ViTAD: Timing Violation-Aware Debugging of RTL Code using Large Language Models

2025-08-18

AI Summary

Addressing the challenges of root-cause localization and manual intervention in resolving timing violations in RTL designs, this paper proposes ViTAD—a novel automated analysis and repair framework integrating Signal Timing Dependency Graphs (STDGs) with Large Language Models (LLMs). ViTAD uniquely combines structured STDG representation with domain-specific timing knowledge to enable precise root-cause inference for violating paths and generate customized, implementation-aware repair strategies. By encoding timing-critical dependencies into graph-based representations and grounding LLM reasoning in a curated timing knowledge base, ViTAD bridges semantic understanding with structural design constraints. Evaluated on 54 real-world industrial RTL cases, ViTAD achieves a 73.68% violation repair success rate—outperforming a pure-LLM baseline by 19.30 percentage points. This demonstrates substantial improvements in both timing convergence efficiency and automation capability for RTL synthesis and optimization workflows.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

In modern Very Large Scale Integrated (VLSI) circuit design flow, the Register-Transfer Level (RTL) stage presents a critical opportunity for timing optimization. Addressing timing violations at this early stage is essential, as modern systems demand higher speeds, where even minor timing violations can lead to functional failures or system crashes. However, traditional timing optimization heavily relies on manual expertise, requiring engineers to iteratively analyze timing reports and debug. To automate this process, this paper proposes ViTAD, a method that efficiently analyzes the root causes of timing violations and dynamically generates targeted repair strategies. Specifically, we first parse Verilog code and timing reports to construct a Signal Timing Dependency Graph (STDG). Based on the STDG, we perform violation path analysis and use large language models (LLMs) to infer the root causes of violations. Finally, by analyzing the causes of violations, we selectively retrieve relevant debugging knowledge from a domain-specific knowledge base to generate customized repair solutions. To evaluate the effectiveness of our method, we construct a timing violation dataset based on real-world open-source projects. This dataset contains 54 cases of violations. Experimental results show that our method achieves a 73.68% success rate in repairing timing violations, while the baseline using only LLM is 54.38%. Our method improves the success rate by 19.30%.

Problem

• Automating timing violation root cause analysis in RTL code
• Generating targeted repair strategies for timing violations
• Reducing manual debugging effort in VLSI circuit design

Innovation

• Uses Signal Timing Dependency Graph analysis
• Leverages large language models for root cause inference
• Retrieves domain-specific knowledge for customized repairs