用表示学习提升芯片设计的自动化效率与精度
Deep Representation Learning for Electronic Design Automation
- 将电路流程元素转化为图像、网格、图结构进行特征提取
- 在时序预测、布线可实现性分析等任务中显著提升性能
- 适合芯片设计工程师和AI+EDA研究者参考
表示学习已成为电子设计自动化（EDA）算法中的有效技术，利用工作流元素的自然表示形式——图像、网格和图结构。面对电路复杂度上升及功耗、性能、面积（PPA）要求日益严格的问题，表示学习能够自动从复杂数据格式中提取有意义的特征。本文综述了表示学习在EDA中的应用，涵盖基础概念，并分析了以往工作与案例研究，涉及时序预测、布线可行性分析与自动布局等任务。重点介绍了基于图像的方法、基于图的方法以及混合多模态解决方案，展示了其在布线、时序与寄生参数预测方面的改进。结果表明，表示学习能显著提升当前集成电路设计流程的效率、准确性和可扩展性。
原文摘要 · Abstract (English)
Representation learning has become an effective technique utilized by electronic design automation (EDA) algorithms, which leverage the natural representation of workflow elements as images, grids, and graphs. By addressing challenges related to the increasing complexity of circuits and stringent power, performance, and area (PPA) requirements, representation learning facilitates the automatic extraction of meaningful features from complex data formats, including images, grids, and graphs. This paper examines the application of representation learning in EDA, covering foundational concepts and analyzing prior work and case studies on tasks that include timing prediction, routability analysis, and automated placement. Key techniques, including image-based methods, graph-based approaches, and hybrid multimodal solutions, are presented to illustrate the improvements provided in routing, timing, and parasitic prediction. The provided advancements demonstrate the potential of representation learning to enhance efficiency, accuracy, and scalability in current integrated circuit design flows.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。