Kernkonzepte
Large language models can be leveraged to provide novice-friendly explanations of synthesis error messages from FPGA design tools like Quartus Prime and Vivado.
Zusammenfassung
The paper examines the use of large language models (LLMs) to generate explanations for synthesis-time errors commonly encountered by novice digital hardware designers. The authors created a dataset of 21 representative bugs in VHDL and Verilog, collected the corresponding error messages from the Quartus Prime and Vivado tools, and then used prompts to task OpenAI's GPT-3.5-turbo, GPT-4, and GPT-4-turbo-preview models to explain the errors.
The authors manually graded the 936 generated explanations based on metrics like conceptual accuracy, completeness, and relevance. They found that the LLMs provided conceptually accurate explanations in 94% of cases, with 71% of the explanations being correct and complete. The results varied across IDEs, programming languages, and prompting strategies, with Quartus Prime errors and Verilog bugs seeing better explanations than Vivado and VHDL, respectively. Prompts that included the specific error line also yielded better responses.
The authors discuss how this work can help improve the accessibility of EDA tools for novice designers and lay the foundation for other LLM-based augmentation of tool feedback. They also note that while the LLMs outperformed expectations, there is still room for improvement, particularly in avoiding over-helpful solutions that could hinder the constructivist learning process.
Statistiken
Training new engineers in digital design is a challenge, particularly when it comes to teaching the complex electronic design automation (EDA) tooling used in this domain.
Estimates have a 67,000 employee shortfall in the US chip design industry by 2030.
The authors generated 936 error message explanations using three OpenAI LLMs over 21 different buggy code samples.
Zitate
"Training new engineers in digital design is a challenge, particularly when it comes to teaching the complex electronic design automation (EDA) tooling used in this domain."
"Estimates have a 67,000 employee shortfall in the US chip design industry by 2030."