Invited: Toward an ML EDA Commons: Establishing Standards, Accessibility, and Reproducibility in ML-driven EDA Research
Vidya A. Chhabria, Jiang Hu, Andrew B. Kahng, Sachin S. Sapatnekar · 2025
Machine learning (ML) is transforming electronic design automation (EDA), offering innovative solutions for designing and optimizing integrated circuits (ICs). However, the field faces significant challenges in standardization, accessibility, and reproducibility, limiting the impact of ML-driven EDA (ML EDA) research. To address these barriers, this paper presents a vision for an ML EDA Commons, a collaborative open ecosystem designed to unify the community and drive progress through establishing standards, shared resources, and stakeholder-based governance. The ML EDA Commons focuses on three objectives: (1) Maturing existing EDA infrastructure to support ML EDA research; (2) Establishing standards for benchmarks, metrics, and data quality and formats for consistent evaluation via governance that includes key stakeholders; and (3) Improving accessibility and reproducibility by providing open datasets, tools, models, and workflows with cloud computing resources, to lower barriers to ML EDA research and promote robust research practices via artifact evaluations, canonical evaluators, and integration pipelines. Inspired by successes of ML and MLCommons, the ML EDA Commons aims to catalyze transparency and sustainability in ML EDA research.