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Reinforcement Learning for Routing

In: Machine Learning Applications in Electronic Design Automation

Author

Listed:
  • Haiguang Liao

    (Carnegie Mellon University)

  • Levent Burak Kara

    (Carnegie Mellon University)

Abstract

Routing has been one of the most critical and challenging steps in electronics design automation (EDA), and existing solutions have historically relied heavily on heuristics and analytical methods. In recent years, reinforcement learning (RL) has emerged as an alternative for use in various routing problems in the space of chip design. RL-based methods tend to outperform existing heuristics and analytical routing algorithms across various metrics including efficiency and solution quality, and a few are able to solve problems that previously remained unsolved. This chapter provides a review of recent RL routing approaches in EDA and shares insights into open challenges and opportunities. Methods covered in this chapter include RL for global routing, RL for detailed routing, RL for standard cell routing, and RL for other related routing problems.

Suggested Citation

  • Haiguang Liao & Levent Burak Kara, 2022. "Reinforcement Learning for Routing," Springer Books, in: Haoxing Ren & Jiang Hu (ed.), Machine Learning Applications in Electronic Design Automation, chapter 0, pages 277-306, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-13074-8_11
    DOI: 10.1007/978-3-031-13074-8_11
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