PyraNet: A Multi-Layered Hierarchical Dataset for Verilog
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arXiv
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| Format: | Preprint |
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2024
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| author | Nadimi, Bardia Boutaib, Ghali Omar Zheng, Hao |
| author_facet | Nadimi, Bardia Boutaib, Ghali Omar Zheng, Hao |
| contents | Recently, there has been a growing interest in leveraging Large Language Models for Verilog code generation. However, the current quality of the generated Verilog code remains suboptimal. This is largely due to the absence of well-defined, well-organized datasets with high-quality samples, as well as a lack of innovative fine-tuning methods and models specifically trained on Verilog. In this paper, we introduce a novel open-source dataset and a corresponding fine-tuning technique, which utilizes a multi-layered structure that we refer to as PyraNet. Our experiments demonstrate that employing the proposed dataset and fine-tuning approach leads to a more accurate fine-tuned model, producing syntactically and functionally correct Verilog code. The evaluation results show improvements by up-to $32.6\%$ in comparison to the CodeLlama-7B baseline model and up-to $16.7\%$ in comparison to the state-of-the-art models using VerilogEval evaluation platform. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_06947 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PyraNet: A Multi-Layered Hierarchical Dataset for Verilog Nadimi, Bardia Boutaib, Ghali Omar Zheng, Hao Hardware Architecture Artificial Intelligence Machine Learning Programming Languages Recently, there has been a growing interest in leveraging Large Language Models for Verilog code generation. However, the current quality of the generated Verilog code remains suboptimal. This is largely due to the absence of well-defined, well-organized datasets with high-quality samples, as well as a lack of innovative fine-tuning methods and models specifically trained on Verilog. In this paper, we introduce a novel open-source dataset and a corresponding fine-tuning technique, which utilizes a multi-layered structure that we refer to as PyraNet. Our experiments demonstrate that employing the proposed dataset and fine-tuning approach leads to a more accurate fine-tuned model, producing syntactically and functionally correct Verilog code. The evaluation results show improvements by up-to $32.6\%$ in comparison to the CodeLlama-7B baseline model and up-to $16.7\%$ in comparison to the state-of-the-art models using VerilogEval evaluation platform. |
| title | PyraNet: A Multi-Layered Hierarchical Dataset for Verilog |
| topic | Hardware Architecture Artificial Intelligence Machine Learning Programming Languages |
| url | https://arxiv.org/abs/2412.06947 |