Routability Optimization for Extreme Aspect Ratio Design Using Convolutional Neural Network

Abstract

Circuits that are placed with very low (or high) aspect ratio are susceptible to routing overflows. Such designs are difficult to close and usually end up with larger area with low area utilization. We observe that non-uniform setting of utilization target greatly helps in these designs, specifically low utilization in the center and gradually higher utilization toward the ends. We introduce a convolutional neural network (CNN) model to predict the setting of utilization target values. Experiments indicate that routing congestion overflows are reduced by 29% on average of test designs with 40% reduction in wirelength.