  In the domain of chip design, Hardware Description Languages (HDLs) play a
pivotal role. However, due to the complex syntax of HDLs and the limited
availability of online resources, debugging HDL codes remains a difficult and
time-intensive task, even for seasoned engineers. Consequently, there is a
pressing need to develop automated HDL code debugging models, which can
alleviate the burden on hardware engineers. Despite the strong capabilities of
Large Language Models (LLMs) in generating, completing, and debugging software
code, their utilization in the specialized field of HDL debugging has been
limited and, to date, has not yielded satisfactory results. In this paper, we
propose an LLM-assisted HDL debugging framework, namely HDLdebugger, which
consists of HDL debugging data generation via a reverse engineering approach, a
search engine for retrieval-augmented generation, and a retrieval-augmented LLM
fine-tuning approach. Through the integration of these components, HDLdebugger
can automate and streamline HDL debugging for chip design. Our comprehensive
experiments, conducted on an HDL code dataset sourced from Huawei, reveal that
HDLdebugger outperforms 13 cutting-edge LLM baselines, displaying exceptional
effectiveness in HDL code debugging.
