Free and Fair Hardware: A Pathway to Copyright Infringement-Free Verilog Generation using LLMs (2505.06096v2)

Abstract: Limitations in LLM capabilities for hardware design tasks, such as generating functional Verilog codes, have motivated various fine-tuning optimizations utilizing curated hardware datasets from open-source repositories. However, these datasets remain limited in size and contain minimal checks on licensing for reuse, resulting in potential copyright violations by fine-tuned LLMs. Therefore, we propose an evaluation benchmark to estimate the risk of Verilog-trained LLMs to generate copyright-protected codes. To minimize this risk, we present an open-source Verilog dataset, FreeSet, containing over 220k files, along with the automated dataset curation framework utilized to provide additional guarantees of fair-use Verilog data. We then execute an LLM fine-tuning framework consisting of continual pre-training, resulting in a fine-tuned Llama model for Verilog, FreeV. Our results indicate that FreeV demonstrates the smallest risk of copyright-infringement among prior works, with only a 3% violation rate. Furthermore, experimental results demonstrate improvements in Verilog generation functionality over its baseline model, improving VerilogEval pass@10 rates by over 10%.

Insights into "Free and Fair Hardware: A Pathway to Copyright Infringement-Free Verilog Generation using LLMs"

This paper addresses the critical issue of copyright infringement in hardware design and generation using LLMs. The authors propose a comprehensive framework, emphasizing the importance of curating datasets free of potentially copyrighted materials, which holds significant implications for the field of artificial intelligence and intellectual property management.

The paper begins by acknowledging the inherent capabilities of LLMs in understanding and generating textual data across various domains, including hardware design. Despite advancements, hardware design remains a complex and sensitive domain due to the proprietary nature and intellectual property associated with digital circuits and HDL (Hardware Description Language) code, such as Verilog. The core challenge addressed is the risk of these models generating content that inadvertently infringes upon copyright protections when fine-tuned on public datasets.

To estimate the risk of copyright infringement, the authors introduce a novel benchmark based on similarity thresholds, which evaluates LLMs' tendency to generate protected hardware designs. This approach is crucial for assessing the ethical implications of deploying LLMs in domains where intellectual property concerns are paramount.

In response to the copyright challenge, the authors present an automated dataset curation framework designed to scrape and compile open-source Verilog files from platforms like GitHub. This results in the creation of FreeSet, a robust open-source dataset that filters out files with non-permissive licenses and copyright indications via metadata and header analysis. Compared to previous datasets, FreeSet stands out with its substantial size (over 222k files) and rigorous adherence to copyright filtering.

The technical merit of the paper is further demonstrated through the fine-tuning of the Llama model using FreeSet, named FreeV. Experimental results reveal that FreeV exhibits a 3\% copyright infringement rate—significantly lower than previously reported instances—thus affirming the efficacy of the dataset curation process.

Furthermore, the fine-tuning process improves Verilog generation functionality, as evidenced by the increase in VerilogEval pass@10 rates by over 10% from the baseline. These findings suggest that meticulous dataset preparation significantly enhances the model's performance while adhering to ethical guidelines regarding intellectual property. This is a notable contribution, as it sets a precedent for future research initiatives aiming to refine LLMs for domain-specific tasks without compromising legal standards.

In summary, the research introduces a methodologically sound approach to reducing copyright infringement in LLM-based hardware design tasks. The implications are profound, presenting a pathway towards the ethical deployment of LLMs in sensitive domains, reinforcing the necessity for open-source datasets that balance performance and intellectual property ethics. Future developments may build upon these findings by exploring other filtering methodologies, model architectures, or fine-tuning techniques, potentially enhancing the efficacy and ethical compliance of LLM applications.