Source-linked AI summary
RTLFixer: Automatically Fixing RTL Syntax Errors with Large Language Models
Yun-Da Tsai, Mingjie Liu, Haoxing Ren
TL;DR
LLM-generated Verilog frequently contains syntax errors, creating a need for iterative debugging beyond one-shot generation. RTLFixer combines ReAct autonomous agents with RAG-based human guidance and compiler feedback, resolving syntax errors at high rates and improving VerilogEval pass@1 results. Its demonstrated scope is syntax debugging; preliminary studies found limited gains for complex simulation-error debugging.
Problem
55% of errors in LLM-generated Verilog are syntax-related, motivating debugging and refinement capabilities beyond one-shot code generation.
Method
RTLFixer uses ReAct and RAG to let LLM agents iteratively revise Verilog with compiler feedback and retrieved human expert guidance.
Results
RTLFixer resolves 98.5% of syntax errors and improves pass@1 by 32.3% on VerilogEval-Machine and 10.1% on VerilogEval-Human.
Takeaways & Limitations
Syntax-focused debugging substantially improves the reported VerilogEval results, while RTLFixer provides an autonomous feedback-driven framework for Verilog code correction.
Takeaways & Limitations
Preliminary studies found limited improvement when debugging simulation errors, with difficulty on complex questions involving high-level design functionality and advanced reasoning.
Abstract
from arXiv · showhide
This paper presents RTLFixer, a novel framework enabling automatic syntax errors fixing for Verilog code with Large Language Models (LLMs). Despite LLM's promising capabilities, our analysis indicates that approximately 55% of errors in LLM-generated Verilog are syntax-related, leading to compilation failures. To tackle this issue, we introduce a novel debugging framework that employs Retrieval-Augmented Generation (RAG) and ReAct prompting, enabling LLMs to act as autonomous agents in interactively debugging the code with feedback. This framework demonstrates exceptional proficiency in resolving syntax errors, successfully correcting about 98.5% of compilation errors in our debugging dataset, comprising 212 erroneous implementations derived from the VerilogEval benchmark. Our method leads to 32.3% and 10.1% increase in pass@1 success rates in the VerilogEval-Machine and VerilogEval-Human benchmarks, respectively.
1 INTRODUCTION
RTLFixer addresses the difficulty of generating syntactically correct Verilog by combining autonomous ReAct debugging with human guidance retrieved through RAG. The framework targets syntax errors, which comprise 55% of LLM-generated Verilog errors, and reports substantial benchmark improvements.
- Motivation: 55% of LLM-generated Verilog errors are syntax errors, exceeding simulation errors and motivating automated syntax debugging.The paper links syntax correction to improved code accuracy and potentially reduced manual effort for human engineers.
- Approach: RTLFixer combines ReAct-based autonomous reasoning and action planning with RAG-based retrieval of human expert guidance.Compiler messages and retrieved guidance provide feedback during iterative debugging.
- Resources: The paper introduces VerilogEval-syntax, a benchmark dataset derived from VerilogEval for syntax-error debugging.The dataset contains erroneous implementations with syntax errors and supports evaluation of the proposed framework.
- Results: 98.5% of syntax errors were resolved, while pass@1 improved by 32.3% on VerilogEval-Machine and 10.1% on VerilogEval-Human.These results are reported for correcting syntax errors in the respective benchmarks.
2 PRELIMINARIES
The preliminaries position RTLFixer within prior work on LLM-based Verilog generation, reasoning and planning, and retrieval-augmented generation. These foundations motivate using ReAct for interactive actions and RAG for access to external knowledge.
- LLMs for Verilog Generation: Prior hardware-design efforts include DAVE, VeriGen, Chip-Chat, VerilogEval, and RTLLM, with earlier work emphasizing LLM-based Verilog generation.The cited benchmarks and systems expanded datasets, model coverage, and collaborative hardware-generation capabilities.
- Reasoning and Planning: Chain-of-thought supports stepwise reasoning, while ToolLLM demonstrates interactive decision-making and action planning with digital tools.These approaches provide background for treating language models as systems that reason and act across multiple steps.
- ReAct: ReAct integrates reasoning traces with specific actions, enabling dynamic interaction with external information sources.This integration is presented as supporting more context-aware autonomous-agent behavior.
- RAG: RAG combines an LLM’s parametric memory with an external knowledge base functioning as non-parametric memory.The approach addresses difficulties in accessing and manipulating knowledge already represented in the model.
