RTL++: Graph-enhanced LLM for RTL Code Generation

TL;DR

RTL++ introduces a graph-enhanced LLM approach for RTL code generation, encoding code into control flow and data flow graphs to improve accuracy and understanding, outperforming existing models.

Contribution

It is the first to utilize graph representations of RTL code to enhance LLM-based code generation, addressing data diversity and quality issues.

Findings

RTL++ outperforms state-of-the-art models on VerilogEval benchmark.

Graph-based encoding improves code correctness and diversity.

Experimental results validate the effectiveness of graph-enhanced context.

Abstract

As hardware design complexity escalates, there is an urgent need for advanced automation in electronic design automation (EDA). Traditional register transfer level (RTL) design methods are manual, time-consuming, and prone to errors. While commercial (instruction-tuned) large language models (LLMs) shows promising performance for automation, they pose security and privacy concerns. Open-source models offer alternatives; however, they frequently fall short in quality/correctness, largely due to limited, high-quality RTL code data essential for effective training and generalization. This paper proposes RTL++, a first-of-its-kind LLM-assisted method for RTL code generation that utilizes graph representations of code structures to enhance the quality of generated code. By encoding RTL code into a textualized control flowgraphs (CFG) and data flow graphs (DFG), RTL++ captures the inherent hierarchy, dependencies, and relationships within the code. This structured graph-based approach enhances the context available to LLMs, enabling them to better understand and generate instructions. By focusing on data generation through graph representations, RTL++ addresses the limitations of previous approaches that rely solely on code and suffer from lack of diversity. Experimental results demonstrate that RTL++ outperforms state-of-the-art models fine-tuned for RTL generation, as evaluated using the VerilogEval benchmark's Pass@1/5/10 metric, as well as the RTLLM1.1 model, which highlight the effectiveness of graph-enhanced context in advancing the capabilities of LLM-assisted RTL code generation.