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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2506.00001 |
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| _version_ | 1866915315304103936 |
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| author | Lin, Qun-Kai Hsu, Cheng Chang, Tian-Sheuan |
| author_facet | Lin, Qun-Kai Hsu, Cheng Chang, Tian-Sheuan |
| contents | Large Language Models (LLMs) have attracted considerable attention in recent years due to their remarkable compatibility with Hardware Description Language (HDL) design. In this paper, we examine the performance of three major LLMs, Claude 3 Opus, ChatGPT-4, and ChatGPT-4o, in designing finite state machines (FSMs). By utilizing the instructional content provided by HDLBits, we evaluate the stability, limitations, and potential approaches for improving the success rates of these models. Furthermore, we explore the impact of using the prompt-refining method, To-do-Oriented Prompting (TOP) Patch, on the success rate of these LLM models in various FSM design scenarios. The results show that the systematic format prompt method and the novel prompt refinement method have the potential to be applied to other domains beyond HDL design automation, considering its possible integration with other prompt engineering techniques in the future. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_00001 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhancing Finite State Machine Design Automation with Large Language Models and Prompt Engineering Techniques Lin, Qun-Kai Hsu, Cheng Chang, Tian-Sheuan Hardware Architecture Computation and Language Large Language Models (LLMs) have attracted considerable attention in recent years due to their remarkable compatibility with Hardware Description Language (HDL) design. In this paper, we examine the performance of three major LLMs, Claude 3 Opus, ChatGPT-4, and ChatGPT-4o, in designing finite state machines (FSMs). By utilizing the instructional content provided by HDLBits, we evaluate the stability, limitations, and potential approaches for improving the success rates of these models. Furthermore, we explore the impact of using the prompt-refining method, To-do-Oriented Prompting (TOP) Patch, on the success rate of these LLM models in various FSM design scenarios. The results show that the systematic format prompt method and the novel prompt refinement method have the potential to be applied to other domains beyond HDL design automation, considering its possible integration with other prompt engineering techniques in the future. |
| title | Enhancing Finite State Machine Design Automation with Large Language Models and Prompt Engineering Techniques |
| topic | Hardware Architecture Computation and Language |
| url | https://arxiv.org/abs/2506.00001 |