ChipExpert: The Open-Source Integrated-Circuit-Design-Specific Large Language Model
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| Main Authors: | , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866911975091470336 |
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| author | Xu, Ning Zhang, Zhaoyang Qi, Lei Wang, Wensuo Zhang, Chao Ren, Zihao Zhang, Huaiyuan Cheng, Xin Zhang, Yanqi Liu, Zhichao Wei, Qingwen Wu, Shiyang Yang, Lanlan Lu, Qianfeng Ma, Yiqun Zhao, Mengyao Liu, Junbo Song, Yufan Geng, Xin Yang, Jun |
| author_facet | Xu, Ning Zhang, Zhaoyang Qi, Lei Wang, Wensuo Zhang, Chao Ren, Zihao Zhang, Huaiyuan Cheng, Xin Zhang, Yanqi Liu, Zhichao Wei, Qingwen Wu, Shiyang Yang, Lanlan Lu, Qianfeng Ma, Yiqun Zhao, Mengyao Liu, Junbo Song, Yufan Geng, Xin Yang, Jun |
| contents | The field of integrated circuit (IC) design is highly specialized, presenting significant barriers to entry and research and development challenges. Although large language models (LLMs) have achieved remarkable success in various domains, existing LLMs often fail to meet the specific needs of students, engineers, and researchers. Consequently, the potential of LLMs in the IC design domain remains largely unexplored. To address these issues, we introduce ChipExpert, the first open-source, instructional LLM specifically tailored for the IC design field. ChipExpert is trained on one of the current best open-source base model (Llama-3 8B). The entire training process encompasses several key stages, including data preparation, continue pre-training, instruction-guided supervised fine-tuning, preference alignment, and evaluation. In the data preparation stage, we construct multiple high-quality custom datasets through manual selection and data synthesis techniques. In the subsequent two stages, ChipExpert acquires a vast amount of IC design knowledge and learns how to respond to user queries professionally. ChipExpert also undergoes an alignment phase, using Direct Preference Optimization, to achieve a high standard of ethical performance. Finally, to mitigate the hallucinations of ChipExpert, we have developed a Retrieval-Augmented Generation (RAG) system, based on the IC design knowledge base. We also released the first IC design benchmark ChipICD-Bench, to evaluate the capabilities of LLMs across multiple IC design sub-domains. Through comprehensive experiments conducted on this benchmark, ChipExpert demonstrated a high level of expertise in IC design knowledge Question-and-Answer tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_00804 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ChipExpert: The Open-Source Integrated-Circuit-Design-Specific Large Language Model Xu, Ning Zhang, Zhaoyang Qi, Lei Wang, Wensuo Zhang, Chao Ren, Zihao Zhang, Huaiyuan Cheng, Xin Zhang, Yanqi Liu, Zhichao Wei, Qingwen Wu, Shiyang Yang, Lanlan Lu, Qianfeng Ma, Yiqun Zhao, Mengyao Liu, Junbo Song, Yufan Geng, Xin Yang, Jun Hardware Architecture Artificial Intelligence Machine Learning The field of integrated circuit (IC) design is highly specialized, presenting significant barriers to entry and research and development challenges. Although large language models (LLMs) have achieved remarkable success in various domains, existing LLMs often fail to meet the specific needs of students, engineers, and researchers. Consequently, the potential of LLMs in the IC design domain remains largely unexplored. To address these issues, we introduce ChipExpert, the first open-source, instructional LLM specifically tailored for the IC design field. ChipExpert is trained on one of the current best open-source base model (Llama-3 8B). The entire training process encompasses several key stages, including data preparation, continue pre-training, instruction-guided supervised fine-tuning, preference alignment, and evaluation. In the data preparation stage, we construct multiple high-quality custom datasets through manual selection and data synthesis techniques. In the subsequent two stages, ChipExpert acquires a vast amount of IC design knowledge and learns how to respond to user queries professionally. ChipExpert also undergoes an alignment phase, using Direct Preference Optimization, to achieve a high standard of ethical performance. Finally, to mitigate the hallucinations of ChipExpert, we have developed a Retrieval-Augmented Generation (RAG) system, based on the IC design knowledge base. We also released the first IC design benchmark ChipICD-Bench, to evaluate the capabilities of LLMs across multiple IC design sub-domains. Through comprehensive experiments conducted on this benchmark, ChipExpert demonstrated a high level of expertise in IC design knowledge Question-and-Answer tasks. |
| title | ChipExpert: The Open-Source Integrated-Circuit-Design-Specific Large Language Model |
| topic | Hardware Architecture Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2408.00804 |