Strengthening the Foundations for IC Physical Design and ML EDA Research
Authors
V. A. Chhabria, V. Gopalakrishnan, A. B. Kahng, S. Kundu, Z. Wang, B.-Y. Wu, and D. Yoon
Abstract
Over the past year, IEEE CEDA DATC has continued to improve the DATC Robust Design Flow (RDF) while also advancing open infrastructure for research, including machine learning for electronic design automation (ML EDA). The 2024 RDF release includes new standalone and integrated global placement and macro placement engines, as well as a CCS-based delay calculator. Advances in baselines and benchmarks include the addition of new benchmarks for macro placement and logic gate sizing, as well as further efforts to establish calibrations of both optimizations and analyses to aid assessments of research progress in EDA. Additional efforts to promote open and reproducible research include refined proxy research enablements and enhanced ML EDA infrastructure through the development and use of new formats, the release of datasets, and the development of Python APIs in OpenROAD.