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HDLdebugger: Streamlining HDL debugging with Large Language Models

Xufeng Yao, Haoyang Li, Tsz Ho Chan, Wenyi Xiao, Mingxuan Yuan, Yu Huang, Lei Chen, Bei Yu

TL;DR

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, which can automate and streamline HDL debugging for chip design.

Abstract

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.

HDLdebugger: Streamlining HDL debugging with Large Language Models

TL;DR

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, which can automate and streamline HDL debugging for chip design.

Abstract

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.
Paper Structure (42 sections, 1 theorem, 11 equations, 8 figures, 7 tables, 2 algorithms)

This paper contains 42 sections, 1 theorem, 11 equations, 8 figures, 7 tables, 2 algorithms.

Table of Contents

  1. introduction
  2. Methodology
  3. HDL Buggy Data Generation
  4. Modification Function Generation
  5. Sample Generation
  6. Search Engine for RAG
  7. Document RAG
  8. Buggy Code RAG
  9. Retrieval-augmented LLM Fine-tuning
  10. Self-guided Thought Generation
  11. Retrieval-augmented Fine-tuning
  12. Experiments
  13. Experiment Setting
  14. HDL Datasets in Huawei
  15. Baselines
  16. ...and 27 more sections

Key Result

theorem 1

Alg. alg:ranker can achieve a $1-1/e$ approximation ratio.

Figures (8)

  • Figure 1: Framework overview of HDLdebugger.
  • Figure 2: Reverse Engineering Pipeline for inducing error to correct code by diverse modification functions summarized from HDL documents.
  • Figure 3: The search engine to retrieve relevant information given error messages and retrieve similar buggy codes based on a buggy code query.
  • Figure 4: Thought generation example.
  • Figure 5: Pass-rate and code retrieved code instances
  • ...and 3 more figures

Theorems & Definitions (1)

  • theorem 1