论文标题
迈向机器学习，以进行芯片设计中的放置和路由：方法论概述
Towards Machine Learning for Placement and Routing in Chip Design: a Methodological Overview
论文作者
论文摘要
位置和路由是现代芯片设计流中的两个必不可少且具有挑战性的任务。与使用启发式方法或专家孔设计算法的传统求解器相比，机器学习通过其数据驱动的性质表现出了有希望的前景，这可能会较少依赖知识和先验，并且通过其先进的计算范式（例如，具有GPU加速的深层网络）可能会更具扩展性。这项调查始于引入放置和路由的基础知识，并简要说明经典的无学习求解器。然后，我们对机器学习的最新进步进行详细审查。最后，我们讨论了未来研究的挑战和机会。
Placement and routing are two indispensable and challenging (NP-hard) tasks in modern chip design flows. Compared with traditional solvers using heuristics or expert-well-designed algorithms, machine learning has shown promising prospects by its data-driven nature, which can be of less reliance on knowledge and priors, and potentially more scalable by its advanced computational paradigms (e.g. deep networks with GPU acceleration). This survey starts with the introduction of basics of placement and routing, with a brief description on classic learning-free solvers. Then we present detailed review on recent advance in machine learning for placement and routing. Finally we discuss the challenges and opportunities for future research.
