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Circuit Optimization for 2D and 3D ICs with Machine Learning

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Machine Learning Applications in Electronic Design Automation

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Abstract

In today’s fast-changing and demanding semiconductor market, a new arsenal of design automation solutions must be developed to provide ever-so-needed speedups and dramatic advances in the design process. This chapter presents how traditional physical design algorithms and their extensive portfolio of design settings can be replaced or enhanced with machine learning and a data-driven philosophy. Indeed, using powerful machine learning methods can help mitigate the penalties of the suboptimality of classical approximation algorithms and heuristics by resolving long-lasting NP-hard circuit optimization problems.

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Authors and Affiliations

  1. School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA

    Anthony Agnesina, Yi-Chen Lu & Sung Kyu Lim

Authors
  1. Anthony Agnesina
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  2. Yi-Chen Lu
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  3. Sung Kyu Lim
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Corresponding author

Correspondence to Anthony Agnesina .

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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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Agnesina, A., Lu, YC., Lim, S.K. (2022). Circuit Optimization for 2D and 3D ICs with Machine Learning. 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_10

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