DepthGraphNet: Circuit Graph Isomorphism Detection via Siamese-Graph Neural Networks

The circuit graph isomorphism (CGI) problem is a fundamental computational problem in EDA that involves determining the equivalence of two circuit graphs. This issue carries great significance in the design flow of circuits, as well as with intellectual property (IP) protection efforts–a solution to this issue would have significant implications for the design process, enabling a faster and more efficient design flow. In this work, we introduce DepthGraphNet, a Siamese-Graph Neural Network (SGNN) for the CGI problem. Additionally, we show that our proposed approach runs considerably faster than classical approaches while maintaining high accuracy on a real-world circuit dataset. Furthermore, we provide theorems which help deal with the time-consuming SGNN architecture design process.