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Deep Learning Framework for Placement

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Machine Learning Applications in Electronic Design Automation

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Abstract

Placement is a critical step in the modern backend design flow for integrated circuits (ICs). It needs to determine the locations of millions of instances and meanwhile optimizes for multiple objectives such as wirelength, routability, timing, and so on. These objectives cannot be accurately evaluated until routing and other succeeding stages are performed. As a result, modern placement algorithm follows an iterative procedure for these cross-layer objectives, which is time-consuming. Recent advances in machine learning and its acceleration bring opportunities to speed up placement algorithms from perspectives of both hardware acceleration and cross-layer modeling. This book chapter will survey recent studies on leveraging deep learning frameworks to accelerate kernel placement solvers as well as integrating machine learning models to speed up cross-layer optimization. We hope this line of studies can broaden the applications of machine learning techniques in IC design automation and stimulate more researches in related fields.

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Authors and Affiliations

  1. Peking University, Beijing, China

    Yibo Lin, Zizheng Guo & Jing Mai

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  1. Yibo Lin
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  2. Zizheng Guo
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  3. Jing Mai
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Correspondence to Yibo Lin .

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Editors and Affiliations

  1. NVIDIA, Austin, TX, USA

    Haoxing Ren

  2. Texas A&M University, College Station, TX, USA

    Jiang Hu

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Lin, Y., Guo, Z., Mai, J. (2022). Deep Learning Framework for Placement. In: Ren, H., Hu, J. (eds) Machine Learning Applications in Electronic Design Automation. Springer, Cham. https://doi.org/10.1007/978-3-031-13074-8_9

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