What is it about?

Large Language Models (LLMs) is widely used for code debugging (e.g., C and Python) but face limitations in debugging Register Transfer Level (RTL) code largely due to data scarcity. This paper presents Make Each Iteration Count (MEIC), a novel framework for RTL debugging. Unlike traditional approaches heavily relying on prompt engineering, model tuning, and model training, MEIC employs an iterative process with LLM to address syntax and function errors efficiently. We also introduce an open-source dataset with 178 RTL errors for evaluation. Results demonstrate a 93% fix rate for syntax errors and a 78% fix rate for function errors.

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Why is it important?

Unlike prior work, our method is inspired by human debugging practices, recognizing the``there is never one-shot debugging'' but iterative nature of the process. Human-led debugging involves collaboration and multiple iterations, from initial design to final verification, with individuals of diverse capabilities. This iterative process continues until achieving error-free code or meeting strict coverage criteria. Acknowledging similarities between uncertainties in LLM outputs and human performance variabilities, this approach lays a strong foundation for LLM-based RTL debugging. Employing an iterative approach specifically addresses uncertainties associated with LLM models.

Perspectives

Writing this article was a great pleasure. I hope this article has been inspiring, promoting the development of automated debugging of hardware codes, and helping improve the efficiency of hardware design.

Yuchen Hu
Southeast University

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This page is a summary of: Make Each Iteration Count, July 2024, ACM (Association for Computing Machinery),
DOI: 10.1145/3674399.3674482.
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