Deep Representation Learning for Electronic Design Automation
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913819818721280 |
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| author | Shrestha, Pratik Phatharodom, Saran Aversa, Alec Blankenship, David Wu, Zhengfeng Savidis, Ioannis |
| author_facet | Shrestha, Pratik Phatharodom, Saran Aversa, Alec Blankenship, David Wu, Zhengfeng Savidis, Ioannis |
| contents | Representation learning has become an effective technique utilized by electronic design automation (EDA) algorithms, which leverage the natural representation of workflow elements as images, grids, and graphs. By addressing challenges related to the increasing complexity of circuits and stringent power, performance, and area (PPA) requirements, representation learning facilitates the automatic extraction of meaningful features from complex data formats, including images, grids, and graphs. This paper examines the application of representation learning in EDA, covering foundational concepts and analyzing prior work and case studies on tasks that include timing prediction, routability analysis, and automated placement. Key techniques, including image-based methods, graph-based approaches, and hybrid multimodal solutions, are presented to illustrate the improvements provided in routing, timing, and parasitic prediction. The provided advancements demonstrate the potential of representation learning to enhance efficiency, accuracy, and scalability in current integrated circuit design flows. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_02105 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep Representation Learning for Electronic Design Automation Shrestha, Pratik Phatharodom, Saran Aversa, Alec Blankenship, David Wu, Zhengfeng Savidis, Ioannis Machine Learning Representation learning has become an effective technique utilized by electronic design automation (EDA) algorithms, which leverage the natural representation of workflow elements as images, grids, and graphs. By addressing challenges related to the increasing complexity of circuits and stringent power, performance, and area (PPA) requirements, representation learning facilitates the automatic extraction of meaningful features from complex data formats, including images, grids, and graphs. This paper examines the application of representation learning in EDA, covering foundational concepts and analyzing prior work and case studies on tasks that include timing prediction, routability analysis, and automated placement. Key techniques, including image-based methods, graph-based approaches, and hybrid multimodal solutions, are presented to illustrate the improvements provided in routing, timing, and parasitic prediction. The provided advancements demonstrate the potential of representation learning to enhance efficiency, accuracy, and scalability in current integrated circuit design flows. |
| title | Deep Representation Learning for Electronic Design Automation |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2505.02105 |