Effective Machine-Learning Models for Predicting Routability During FPGA Placement

Abstract

The ability to efficiently and accurately predict placement routability, while avoiding the large computational cost of performing routing, is an asset when seeking to reduce total placement and routing runtime. In this paper, we present a series of simple ML models and ensembles to predict the routability of a placement solution. Ensembles based on Bagging, Boosting and Stack of classifiers are introduced to produce more accurate and robust solutions than single/simple models. Our results show an improvement in prediction accuracy and runtime compared to the best published results in the literature.