract

Recently a prior work has been proposed to improve the power distribution network (PDN) design with some practical methodologies. However, we found that such approach will cause redundant resources, resulting in the waste of the metal application. In this paper, we present a more effective design flow to automatically generate a PDN verified by the commercial tool without IR-Drop violation. We propose an analytical model and consider the different types of macros to determine the total metal width of PDN. Moreover, the optimization is based on a centroid learning method from unsupervised learning to consolidate PDN. Our work has experimented on real designs in 65 nm process, 0.18 um generic process, and 40 nm process. The results show that our framework can satisfy the given IR-Drop constraints and simultaneously save lots of metal resource (means no overdesign).