3 RTLFIXER: RESOLVING SYNTAX ERROR WITH LLM AGENTS AND RETRIEVAL
RTLFixer uses an LLM agent with ReAct and RAG to iteratively revise erroneous Verilog using compiler feedback and retrieved expert guidance. The paper also constructs a syntax-debugging dataset from VerilogEval for evaluation.
- Framework: RTLFixer repeatedly revises Verilog by combining compiler error logs, retrieved human guidance, and ReAct-driven reasoning and actions.The loop continues until compilation succeeds or the user-selected iteration limit is reached.
- ReAct Iterative Prompting: ReAct interleaves Thought, Action, and Observation steps, including explaining errors, searching guidance, revising code, and recompiling.A Finish action outputs the final response after successful compilation.
- Baseline: One-shot prompting serves as the baseline with a single compiler-feedback turn, unlike ReAct’s iterative reasoning, planning, and compiler interaction.The comparison isolates the contribution of iterative prompting relative to single-turn feedback.
- RAG Guidance: RAG retrieves curated human instructions and demonstrations for syntax-error resolution from a database organized around compiler error categories.The database stores compiler logs, error-code segments, and corresponding guidance; experiments use exact error-tag matching.
- Debugging Dataset: VerilogEval-syntax contains flawed Verilog implementations with syntax errors derived from the VerilogEval problem set.Sampling, filtering, and clustering produce a diverse set of representative erroneous implementations for debugging evaluation.
4 EXPERIMENTS
RTLFixer is evaluated through compile-fix rate, simulation pass@k, benchmark comparisons, and ablations of prompting, retrieval, feedback quality, and LLM choice. Results show substantial gains from ReAct and RAG, with syntax correction improving VerilogEval simulation performance and generalizing to RTLLM.
- 4.1 Evaluation Metrics: The experiments measure compile-fix rate over n=10 samples and functional correctness with the pass@k metric using n=20 samples.A problem is solved when any of its k samples passes simulation tests.
- 4.2 Main Results: ReAct and RAG each provide large performance gains, with RAG improving Quartus fix rate from 79.9% to 98.5% for ReAct.ReAct also improves syntax success over one-shot generation by 25.7%, 26.4%, and 31.2% with Simple, iverilog, and Quartus feedback, respectively.
- 4.2 Main Results: 32.3% and 10.1% improvements in VerilogEval pass@1 follow syntax-error correction for the Machine and Human subsets, respectively.Syntax errors constitute 55% of GPT-3.5-generated Verilog errors, and the approach raises one reported pass rate from 26.7% to 36.8%.
- 4.2 Main Results: Syntax-error correction generalizes to RTLLM, while improvements are larger for easy Human problems than hard ones, at 14.5% versus 6.7% on pass@1.For simple Human problems and low-level Machine descriptions, correction raises pass@1 to around 80%.
- 4.3.1 Impact of Feedback Quality: Higher-quality compiler feedback improves syntax-fixing success, with Quartus providing more informative messages than iverilog.The disparity is especially pronounced for ReAct with RAG, potentially reflecting better use of retrieved human guidance.
- 4.3.2 Impact of LLM: GPT-4 reaches 98% syntax-fixing success for one-shot prompting with RAG and Quartus, while ReAct adds only approximately 1% over GPT-4 one-shot performance.The results suggest ReAct and RAG can narrow the gap between weaker and stronger LLMs, particularly for weaker open-source models.
5 ANALYSIS AND DISCUSSION
RTLFixer’s remaining syntax-fixing failures often involve arithmetic index reasoning, while iterative refinement resolves most problems in one revision. The framework shows limited improvement when debugging complex simulation logic errors.
- Failure Analysis: Arithmetic index calculations caused a representative failure when the agent did not recognize an out-of-bound array access.The erroneous implementation produced index -17 for vector q, outside its declared range [255:0].
- Iterative Code Refinement: About 90% of syntax-error problems were resolved in a single ReAct revision.Additional revisions were needed in the remaining cases because new errors could appear after the initial correction.
- Challenges in Debugging Simulation Errors: Simulation-error debugging produced limited improvements beyond syntax-error fixes.LLMs handled simple logic errors but struggled with complex problems involving high-level functionality descriptions and advanced reasoning.
6 CONCLUSION
RTLFixer combines retrieval-augmented generation and ReAct prompting to improve LLM-based Verilog debugging. The framework achieves syntax-error resolution success rates as high as 98.5%.
- Conclusion: RTLFixer combines Retrieval Augmented Generation and ReAct prompting for debugging Verilog code with Large Language Models.The framework is presented as an autonomous language agent and includes a dataset for further exploration.