用新方法提升芯片布线预测准确率，效果比现有方案好40%。
Improving Routability Prediction via NAS Using a Smooth One-shot Augmented Predictor
- 结合一次性搜索与预测器，用平滑数据增强改进神经架构搜索
- 在布线热点检测中达到0.9802的ROC-AUC，查询仅需0.461毫秒
- 适合芯片设计自动化领域的研究人员和工程师参考
现代EDA工具中的布线可布性优化已广泛采用机器学习模型，但构建和优化这些模型仍是挑战。神经架构搜索（NAS）可用于辅助模型构建与性能提升。传统NAS在布线预测任务中表现不佳，主要源于训练目标与搜索目标分离带来的噪声，以及搜索目标方差增大所引发的复杂性。为此，本文提出新型NAS方法SOAP-NAS，通过创新的数据增强技术和一次性搜索与预测器相结合的策略，解决上述问题。实验结果表明，该方法在DRC热点检测任务中相较现有方案提升40%，接近理想性能。SOAPNet实现0.9802的ROC-AUC值，单次查询耗时仅0.461毫秒。
原文摘要 · Abstract (English)
Routability optimization in modern EDA tools has benefited greatly from using machine learning (ML) models. Constructing and optimizing the performance of ML models continues to be a challenge. Neural Architecture Search (NAS) serves as a tool to aid in the construction and improvement of these models. Traditional NAS techniques struggle to perform well on routability prediction as a result of two primary factors. First, the separation between the training objective and the search objective adds noise to the NAS process. Secondly, the increased variance of the search objective further complicates performing NAS. We craft a novel NAS technique, coined SOAP-NAS, to address these challenges through novel data augmentation techniques and a novel combination of one-shot and predictor-based NAS. Results show that our technique outperforms existing solutions by 40% closer to the ideal performance measured by ROC-AUC (area under the receiver operating characteristic curve) in DRC hotspot detection. SOAPNet is able to achieve an ROC-AUC of 0.9802 and a query time of only 0.461 ms.
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