Large Language Models (LLMs) for Electronic Design Automation (EDA)
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arXiv
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909755711160320 |
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| author | Xu, Kangwei Schwachhofer, Denis Blocklove, Jason Polian, Ilia Domanski, Peter Pflüger, Dirk Garg, Siddharth Karri, Ramesh Sinanoglu, Ozgur Knechtel, Johann Zhao, Zhuorui Schlichtmann, Ulf Li, Bing |
| author_facet | Xu, Kangwei Schwachhofer, Denis Blocklove, Jason Polian, Ilia Domanski, Peter Pflüger, Dirk Garg, Siddharth Karri, Ramesh Sinanoglu, Ozgur Knechtel, Johann Zhao, Zhuorui Schlichtmann, Ulf Li, Bing |
| contents | With the growing complexity of modern integrated circuits, hardware engineers are required to devote more effort to the full design-to-manufacturing workflow. This workflow involves numerous iterations, making it both labor-intensive and error-prone. Therefore, there is an urgent demand for more efficient Electronic Design Automation (EDA) solutions to accelerate hardware development. Recently, large language models (LLMs) have shown remarkable advancements in contextual comprehension, logical reasoning, and generative capabilities. Since hardware designs and intermediate scripts can be represented as text, integrating LLM for EDA offers a promising opportunity to simplify and even automate the entire workflow. Accordingly, this paper provides a comprehensive overview of incorporating LLMs into EDA, with emphasis on their capabilities, limitations, and future opportunities. Three case studies, along with their outlook, are introduced to demonstrate the capabilities of LLMs in hardware design, testing, and optimization. Finally, future directions and challenges are highlighted to further explore the potential of LLMs in shaping the next-generation EDA, providing valuable insights for researchers interested in leveraging advanced AI technologies for EDA. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_20030 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Large Language Models (LLMs) for Electronic Design Automation (EDA) Xu, Kangwei Schwachhofer, Denis Blocklove, Jason Polian, Ilia Domanski, Peter Pflüger, Dirk Garg, Siddharth Karri, Ramesh Sinanoglu, Ozgur Knechtel, Johann Zhao, Zhuorui Schlichtmann, Ulf Li, Bing Systems and Control Artificial Intelligence Hardware Architecture Machine Learning With the growing complexity of modern integrated circuits, hardware engineers are required to devote more effort to the full design-to-manufacturing workflow. This workflow involves numerous iterations, making it both labor-intensive and error-prone. Therefore, there is an urgent demand for more efficient Electronic Design Automation (EDA) solutions to accelerate hardware development. Recently, large language models (LLMs) have shown remarkable advancements in contextual comprehension, logical reasoning, and generative capabilities. Since hardware designs and intermediate scripts can be represented as text, integrating LLM for EDA offers a promising opportunity to simplify and even automate the entire workflow. Accordingly, this paper provides a comprehensive overview of incorporating LLMs into EDA, with emphasis on their capabilities, limitations, and future opportunities. Three case studies, along with their outlook, are introduced to demonstrate the capabilities of LLMs in hardware design, testing, and optimization. Finally, future directions and challenges are highlighted to further explore the potential of LLMs in shaping the next-generation EDA, providing valuable insights for researchers interested in leveraging advanced AI technologies for EDA. |
| title | Large Language Models (LLMs) for Electronic Design Automation (EDA) |
| topic | Systems and Control Artificial Intelligence Hardware Architecture Machine Learning |
| url | https://arxiv.org/abs/2508.20030 |