SysVCoder: An LLM-Driven Framework for Systematic Generation of System-Level Design

2026-07-01 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, extended

Summary

ComplexVCoder is an open-source, LLM-driven framework designed to improve the generation quality and efficiency of complex Verilog code. It addresses challenges in handling large-scale, multi-level RTL designs by employing a two-stage generation mechanism. This mechanism leverages a novel General Intermediate Representation (GIR) to facilitate a structured transition from natural language descriptions to intricate Verilog. The framework also integrates a rule-based alignment method and a domain-specific, retraining-free Retrieval-Augmented Generation (RAG) module to enhance code correctness by incorporating relevant design knowledge. Evaluated on a new dataset of 55 complex Verilog designs, ComplexVCoder significantly outperforms state-of-the-art frameworks like CodeV and RTLCoder by 14.6% and 22.2% respectively in functional correctness. Notably, it achieves performance comparable to larger models such as GPT-3.5 and DeepSeek-V3 using a smaller 32B Qwen2.5 model.

Key takeaway

For Machine Learning Engineers or Hardware Designers tasked with generating complex Verilog code, you should consider ComplexVCoder. This framework significantly improves functional correctness for multi-level RTL designs, outperforming existing state-of-the-art tools by up to 22.2%. Crucially, it enables smaller, open-source LLMs like Qwen2.5-32B to rival larger proprietary models, offering a resource-efficient path to accurate hardware synthesis. Evaluate its two-stage, GIR-based approach for your next complex design project.

Key insights

A two-stage LLM framework using an intermediate representation significantly improves complex Verilog code generation.

Principles

• Intermediate representations simplify complex code synthesis.
• Structured two-stage generation enhances interpretability.
• Retraining-free RAG boosts functional correctness.

Method

Translate natural language to GIR via instruction tuning, then GIR to Verilog using rule-based alignment and retraining-free RAG.

In practice

• Synthesize multi-level Verilog designs from text.
• Employ smaller LLMs for complex hardware tasks.
• Improve code generation transparency with GIR.

Topics

• LLM-driven Code Generation
• Verilog HDL
• RTL Design
• Intermediate Representation
• Retrieval-Augmented Generation
• Electronic Design Automation

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.