In this study, we explore the capability of Large Language Models (LLMs) to automate hardware design by generating high-quality Verilog code, a common language for designing and modeling digital systems. We fine-tune pre-existing LLMs on Verilog datasets compiled from GitHub and Verilog textbooks. We evaluate the functional correctness of the generated Verilog code using a specially designed test suite, featuring a custom problem set and testing benches. Here, our fine-tuned open-source CodeGen-16B model outperforms the commercial state-of-the-art GPT-3.5-turbo model with a 1.1% overall increase. Upon testing with a more diverse and complex problem set, we find that the fine-tuned model shows competitive performance against state-of-the-art gpt-3.5-turbo, excelling in certain scenarios. Notably, it demonstrates a 41% improvement in generating syntactically correct Verilog code across various problem categories compared to its pre-trained counterpart, highlighting the potential of smaller, in-house LLMs in hardware design automation.
@article{arxiv.2308.00708,
title = {VeriGen: A Large Language Model for Verilog Code Generation},
author = {Shailja Thakur and Baleegh Ahmad and Hammond Pearce and Benjamin Tan and Brendan Dolan-Gavitt and Ramesh Karri and Siddharth Garg},
journal= {arXiv preprint arXiv:2308.00708},
year = {2023}
}
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arXiv admin note: text overlap with arXiv:2212.11140