HDLCoRe: A Training-Free Framework for Mitigating Hallucinations in LLM-Generated HDL

2025-03-18

AI Summary

Large language models (LLMs) suffer from severe hallucinations and functional errors when generating hardware description language (HDL) code, primarily due to scarce domain-specific training data. To address this without fine-tuning, we propose a lightweight, inference-time framework. Our method introduces two key innovations: (1) HDL-aware self-verifying chain-of-thought prompting, which integrates domain knowledge to guide stepwise reasoning and iterative self-validation; and (2) a two-stage heterogeneous retrieval-augmented generation (RAG) mechanism that jointly performs critical component extraction and sequential re-ranking to balance syntactic robustness and semantic relevance. Evaluated on the RTLLM2.0 benchmark, our approach significantly reduces hallucination rates while substantially improving both syntactic and functional correctness. Remarkably, it achieves state-of-the-art performance without any parameter updates or fine-tuning.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Knowledge Representation and Reasoning: Description Logics

Application Category

• Search and Retrieval-Augmented AI: Large language models for search
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Recent advances in large language models (LLMs) have demonstrated remarkable capabilities in code generation tasks. However, when applied to hardware description languages (HDL), these models exhibit significant limitations due to data scarcity, resulting in hallucinations and incorrect code generation. To address these challenges, we propose HDLCoRe, a training-free framework that enhances LLMs' HDL generation capabilities through prompt engineering techniques and retrieval-augmented generation (RAG). Our approach consists of two main components: (1) an HDL-aware Chain-of-Thought (CoT) prompting technique with self-verification that classifies tasks by complexity and type, incorporates domain-specific knowledge, and guides LLMs through step-by-step self-simulation for error correction; and (2) a two-stage heterogeneous RAG system that addresses formatting inconsistencies through key component extraction and efficiently retrieves relevant HDL examples through sequential filtering and re-ranking. HDLCoRe eliminates the need for model fine-tuning while substantially improving LLMs' HDL generation capabilities. Experimental results demonstrate that our framework achieves superior performance on the RTLLM2.0 benchmark, significantly reducing hallucinations and improving both syntactic and functional correctness.

Problem

• Mitigating hallucinations in LLM-generated HDL code
• Enhancing HDL generation without model fine-tuning
• Improving syntactic and functional correctness in HDL

Innovation

• HDL-aware CoT prompting with self-verification
• Two-stage heterogeneous RAG system
• Training-free framework for HDL generation