This paper presents CRADLE, a conversational framework for design space exploration of RTL designs using LLM-based multi-agent systems. Unlike existing rigid approaches, CRADLE enables user-guided flows with internal self-verification, correction, and optimization. We demonstrate the framework with a generator-critic agent system targeting FPGA resource minimization using state-of-the-art LLMs. Experimental results on the RTLLM benchmark show that CRADLE achieves significant reductions in resource usage with averages of 48% and 40% in LUTs and FFs across all benchmark designs.
@article{arxiv.2508.08709,
title = {CRADLE: Conversational RTL Design Space Exploration with LLM-based Multi-Agent Systems},
author = {Lukas Krupp and Maximilian Schöffel and Elias Biehl and Norbert Wehn},
journal= {arXiv preprint arXiv:2508.08709},
year = {2025}
}
Comments
Accepted for presentation at the 22nd International SoC Conference (ISOCC 2025). Proceedings to be included in IEEE Xplore