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.

翻译：机器学习在超大规模集成电路设计的电子设计自动化中的应用已引起广泛研究关注。尽管构建有效ML模型需要大量数据集，但由于缺乏综合性公共资源，大多数研究仍局限于规模较小的内部生成数据集。为此，我们提出EDALearn——首个专为EDA领域ML任务设计的全流程开源基准测试套件。该套件构建了从综合到物理实现的端到端流程，覆盖各阶段数据采集。它支持跨技术节点的ML可迁移性研究，并保障实验可复现性。我们的基准测试能够适配多种VLSI设计实例与规模，准确反映当代VLSI设计的复杂性。此外，我们提供了深入的数据分析，使用户能够全面理解数据属性与分布——这对创建高效ML模型至关重要。本工作旨在推动ML-EDA领域的进一步发展。