Power Grid Reduction by Sparse Convex Optimization

Wei Ye, Meng Li, Kai Zhong, Bei Yu, David Z. Pan

Abstract: With the dramatic increase in the complexity of modern integrated circuits (ICs), direct analysis and verification of IC power distribution networks (PDNs) have become extremely computationally expensive. Various power grid reduction methods are proposed to reduce the grid size for fast verification and simulation but usually suffer from poor scalability. In this paper, we present a convex optimization-based framework for power grid reduction. Edge sparsification is formulated as a weighted convex optimization problem with sparsity-inducing penalties, which provides an accurate control over the final error. A greedy coordinate descent (GCD) method with optimality guarantee is proposed along with a novel coordinate selection strategy to improve the efficiency and accuracy of edge sparsification. Experimental results demonstrate that the proposed approach achieves better performance compared with traditional gradient descent methods, and 98% accuracy and good sparsity for industrial benchmarks.