Power Distribution Network Optimization Using HLA-GCN for Routability Enhancement

Abstract

Power distribution network (PDN) consumes many routing resources to satisfy IR-drop constraints. With the increasing IR drop and the decreasing metal tracks in recent technology, the design of PDN becomes very important for circuit routing. In this paper, post-placement PDN optimization is proposed for routability enhancement. For a given regular PDN, we iteratively remove partial straps that have a small impact on IR-drop while improving routing overflow. Hierarchical layoutaware graph convolutional network (HLA-GCN) is introduced to find the candidate areas for strap removal, and one area is selected based on scoring. This process is applied twice to reduce the candidates for strap removal, and one strap is finally chosen after identifying the actual impact on IR-drop and routing congestion. This method is enabled by fast incremental IR-drop analysis using PDN-GCN, which classifies nodes with voltage change to update only those nodes in the modified nodal analysis. Experimental results address that the proposed method reduces routing overflow by 16% in an acceptable time, where IR-drop values are updated quickly with high accuracy of 1% error.