VeriPilot: An LLM-Powered Verilog Debugging Framework

2026-06-22 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Software Development & Engineering · Depth: Expert, quick

Summary

VeriPilot is an LLM-powered framework designed to address the time-consuming challenge of Verilog debugging, particularly for complex bugs that existing LLM methods struggle with due to their reliance on output-level feedback. Proposed to overcome limitations in tracing long dependency chains within large codebases, VeriPilot integrates golden reference models to enable fine-grained bug localization and repair. It achieves this by aligning internal variable semantics between the Verilog design and its golden model through LLM-based analysis. The framework then uses Control-Data-Flow Graphs (CDFGs) derived from static analysis for step-by-step signal tracing, pinpointing suspicious code regions and their correct counterparts. These structured insights guide the LLM for automated code repair. Experimental results on NVIDIA's Comprehensive Verilog Design Problems (CVDP) benchmark show VeriPilot boosts GPT-4o's repair success rate from 54.3% to 85.71%, significantly improving both localization accuracy and repair effectiveness.

Key takeaway

For AI Hardware Engineers struggling with complex Verilog debugging, VeriPilot demonstrates a critical shift from output-level LLM analysis to fine-grained, golden model-guided repair. You should consider integrating static analysis-derived Control-Data-Flow Graphs and semantic alignment with golden models into your automated debugging pipelines. This approach significantly improves LLM repair success rates, as shown by GPT-4o's jump from 54.3% to 85.71%, reducing design cycle times and enhancing reliability.

Key insights

VeriPilot enhances LLM-based Verilog debugging by using golden models and CDFGs for fine-grained bug localization and repair.

Principles

• Align internal variable semantics for debugging.
• Use golden models for fine-grained comparison.
• Guide LLMs with structured static analysis insights.

Method

VeriPilot aligns Verilog design and golden model semantics via LLM analysis, traces signals with CDFGs to identify suspicious code, then provides these structured insights to an LLM for guided repair.

In practice

• Implement golden models for complex designs.
• Integrate CDFG analysis into debug flows.
• Fine-tune LLMs with structured bug insights.

Topics

• Verilog Debugging
• Large Language Models
• Control-Data-Flow Graphs
• Golden Reference Models
• Hardware Architecture
• Automated Code Repair

Code references

• YihanWn/VeriPilot

Best for: Research Scientist, AI Hardware Engineer, AI Scientist, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.