EDALearn: A Comprehensive RTL-to-Signoff EDA Benchmark for Democratized
  and Reproducible ML for EDA Research
Jingyu Pan Chen-Chia Chang Zhiyao Xie Yiran Chen
The application of Machine Learning (ML) in Electronic Design Automation
(EDA) for Very Large-Scale Integration (VLSI) design has garnered significant
research attention. Despite the requirement for extensive datasets to build
effective ML models, most studies are limited to smaller, internally generated
datasets due to the lack of comprehensive public resources. In response, we
introduce EDALearn, the first holistic, open-source benchmark suite
specifically for ML tasks in EDA. This benchmark suite presents an end-to-end
flow from synthesis to physical implementation, enriching data collection
across various stages. It fosters reproducibility and promotes research into ML
transferability across different technology nodes. Accommodating a wide range
of VLSI design instances and sizes, our benchmark aptly represents the
complexity of contemporary VLSI designs. Additionally, we provide an in-depth
data analysis, enabling users to fully comprehend the attributes and
distribution of our data, which is essential for creating efficient ML models.
Our contributions aim to encourage further advances in the ML-EDA domain.