The rise of machine learning technology inspires a boom of its applications in electronic design automation (EDA) and helps improve the degree of automation in chip designs. However, manually crafted machine learning models require extensive human expertise and tremendous engineering efforts. In this work, we leverage neural architecture search (NAS) to automate the development of high-quality neural architectures for routability prediction, which can help to guide cell placement toward routable solutions. Our search method supports various operations and highly flexible connections, leading to architectures significantly different from all previous human-crafted models. Experimental results on a large dataset demonstrate that our automatically generated neural architectures clearly outperform multiple representative manually crafted solutions. Compared to the best case of manually crafted models, NAS-generated models achieve 5.85% higher Kendall's $\tau$ in predicting the number of nets with DRC violations and 2.12% better area under ROC curve (ROC-AUC) in DRC hotspot detection. Moreover, compared with human-crafted models, which easily take weeks to develop, our efficient NAS approach finishes the whole automatic search process with only 0.3 days.
翻译:机器学习技术的兴起刺激了其在电子设计自动化(EDA)中的应用,有助于提高芯片设计自动化程度。然而,手工制作的机器学习模型需要广泛的人材专长和巨大的工程努力。在这项工作中,我们利用神经结构搜索(NAS)来将高质量神经结构的开发自动化,以进行可逆转性预测,这有助于引导细胞定位到可路途解决方案。我们的搜索方法支持各种操作和高度灵活的连接,导致与以往所有人类制造的模型大不相同的结构。大型数据集的实验结果表明,我们自动生成的神经结构明显地超越了多种具有代表性的手工设计解决方案。与人工制作模型的最佳案例相比,NAS生成的神经结构模型在预测刚果民主共和国违规蚊帐数量和刚果民主共和国热点探测中ROC曲线下2.12%的更好区域方面实现了5.85%以上。此外,与人造模型相比,我们高效的NAS方法仅用0.3天完成整个自动搜索过程。