Understanding and Mitigating Errors of LLM-Generated RTL Code

TL;DR

This paper analyzes errors in LLM-generated RTL code, identifying root causes and proposing targeted correction techniques that significantly improve accuracy on a benchmark.

Contribution

It introduces a comprehensive error analysis and novel correction methods, including knowledge bases and debugging loops, to enhance LLM-based RTL code generation.

Findings

Achieved 98.1% accuracy on VerilogEval benchmark

Identified key error sources such as lack of domain knowledge and ambiguous inputs

Demonstrated effectiveness of integrated correction techniques

Abstract

Despite limited success in large language model (LLM)-based register-transfer-level (RTL) code generation, the root causes of errors remain poorly understood. To address this, we conduct a comprehensive error analysis, finding that most failures arise not from deficient reasoning, but from a lack of RTL programming knowledge, insufficient circuit understanding, ambiguous specifications, or misinterpreted multimodal inputs. Leveraging in-context learning, we propose targeted correction techniques: a retrieval-augmented generation (RAG) knowledge base to supply domain expertise; design description rules with rule-checking to clarify inputs; external tools to convert multimodal data into LLM-compatible formats; and an iterative simulation-debugging loop for remaining errors. Integrating these into an LLM-based framework yields significant improvement, achieving 98.1% accuracy on the VerilogEval benchmark with DeepSeek-v3.2-Speciale, demonstrating the effectiveness of our approach.