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

arXiv: https://arxiv.org/abs/2506.13905 · HTML: https://mlsanity.com/paper/2506.13905/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2025.

## Built on by (most cited first, 18 of 18)

- [ChipBench: A Next-Step Benchmark for Evaluating LLM Performance in AI-Aided Chip Design](https://mlsanity.com/paper/2601.21448/) (2026)
- [Dr. RTL: Autonomous Agentic RTL Optimization through Tool-Grounded Self-Improvement](https://mlsanity.com/paper/2604.14989/) (2026)
- [ChipSeek: Optimizing Verilog Generation via EDA-Integrated Reinforcement Learning](https://mlsanity.com/paper/2507.04736/) (2025)
- [HardSecBench: Benchmarking the Security Awareness of LLMs for Hardware Code Generation](https://mlsanity.com/paper/2601.13864/) (2026)
- [CktEvo: Repository-Level RTL Code Benchmark for Design Evolution](https://mlsanity.com/paper/2603.08718/) (2026)
- [CODMAS: A Dialectic Multi-Agent Collaborative Framework for Structured RTL Optimization](https://mlsanity.com/paper/2603.17204/) (2026)
- [Proof2Silicon: Prompt Repair for Verified Code and Hardware Generation via Reinforcement Learning](https://mlsanity.com/paper/2509.06239/) (2025)
- [David vs. Goliath: Can Small Models Win Big with Agentic AI in Hardware Design?](https://mlsanity.com/paper/2512.05073/) (2025)
- [QiMeng-ChipV-RTL: Exploiting Information Locality for IP-level Verilog Generation](https://mlsanity.com/paper/2602.00704/) (2026)
- [On Integrating Resilience and Human Oversight into LLM-Assisted Modeling Workflows for Digital Twins](https://mlsanity.com/paper/2603.25898/) (2026)
- [VeriGraphi: A Multi-Agent Framework of Hierarchical RTL Generation for Large Hardware Designs](https://mlsanity.com/paper/2604.14550/) (2026)
- [NoTB: Oracle-Free Triage of LLM-Generated RTL via Cross-Model Formal Consensus](https://mlsanity.com/paper/2608.21962/) (2026)
- [LEGO: An LLM Skill-Based Front-End Design Generation Platform](https://mlsanity.com/paper/2604.23355/) (2026)
- [EstRTL: Functional Estimation Guided RTL Code Generation](https://mlsanity.com/paper/2606.09867/) (2026)
- [SwiftCTS: Fast Cross-Design Prediction and Pareto Optimization of Clock Tree Metrics via Few-Shot Calibration](https://mlsanity.com/paper/2606.11348/) (2026)
- [Interpretable and Verifiable Hardware Generation with LLM-Driven Stepwise Refinement](https://mlsanity.com/paper/2606.19387/) (2026)
- [Agentic Electronic Design Automation: A Handoff Perspective](https://mlsanity.com/paper/2606.19795/) (2026)
- [VHDL-REPOBENCH: A Repository-Level Benchmark for Evaluating Large Language Models on VHDL Design Generation](https://mlsanity.com/paper/2610.05380/) (2026)

## Abstract (arXiv)

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.