QiMeng: Fully Automated Hardware and Software Design for Processor Chip
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| Main Authors: | , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909639460782080 |
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| author | Zhang, Rui Wen, Yuanbo Cheng, Shuyao Huang, Di Peng, Shaohui Guo, Jiaming Jin, Pengwei Zhao, Jiacheng Ma, Tianrui Zhu, Yaoyu Hao, Yifan Zhao, Yongwei Liang, Shengwen Wang, Ying Hu, Xing Du, Zidong Cui, Huimin Li, Ling Guo, Qi Chen, Yunji |
| author_facet | Zhang, Rui Wen, Yuanbo Cheng, Shuyao Huang, Di Peng, Shaohui Guo, Jiaming Jin, Pengwei Zhao, Jiacheng Ma, Tianrui Zhu, Yaoyu Hao, Yifan Zhao, Yongwei Liang, Shengwen Wang, Ying Hu, Xing Du, Zidong Cui, Huimin Li, Ling Guo, Qi Chen, Yunji |
| contents | Processor chip design technology serves as a key frontier driving breakthroughs in computer science and related fields. With the rapid advancement of information technology, conventional design paradigms face three major challenges: the physical constraints of fabrication technologies, the escalating demands for design resources, and the increasing diversity of ecosystems. Automated processor chip design has emerged as a transformative solution to address these challenges. While recent breakthroughs in Artificial Intelligence (AI), particularly Large Language Models (LLMs) techniques, have opened new possibilities for fully automated processor chip design, substantial challenges remain in establishing domain-specific LLMs for processor chip design.
In this paper, we propose QiMeng, a novel system for fully automated hardware and software design of processor chips. QiMeng comprises three hierarchical layers. In the bottom-layer, we construct a domain-specific Large Processor Chip Model (LPCM) that introduces novel designs in architecture, training, and inference, to address key challenges such as knowledge representation gap, data scarcity, correctness assurance, and enormous solution space. In the middle-layer, leveraging the LPCM's knowledge representation and inference capabilities, we develop the Hardware Design Agent and the Software Design Agent to automate the design of hardware and software for processor chips. Currently, several components of QiMeng have been completed and successfully applied in various top-layer applications, demonstrating significant advantages and providing a feasible solution for efficient, fully automated hardware/software design of processor chips. Future research will focus on integrating all components and performing iterative top-down and bottom-up design processes to establish a comprehensive QiMeng system. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_05007 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | QiMeng: Fully Automated Hardware and Software Design for Processor Chip Zhang, Rui Wen, Yuanbo Cheng, Shuyao Huang, Di Peng, Shaohui Guo, Jiaming Jin, Pengwei Zhao, Jiacheng Ma, Tianrui Zhu, Yaoyu Hao, Yifan Zhao, Yongwei Liang, Shengwen Wang, Ying Hu, Xing Du, Zidong Cui, Huimin Li, Ling Guo, Qi Chen, Yunji Hardware Architecture Machine Learning Processor chip design technology serves as a key frontier driving breakthroughs in computer science and related fields. With the rapid advancement of information technology, conventional design paradigms face three major challenges: the physical constraints of fabrication technologies, the escalating demands for design resources, and the increasing diversity of ecosystems. Automated processor chip design has emerged as a transformative solution to address these challenges. While recent breakthroughs in Artificial Intelligence (AI), particularly Large Language Models (LLMs) techniques, have opened new possibilities for fully automated processor chip design, substantial challenges remain in establishing domain-specific LLMs for processor chip design. In this paper, we propose QiMeng, a novel system for fully automated hardware and software design of processor chips. QiMeng comprises three hierarchical layers. In the bottom-layer, we construct a domain-specific Large Processor Chip Model (LPCM) that introduces novel designs in architecture, training, and inference, to address key challenges such as knowledge representation gap, data scarcity, correctness assurance, and enormous solution space. In the middle-layer, leveraging the LPCM's knowledge representation and inference capabilities, we develop the Hardware Design Agent and the Software Design Agent to automate the design of hardware and software for processor chips. Currently, several components of QiMeng have been completed and successfully applied in various top-layer applications, demonstrating significant advantages and providing a feasible solution for efficient, fully automated hardware/software design of processor chips. Future research will focus on integrating all components and performing iterative top-down and bottom-up design processes to establish a comprehensive QiMeng system. |
| title | QiMeng: Fully Automated Hardware and Software Design for Processor Chip |
| topic | Hardware Architecture Machine Learning |
| url | https://arxiv.org/abs/2506.05007 |