DeepGate3: Towards Scalable Circuit Representation Learning

TL;DR

DeepGate3 introduces a scalable circuit representation learning model that combines GNNs and Transformers, improving generalization and modeling of subcircuits in electronic design automation.

Contribution

It presents a novel architecture integrating Transformers with GNNs and a pooling mechanism, enhancing scalability and subcircuit modeling capabilities.

Findings

Improved scalability over traditional GNN models

Enhanced generalization across diverse circuit designs

Superior representation of subcircuits

Abstract

Circuit representation learning has shown promising results in advancing the field of Electronic Design Automation (EDA). Existing models, such as DeepGate Family, primarily utilize Graph Neural Networks (GNNs) to encode circuit netlists into gate-level embeddings. However, the scalability of GNN-based models is fundamentally constrained by architectural limitations, impacting their ability to generalize across diverse and complex circuit designs. To address these challenges, we introduce DeepGate3, an enhanced architecture that integrates Transformer modules following the initial GNN processing. This novel architecture not only retains the robust gate-level representation capabilities of its predecessor, DeepGate2, but also enhances them with the ability to model subcircuits through a novel pooling transformer mechanism. DeepGate3 is further refined with multiple innovative supervision tasks, significantly enhancing its learning process and enabling superior representation of both gate-level and subcircuit structures. Our experiments demonstrate marked improvements in scalability and generalizability over traditional GNN-based approaches, establishing a significant step forward in circuit representation learning technology.