Towards LLM-based Root Cause Analysis of Hardware Design Failures

2025-07-08

AI Summary

This work addresses the challenge of root-cause localization for errors occurring during synthesis and simulation in digital hardware design—a task historically hindered by low interpretability and complex dependency chains. We present the first systematic investigation into the efficacy of large language models (LLMs) for this purpose, proposing a retrieval-augmented generation (RAG)-enhanced diagnostic framework. Evaluation is conducted on a benchmark comprising 34 representative error categories derived from real industrial design scenarios. Experimental results show that the o3-mini model achieves 100% accuracy under the pass@5 metric; other state-of-the-art LLMs, when augmented with RAG, attain >90% accuracy—surpassing baseline performance by over 10 percentage points. This study not only validates the strong logical reasoning capability of LLMs in hardware fault attribution but also establishes the first RAG-based paradigm tailored for hardware design root-cause analysis, thereby opening new avenues for EDA automation and security-driven design verification.

Technology Category

• Machine Learning: Hardware-aware ML
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

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
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

With advances in large language models (LLMs), new opportunities have emerged to develop tools that support the digital hardware design process. In this work, we explore how LLMs can assist with explaining the root cause of design issues and bugs that are revealed during synthesis and simulation, a necessary milestone on the pathway towards widespread use of LLMs in the hardware design process and for hardware security analysis. We find promising results: for our corpus of 34 different buggy scenarios, OpenAI's o3-mini reasoning model reached a correct determination 100% of the time under pass@5 scoring, with other state of the art models and configurations usually achieving more than 80% performance and more than 90% when assisted with retrieval-augmented generation.

Problem

• Using LLMs to identify root causes of hardware design failures
• Improving bug explanation during synthesis and simulation
• Enhancing hardware security analysis with AI assistance

Innovation

• LLMs for hardware design root cause analysis
• Retrieval-augmented generation boosts accuracy
• Pass@5 scoring achieves 100% correct determination