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

In: Machine Learning Applications in Electronic Design Automation

Author

Listed:
  • Anthony Agnesina

    (Georgia Institute of Technology, School of Electrical and Computer Engineering)

  • Yi-Chen Lu

    (Georgia Institute of Technology, School of Electrical and Computer Engineering)

  • Sung Kyu Lim

    (Georgia Institute of Technology, School of Electrical and Computer Engineering)

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.

Suggested Citation

  • Anthony Agnesina & Yi-Chen Lu & Sung Kyu Lim, 2022. "Circuit Optimization for 2D and 3D ICs with Machine Learning," Springer Books, in: Haoxing Ren & Jiang Hu (ed.), Machine Learning Applications in Electronic Design Automation, chapter 0, pages 247-275, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-13074-8_10
    DOI: 10.1007/978-3-031-13074-8_10
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