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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.09655 |
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| _version_ | 1866910787097853952 |
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| author | Pan, Jingyu Zhou, Guanglei Chang, Chen-Chia Jacobson, Isaac Hu, Jiang Chen, Yiran |
| author_facet | Pan, Jingyu Zhou, Guanglei Chang, Chen-Chia Jacobson, Isaac Hu, Jiang Chen, Yiran |
| contents | 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 paper 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_09655 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Survey of Research in Large Language Models for Electronic Design Automation Pan, Jingyu Zhou, Guanglei Chang, Chen-Chia Jacobson, Isaac Hu, Jiang Chen, Yiran Machine Learning 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 paper 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. |
| title | A Survey of Research in Large Language Models for Electronic Design Automation |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2501.09655 |