VeriMoA: A Mixture-of-Agents Framework for Spec-to-HDL Generation

2026-04-21 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Electronic Design Automation · Depth: Expert, long

Summary

VeriMoA is a training-free mixture-of-agents (MoA) framework designed to automate Register Transfer Level (RTL) design by generating Hardware Description Language (HDL) from natural language specifications. It addresses limitations of current LLM-based HDL generation, such as sparse parametric knowledge and domain-specific constraints, which lead to noise propagation and constrained reasoning space in existing multi-agent systems. VeriMoA introduces two key innovations: a quality-guided caching mechanism that stores and ranks all intermediate HDL outputs, enabling agents to select the highest-quality code from the entire generation process, and a multi-path generation strategy that uses C++ and Python as intermediate representations. This two-stage translation (specification → high-level code → HDL) expands the solution space and leverages LLM fluency in high-resource languages. Experiments on VerilogEval 2.0 and RTLLM 2.0 benchmarks show VeriMoA improves Pass@1 by 15–30% across various LLM backbones, allowing smaller models to achieve performance comparable to larger or fine-tuned alternatives without costly training.

Key takeaway

For AI Hardware Engineers developing RTL designs, VeriMoA offers a robust, training-free approach to automate HDL generation. You should consider integrating its quality-guided caching and multi-path generation strategies to improve code accuracy and explore broader design spaces, potentially enabling smaller LLMs to achieve high performance without incurring significant fine-tuning costs. This framework can accelerate design cycles and reduce engineering effort.

Key insights

VeriMoA enhances LLM-based HDL generation via quality-guided caching and multi-path intermediate representations, improving accuracy without training.

Principles

• Monotonic knowledge accumulation prevents error propagation.
• Intermediate representations expand solution space.
• Quality-based selection improves agent collaboration.

Method

VeriMoA employs MoA layers, a quality evaluator, and a global cache. Agents generate HDL, which is scored and cached. Subsequent agents select top-N quality outputs from the entire cache, using C++ and Python as intermediate steps.

In practice

• Utilize C++ and Python as intermediate steps for HDL generation.
• Implement quality-guided caching for iterative code refinement.
• Apply simulation-based evaluation for HDL quality scoring.

Topics

• VeriMoA
• Mixture-of-Agents Framework
• Spec-to-HDL Generation
• RTL Design Automation
• Quality-Guided Caching

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

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