The Dawn of AI-Native EDA: Opportunities and Challenges of Large Circuit Models
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| Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| author | Chen, Lei Chen, Yiqi Chu, Zhufei Fang, Wenji Ho, Tsung-Yi Huang, Ru Huang, Yu Khan, Sadaf Li, Min Li, Xingquan Li, Yu Liang, Yun Liu, Jinwei Liu, Yi Lin, Yibo Luo, Guojie Shi, Zhengyuan Sun, Guangyu Tsaras, Dimitrios Wang, Runsheng Wang, Ziyi Wei, Xinming Xie, Zhiyao Xu, Qiang Xue, Chenhao Yan, Junchi Yang, Jun Yu, Bei Yuan, Mingxuan Young, Evangeline F. Y. Zeng, Xuan Zhang, Haoyi Zhang, Zuodong Zhao, Yuxiang Zhen, Hui-Ling Zheng, Ziyang Zhu, Binwu Zhu, Keren Zou, Sunan |
| author_facet | Chen, Lei Chen, Yiqi Chu, Zhufei Fang, Wenji Ho, Tsung-Yi Huang, Ru Huang, Yu Khan, Sadaf Li, Min Li, Xingquan Li, Yu Liang, Yun Liu, Jinwei Liu, Yi Lin, Yibo Luo, Guojie Shi, Zhengyuan Sun, Guangyu Tsaras, Dimitrios Wang, Runsheng Wang, Ziyi Wei, Xinming Xie, Zhiyao Xu, Qiang Xue, Chenhao Yan, Junchi Yang, Jun Yu, Bei Yuan, Mingxuan Young, Evangeline F. Y. Zeng, Xuan Zhang, Haoyi Zhang, Zuodong Zhao, Yuxiang Zhen, Hui-Ling Zheng, Ziyang Zhu, Binwu Zhu, Keren Zou, Sunan |
| contents | Within the Electronic Design Automation (EDA) domain, AI-driven solutions have emerged as formidable tools, yet they typically augment rather than redefine existing methodologies. These solutions often repurpose deep learning models from other domains, such as vision, text, and graph analytics, applying them to circuit design without tailoring to the unique complexities of electronic circuits. Such an AI4EDA approach falls short of achieving a holistic design synthesis and understanding, overlooking the intricate interplay of electrical, logical, and physical facets of circuit data. This paper argues for a paradigm shift from AI4EDA towards AI-native EDA, integrating AI at the core of the design process. Pivotal to this vision is the development of a multimodal circuit representation learning technique, poised to provide a comprehensive understanding by harmonizing and extracting insights from varied data sources, such as functional specifications, RTL designs, circuit netlists, and physical layouts. We champion the creation of large circuit models (LCMs) that are inherently multimodal, crafted to decode and express the rich semantics and structures of circuit data, thus fostering more resilient, efficient, and inventive design methodologies. Embracing this AI-native philosophy, we foresee a trajectory that transcends the current innovation plateau in EDA, igniting a profound shift-left in electronic design methodology. The envisioned advancements herald not just an evolution of existing EDA tools but a revolution, giving rise to novel instruments of design tools that promise to radically enhance design productivity and inaugurate a new epoch where the optimization of circuit performance, power, and area (PPA) is achieved not incrementally, but through leaps that redefine the benchmarks of electronic systems' capabilities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_07257 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The Dawn of AI-Native EDA: Opportunities and Challenges of Large Circuit Models Chen, Lei Chen, Yiqi Chu, Zhufei Fang, Wenji Ho, Tsung-Yi Huang, Ru Huang, Yu Khan, Sadaf Li, Min Li, Xingquan Li, Yu Liang, Yun Liu, Jinwei Liu, Yi Lin, Yibo Luo, Guojie Shi, Zhengyuan Sun, Guangyu Tsaras, Dimitrios Wang, Runsheng Wang, Ziyi Wei, Xinming Xie, Zhiyao Xu, Qiang Xue, Chenhao Yan, Junchi Yang, Jun Yu, Bei Yuan, Mingxuan Young, Evangeline F. Y. Zeng, Xuan Zhang, Haoyi Zhang, Zuodong Zhao, Yuxiang Zhen, Hui-Ling Zheng, Ziyang Zhu, Binwu Zhu, Keren Zou, Sunan Hardware Architecture Emerging Technologies Within the Electronic Design Automation (EDA) domain, AI-driven solutions have emerged as formidable tools, yet they typically augment rather than redefine existing methodologies. These solutions often repurpose deep learning models from other domains, such as vision, text, and graph analytics, applying them to circuit design without tailoring to the unique complexities of electronic circuits. Such an AI4EDA approach falls short of achieving a holistic design synthesis and understanding, overlooking the intricate interplay of electrical, logical, and physical facets of circuit data. This paper argues for a paradigm shift from AI4EDA towards AI-native EDA, integrating AI at the core of the design process. Pivotal to this vision is the development of a multimodal circuit representation learning technique, poised to provide a comprehensive understanding by harmonizing and extracting insights from varied data sources, such as functional specifications, RTL designs, circuit netlists, and physical layouts. We champion the creation of large circuit models (LCMs) that are inherently multimodal, crafted to decode and express the rich semantics and structures of circuit data, thus fostering more resilient, efficient, and inventive design methodologies. Embracing this AI-native philosophy, we foresee a trajectory that transcends the current innovation plateau in EDA, igniting a profound shift-left in electronic design methodology. The envisioned advancements herald not just an evolution of existing EDA tools but a revolution, giving rise to novel instruments of design tools that promise to radically enhance design productivity and inaugurate a new epoch where the optimization of circuit performance, power, and area (PPA) is achieved not incrementally, but through leaps that redefine the benchmarks of electronic systems' capabilities. |
| title | The Dawn of AI-Native EDA: Opportunities and Challenges of Large Circuit Models |
| topic | Hardware Architecture Emerging Technologies |
| url | https://arxiv.org/abs/2403.07257 |