Circuit Representation

Circuit representation learning focuses on developing effective methods to encode the structure and function of circuits into numerical representations suitable for machine learning. Current research emphasizes the use of graph neural networks (GNNs) and transformers, often combined in hybrid architectures, to capture both local and global circuit properties, with a growing interest in incorporating attention mechanisms for improved scalability and generalizability. These advancements are significantly impacting electronic design automation (EDA) by enabling more efficient circuit design, analysis, and optimization, particularly in areas like timing prediction and logic synthesis. Furthermore, connections between circuit representations and tensor factorizations are being explored to unify and improve existing models.