Spec2RTL-Agent: Automated Hardware Code Generation from Complex Specifications Using LLM Agent Systems

2025-06-16

AI Summary

Existing LLM-based RTL generation methods struggle with the complexity of unstructured specification documents in real-world hardware development or rely heavily on manual intervention, limiting scalability. This paper introduces the first multi-agent collaborative framework specifically designed for RTL generation, comprising three core modules: (1) specification understanding and planning, (2) progressive, C++-oriented HLS code generation with dynamic prompt optimization, and (3) adaptive error localization and reflective repair. The framework enables end-to-end, low-intervention automation from unstructured specifications to synthesizable RTL. Integrating multi-stage reasoning, dynamic prompt engineering, and iterative self-correction, it is evaluated on three realistic specification tasks. Results show a significant improvement in RTL functional correctness, a 75% reduction in human intervention, and—critically—the first demonstration of fully automated,全流程 RTL generation without manual guidance.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Machine Learning: Large Multimodal Models (LMMs)
• Multiagent Systems: Multiagent Learning

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Despite recent progress in generating hardware RTL code with LLMs, existing solutions still suffer from a substantial gap between practical application scenarios and the requirements of real-world RTL code development. Prior approaches either focus on overly simplified hardware descriptions or depend on extensive human guidance to process complex specifications, limiting their scalability and automation potential. In this paper, we address this gap by proposing an LLM agent system, termed Spec2RTL-Agent, designed to directly process complex specification documentation and generate corresponding RTL code implementations, advancing LLM-based RTL code generation toward more realistic application settings. To achieve this goal, Spec2RTL-Agent introduces a novel multi-agent collaboration framework that integrates three key enablers: (1) a reasoning and understanding module that translates specifications into structured, step-by-step implementation plans; (2) a progressive coding and prompt optimization module that iteratively refines the code across multiple representations to enhance correctness and synthesisability for RTL conversion; and (3) an adaptive reflection module that identifies and traces the source of errors during generation, ensuring a more robust code generation flow. Instead of directly generating RTL from natural language, our system strategically generates synthesizable C++ code, which is then optimized for HLS. This agent-driven refinement ensures greater correctness and compatibility compared to naive direct RTL generation approaches. We evaluate Spec2RTL-Agent on three specification documents, showing it generates accurate RTL code with up to 75% fewer human interventions than existing methods. This highlights its role as the first fully automated multi-agent system for RTL generation from unstructured specs, reducing reliance on human effort in hardware design.

Problem

• Automate RTL code generation from complex specifications
• Reduce human intervention in hardware design process
• Enhance correctness and scalability of LLM-based RTL generation

Innovation

• Multi-agent collaboration framework for RTL generation
• Progressive coding with iterative refinement for correctness
• Adaptive reflection module for error tracing