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

TL;DR

This paper introduces Spec2RTL-Agent, an innovative multi-agent system that automates hardware RTL code generation from complex specifications, significantly reducing human intervention and improving correctness through strategic multi-stage refinement.

Contribution

The paper presents the first fully automated multi-agent framework that processes complex hardware specifications and generates synthesizable RTL code with minimal human guidance.

Findings

Achieves up to 75% reduction in human interventions compared to existing methods.

Generates accurate RTL code from unstructured specifications.

Utilizes a multi-module approach for improved correctness and robustness.

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.