VeriGraphi: A Multi-Agent Framework of Hierarchical RTL Generation for Large Hardware Designs

2026-04-16 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Hardware Design & Architecture, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

VeriGraphi is a multi-agent framework designed to improve the generation of synthesizable Verilog for large, hierarchical hardware designs using large language models (LLMs). LLMs typically struggle with maintaining context, interfaces, and structural coherence across modules in complex designs. VeriGraphi addresses these issues by introducing a spec-anchored Knowledge Graph, called a Hierarchical Design Architecture (HDA), which explicitly encodes module hierarchy, port interfaces, wiring semantics, and inter-module dependencies. This HDA is built iteratively through multi-agent analysis of the design specification, providing a machine-checkable structural scaffold before code generation. A progressive coding module then uses this Knowledge Graph to incrementally generate pseudo-code and synthesizable RTL, ensuring interface consistency and dependency correctness. Evaluated on three NIST specification documents and a detailed RV32I processor case study, VeriGraphi demonstrates reliable hierarchical RTL generation for RISC-V with minimal human intervention and strong functional correctness.

Key takeaway

For research scientists developing LLM-based hardware design tools, VeriGraphi's approach of using a spec-anchored Knowledge Graph offers a robust solution to common challenges like context loss and interface hallucination. You should consider integrating similar structured knowledge representations into your design pipelines to enhance the reliability and functional correctness of LLM-generated hierarchical RTL, particularly for complex architectures like RISC-V processors.

Key insights

VeriGraphi uses a spec-anchored Knowledge Graph to guide LLM-based hierarchical RTL generation, improving structural coherence and interface consistency.

Principles

• Explicitly encode design hierarchy and dependencies.
• Validate structural integrity before code generation.

Method

VeriGraphi constructs a Hierarchical Design Architecture (HDA) Knowledge Graph via multi-agent analysis, then a progressive coding module generates RTL guided by the HDA to ensure consistency.

In practice

• Apply Knowledge Graphs to manage LLM context in complex code generation.
• Use multi-agent systems for iterative specification analysis.

Topics

• VeriGraphi
• Hierarchical RTL Generation
• Knowledge Graph
• Multi-Agent Framework
• Hardware Design Automation

Best for: Research Scientist, AI Scientist, AI Engineer, AI Hardware Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.