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

Anthony Agnesina (), Yi-Chen Lu () and Sung Kyu Lim ()
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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

Chapter Chapter 10 in Machine Learning Applications in Electronic Design Automation, 2022, pp 247-275 from Springer

Abstract: 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.

Keywords: Machine learning; Reinforcement learning; Graph neural networks; 3D integrated circuits; Physical design (EDA); Circuit optimization (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-13074-8_10

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DOI: 10.1007/978-3-031-13074-8_10

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