On advancing physical design using graph neural networks

Abstract

As modern Physical Design (PD) algorithms and methodologies evolve into the post-Moore era with the aid of machine learning, Graph Neural Networks (GNNs) are becoming increasingly ubiquitous given that netlists are essentially graphs. Recently, their ability to perform effective graph learning has provided significant insights to understand the underlying dynamics during netlist-to-layout transformations. GNNs follow a message-passing scheme, where the goal is to construct meaningful representations either at the entire graph or node-level by recursively aggregating and transforming the initial features. In the realm of PD, the GNN-learned representations have been leveraged to solve the tasks such as cell clustering, quality-of-result prediction, activity simulation, etc., which often overcome the limitations of traditional PD algorithms. In this work, we first revisit recent advancements that GNNs have made in PD. Second, we discuss how GNNs serve as the backbone of novel PD flows. Finally, we present our thoughts on ongoing and future PD challenges that GNNs can tackle and succeed.