OpenRTLSet: A Fully Open-Source Dataset for Large Language Model-based Verilog Module Design
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Computer Science > Computation and Language
Title:OpenRTLSet: A Fully Open-Source Dataset for Large Language Model-based Verilog Module Design
Abstract:OpenRTLSet introduces the largest fully open-source dataset for hardware design, offering over 131,000 diverse Verilog code samples to the research community and industry. Our dataset uniquely combines Verilog code from GitHub repositories (102k modules), VHDL translations (5k modules), and synthesizable C/C++ translations (24k modules), all freely accessible without proprietary restrictions. Using the reasoning model DeepSeek-R1, we generated paired natural language descriptions for each code sample, enabling fine-tuning of various language model families (e.g., Qwen and Granite) for Verilog code generation. Our dataset explores multiple options, including Verilator-generated C++ files as additional context during labeling, quantization techniques (INT4 vs. BF16), and performance differences across model sizes (7B-32B parameters). OpenRTLSet demonstrates that open-source approaches can achieve superior performance in hardware design tasks, establishing a new foundation for accessible research and commercial use in this domain.
| Comments: | Accepted by ICLAD'25 |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2606.10285 [cs.CL] |
| (or arXiv:2606.10285v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.10285
arXiv-issued DOI via DataCite (pending registration)
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| Journal reference: | 2025 IEEE International Conference on LLM-Aided Design (ICLAD), Stanford, CA, USA, 2025, pp. 212-218 |
| Related DOI: | https://doi.org/10.1109/ICLAD65226.2025.00038
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