Towards Machine Learning for Placement and Routing in Chip Design: a Methodological Overview
It addresses the NP-hard problems in chip design for engineers and researchers, but is incremental as it is a methodological overview.
This survey examines the application of machine learning to placement and routing in chip design, highlighting its potential to reduce reliance on expert knowledge and improve scalability compared to traditional methods.
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.