A Survey of Research in Large Language Models for Electronic Design Automation

Within the rapidly evolving domain of Electronic Design Automation (EDA), Large Language Models (LLMs) have emerged as transformative technologies, offering unprecedented capabilities for optimizing and automating various aspects of electronic design. This survey provides a comprehensive exploration of LLM applications in EDA, focusing on advancements in model architectures, the implications of varying model sizes, and innovative customization techniques that enable tailored analytical insights. By examining the intersection of LLM capabilities and EDA requirements, the article highlights the significant impact these models have on extracting nuanced understandings from complex datasets. Furthermore, it addresses the challenges and opportunities in integrating LLMs into EDA workflows, paving the way for future research and application in this dynamic field. Through this detailed analysis, the survey aims to offer valuable insights to professionals in the EDA industry, AI researchers, and anyone interested in the convergence of advanced AI technologies and electronic design.

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Publication Details

Journal
ACM Transactions on Design Automation of Electronic Systems
Published
2025-02-04
DOI
https://doi.org/10.1145/3715324
Citations
22
Primary Topic
Model-Driven Software Engineering Techniques
Type
article
Field-Weighted Citation Impact
50.77
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article

A Survey of Research in Large Language Models for Electronic Design Automation

G.-S. Zhou, Jingyu Pan, Chen-Chia Chang, Yiran Chen et al.
22 citations
ACM Transactions on Design Automation of Electronic Systems
Model-Driven Software Engineering Techniques
50.77
article

A Survey of Research in Large Language Models for Electronic Design Automation

G.-S. Zhou, Jingyu Pan, Chen-Chia Chang, Yiran Chen, Jiang Hu, Isaac Jacobson
article en
22 citations

Abstract

Within the rapidly evolving domain of Electronic Design Automation (EDA), Large Language Models (LLMs) have emerged as transformative technologies, offering unprecedented capabilities for optimizing and automating various aspects of electronic design. This survey provides a comprehensive exploration of LLM applications in EDA, focusing on advancements in model architectures, the implications of varying model sizes, and innovative customization techniques that enable tailored analytical insights. By examining the intersection of LLM capabilities and EDA requirements, the article highlights the significant impact these models have on extracting nuanced understandings from complex datasets. Furthermore, it addresses the challenges and opportunities in integrating LLMs into EDA workflows, paving the way for future research and application in this dynamic field. Through this detailed analysis, the survey aims to offer valuable insights to professionals in the EDA industry, AI researchers, and anyone interested in the convergence of advanced AI technologies and electronic design.

ACM Transactions on Design Automation of Electronic SystemsVol. 30(3)
Duke University (US), Texas A&M University (US)
Openalex Percentile: Top 0%
Model-Driven Software Engineering Techniques
50.77
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