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Machine Learning for Analog Layout

In: Machine Learning Applications in Electronic Design Automation

Author

Listed:
  • Steven M. Burns

    (Intel Labs)

  • Hao Chen

    (Texas A&M University)

  • Tonmoy Dhar

    (The University of Texas at Austin)

  • Ramesh Harjani

    (The University of Texas at Austin)

  • Jiang Hu

    (University of Minnesota)

  • Nibedita Karmokar

    (The University of Texas at Austin)

  • Kishor Kunal

    (The University of Texas at Austin)

  • Yaguang Li

    (University of Minnesota)

  • Yishuang Lin

    (University of Minnesota)

  • Mingjie Liu

    (Texas A&M University)

  • Meghna Madhusudan

    (The University of Texas at Austin)

  • Parijat Mukherjee

    (Intel Labs)

  • David Z. Pan

    (Texas A&M University)

  • Jitesh Poojary

    (The University of Texas at Austin)

  • S. Ramprasath

    (The University of Texas at Austin)

  • Sachin S. Sapatnekar

    (The University of Texas at Austin)

  • Arvind K. Sharma

    (The University of Texas at Austin)

  • Wenbin Xu

    (University of Minnesota)

  • Soner Yaldiz

    (Intel Labs)

  • Keren Zhu

    (Texas A&M University)

Abstract

The performance of analog circuits is critically dependent on layout parasitics, but layout has traditionally been a manual and time-consuming task. Recent advances in ML have enabled new capabilities to facilitate fast automated placement and routing. This chapter presents an overview of these techniques, including geometric constraint generation and constrained placement and routing. A variety of ML techniques are used in various steps of analog placement and routing, including graph neural networks, random forest methods, support vector machines, graph attention networks, generative adversarial networks, reinforcement learning, and variational autoencoders. This chapter shows how these general ML algorithms are specifically customized to the requirements of optimized analog layout.

Suggested Citation

  • Steven M. Burns & Hao Chen & Tonmoy Dhar & Ramesh Harjani & Jiang Hu & Nibedita Karmokar & Kishor Kunal & Yaguang Li & Yishuang Lin & Mingjie Liu & Meghna Madhusudan & Parijat Mukherjee & David Z. Pan, 2022. "Machine Learning for Analog Layout," Springer Books, in: Haoxing Ren & Jiang Hu (ed.), Machine Learning Applications in Electronic Design Automation, chapter 0, pages 505-544, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-13074-8_17
    DOI: 10.1007/978-3-031-13074-8_17
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