OpenROAD and CircuitOps: Infrastructure for ML EDA Research and Education

Traditional electronic design automation (EDA) techniques struggle to fulfill the stringent efficiency and quick turnaround demands of complex integrated systems. Machine learning (ML) strategies for EDA ('ML EDA') are pivotal in transforming EDA to address these challenges. However, they encounter significant obstacles due to inadequate infrastructure, ranging from datasets to software interfaces. This paper demonstrates a software infrastructure for ML EDA built on two key technologies: (i) OpenROAD's Python APIs, and (ii) NVIDIA's CircuitOps, an EDA data representation format tailored for ML, facilitating ML EDA applications. The paper illustrates three ML EDA examples that utilize the established OpenROAD and CircuitOps infrastructure.