Net-Based Machine Learning-Aided Approaches for Timing and Crosstalk Prediction

Rongjian Liang (), Zhiyao Xie (), Erick Carvajal Barboza () and Jiang Hu ()
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Rongjian Liang: Texas A&M University
Zhiyao Xie: Hong Kong University of Science and Technology
Erick Carvajal Barboza: Texas A&M University
Jiang Hu: Texas A&M University

Chapter Chapter 3 in Machine Learning Applications in Electronic Design Automation, 2022, pp 63-84 from Springer

Abstract: Abstract Timing and crosstalk are determined by the complicated joint effects of layout, electrical, and logic parameters, which make conventional estimation methods either too slow or very inaccurate. Thanks to their strong knowledge extraction and reuse capability, machine learning (ML) techniques have been adopted to improve the predictability of timing and crosstalk effects at different design stages. Many of these works develop net-based models, whose outcomes provide detailed information to guide timing optimization and crosstalk avoidance techniques. In this chapter, we first present a comprehensive review of net-based ML-aided approaches for timing and crosstalk prediction. Then, four representative case studies are introduced in detail with the focus on problem formulation, prediction flow, feature engineering, and machine learning engines. Finally, a few conclusion remarks are given.

Keywords: Timing prediction; Crosstalk prediction; Machine learning; Physical design; Wirelength (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_3

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DOI: 10.1007/978-3-031-13074-8_3

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