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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2509.04905 |
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| _version_ | 1866909771842453504 |
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| author | Xu, Qiang Stok, Leon Drechsler, Rolf Wang, Xi Zhang, Grace Li Markov, Igor L. |
| author_facet | Xu, Qiang Stok, Leon Drechsler, Rolf Wang, Xi Zhang, Grace Li Markov, Igor L. |
| contents | Recent breakthroughs in Large Language Models (LLMs) and Large Circuit Models (LCMs) have sparked excitement across the electronic design automation (EDA) community, promising a revolution in circuit design and optimization. Yet, this excitement is met with significant skepticism: Are these AI models a genuine revolution in circuit design, or a temporary wave of inflated expectations? This paper serves as a foundational text for the corresponding ICCAD 2025 panel, bringing together perspectives from leading experts in academia and industry. It critically examines the practical capabilities, fundamental limitations, and future prospects of large AI models in hardware design. The paper synthesizes the core arguments surrounding reliability, scalability, and interpretability, framing the debate on whether these models can meaningfully outperform or complement traditional EDA methods. The result is an authoritative overview offering fresh insights into one of today's most contentious and impactful technology trends. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_04905 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Revolution or Hype? Seeking the Limits of Large Models in Hardware Design Xu, Qiang Stok, Leon Drechsler, Rolf Wang, Xi Zhang, Grace Li Markov, Igor L. Machine Learning Recent breakthroughs in Large Language Models (LLMs) and Large Circuit Models (LCMs) have sparked excitement across the electronic design automation (EDA) community, promising a revolution in circuit design and optimization. Yet, this excitement is met with significant skepticism: Are these AI models a genuine revolution in circuit design, or a temporary wave of inflated expectations? This paper serves as a foundational text for the corresponding ICCAD 2025 panel, bringing together perspectives from leading experts in academia and industry. It critically examines the practical capabilities, fundamental limitations, and future prospects of large AI models in hardware design. The paper synthesizes the core arguments surrounding reliability, scalability, and interpretability, framing the debate on whether these models can meaningfully outperform or complement traditional EDA methods. The result is an authoritative overview offering fresh insights into one of today's most contentious and impactful technology trends. |
| title | Revolution or Hype? Seeking the Limits of Large Models in Hardware Design |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2509.04905 |