HDLxGraph: Bridging Large Language Models and HDL Repositories via HDL Graph Databases

• URL: http://arxiv.org/abs/2505.15701v1
• Date: Wed, 21 May 2025 16:14:10 GMT
• Title: HDLxGraph: Bridging Large Language Models and HDL Repositories via HDL Graph Databases
• Authors: Pingqing Zheng, Jiayin Qin, Fuqi Zhang, Shang Wu, Yu Cao, Caiwen Ding, Yang, Zhao,
• Abstract summary: Large Language Models (LLMs) have demonstrated their potential in hardware design tasks.<n>Yet, their performance in real-world, repository-level HDL projects with thousands or even tens of thousands of code lines is hindered.<n>We propose HDLxGraph, a novel framework that integrates Graph Retrieval Augmented Generation (Graph RAG) with LLMs.
• Score: 57.51078142561683
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) have demonstrated their potential in hardware design tasks, such as Hardware Description Language (HDL) generation and debugging. Yet, their performance in real-world, repository-level HDL projects with thousands or even tens of thousands of code lines is hindered. To this end, we propose HDLxGraph, a novel framework that integrates Graph Retrieval Augmented Generation (Graph RAG) with LLMs, introducing HDL-specific graph representations by incorporating Abstract Syntax Trees (ASTs) and Data Flow Graphs (DFGs) to capture both code graph view and hardware graph view. HDLxGraph utilizes a dual-retrieval mechanism that not only mitigates the limited recall issues inherent in similarity-based semantic retrieval by incorporating structural information, but also enhances its extensibility to various real-world tasks by a task-specific retrieval finetuning. Additionally, to address the lack of comprehensive HDL search benchmarks, we introduce HDLSearch, a multi-granularity evaluation dataset derived from real-world repository-level projects. Experimental results demonstrate that HDLxGraph significantly improves average search accuracy, debugging efficiency and completion quality by 12.04%, 12.22% and 5.04% compared to similarity-based RAG, respectively. The code of HDLxGraph and collected HDLSearch benchmark are available at https://github.com/Nick-Zheng-Q/HDLxGraph.

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