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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/2507.19525 |
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| _version_ | 1866913960225144832 |
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| author | Zhao, Chenchen Shi, Zhengyuan Wen, Xiangyu Liu, Chengjie Liu, Yi Zhou, Yunhao Zhao, Yuxiang Feng, Hefei Zhu, Yinan Wan, Gwok-Waa Cheng, Xin Chen, Weiyu Fu, Yongqi Chen, Chujie Xue, Chenhao Sun, Guangyu Wang, Ying Lin, Yibo Yang, Jun Xu, Ning Wang, Xi Xu, Qiang |
| author_facet | Zhao, Chenchen Shi, Zhengyuan Wen, Xiangyu Liu, Chengjie Liu, Yi Zhou, Yunhao Zhao, Yuxiang Feng, Hefei Zhu, Yinan Wan, Gwok-Waa Cheng, Xin Chen, Weiyu Fu, Yongqi Chen, Chujie Xue, Chenhao Sun, Guangyu Wang, Ying Lin, Yibo Yang, Jun Xu, Ning Wang, Xi Xu, Qiang |
| contents | The emergence of multimodal large language models (MLLMs) presents promising opportunities for automation and enhancement in Electronic Design Automation (EDA). However, comprehensively evaluating these models in circuit design remains challenging due to the narrow scope of existing benchmarks. To bridge this gap, we introduce MMCircuitEval, the first multimodal benchmark specifically designed to assess MLLM performance comprehensively across diverse EDA tasks. MMCircuitEval comprises 3614 meticulously curated question-answer (QA) pairs spanning digital and analog circuits across critical EDA stages - ranging from general knowledge and specifications to front-end and back-end design. Derived from textbooks, technical question banks, datasheets, and real-world documentation, each QA pair undergoes rigorous expert review for accuracy and relevance. Our benchmark uniquely categorizes questions by design stage, circuit type, tested abilities (knowledge, comprehension, reasoning, computation), and difficulty level, enabling detailed analysis of model capabilities and limitations. Extensive evaluations reveal significant performance gaps among existing LLMs, particularly in back-end design and complex computations, highlighting the critical need for targeted training datasets and modeling approaches. MMCircuitEval provides a foundational resource for advancing MLLMs in EDA, facilitating their integration into real-world circuit design workflows. Our benchmark is available at https://github.com/cure-lab/MMCircuitEval. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_19525 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs Zhao, Chenchen Shi, Zhengyuan Wen, Xiangyu Liu, Chengjie Liu, Yi Zhou, Yunhao Zhao, Yuxiang Feng, Hefei Zhu, Yinan Wan, Gwok-Waa Cheng, Xin Chen, Weiyu Fu, Yongqi Chen, Chujie Xue, Chenhao Sun, Guangyu Wang, Ying Lin, Yibo Yang, Jun Xu, Ning Wang, Xi Xu, Qiang Machine Learning Artificial Intelligence The emergence of multimodal large language models (MLLMs) presents promising opportunities for automation and enhancement in Electronic Design Automation (EDA). However, comprehensively evaluating these models in circuit design remains challenging due to the narrow scope of existing benchmarks. To bridge this gap, we introduce MMCircuitEval, the first multimodal benchmark specifically designed to assess MLLM performance comprehensively across diverse EDA tasks. MMCircuitEval comprises 3614 meticulously curated question-answer (QA) pairs spanning digital and analog circuits across critical EDA stages - ranging from general knowledge and specifications to front-end and back-end design. Derived from textbooks, technical question banks, datasheets, and real-world documentation, each QA pair undergoes rigorous expert review for accuracy and relevance. Our benchmark uniquely categorizes questions by design stage, circuit type, tested abilities (knowledge, comprehension, reasoning, computation), and difficulty level, enabling detailed analysis of model capabilities and limitations. Extensive evaluations reveal significant performance gaps among existing LLMs, particularly in back-end design and complex computations, highlighting the critical need for targeted training datasets and modeling approaches. MMCircuitEval provides a foundational resource for advancing MLLMs in EDA, facilitating their integration into real-world circuit design workflows. Our benchmark is available at https://github.com/cure-lab/MMCircuitEval. |
| title | MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2507.19525 |