EDALearn

EDALearn是由杜克大学和香港科技大学联合开发的综合性EDA基准数据集，旨在为EDA领域的机器学习任务提供全面的数据支持。该数据集涵盖了从合成到物理实现的整个流程，包括多种VLSI设计实例和标准化评估框架，以促进ML模型的公平比较和研究。EDALearn不仅支持多种EDA任务，如布局、路由、电源分析等，还提供了详细的数据分析，帮助用户深入理解数据属性和分布，从而开发出更有效的ML模型。该数据集的应用领域广泛，主要用于解决现代VLSI设计中的复杂问题，如性能优化、电源管理和设计自动化。

EDALearn is a comprehensive EDA benchmark dataset co-developed by Duke University and The Hong Kong University of Science and Technology, aiming to provide comprehensive data support for machine learning tasks in the EDA field. This dataset covers the entire workflow from synthesis to physical implementation, including various VLSI design instances and standardized evaluation frameworks to facilitate fair comparison and research of ML models. EDALearn not only supports multiple EDA tasks such as placement, routing, power analysis and other related tasks, but also provides detailed data analysis to help users gain in-depth insights into data attributes and distributions, so as to develop more effective ML models. This dataset has a wide range of application scenarios, and is mainly used to solve complex problems in modern VLSI design, such as performance optimization, power management and design automation.