CRADLE: Conversational RTL Design Space Exploration with LLM-based Multi-Agent Systems
CRADLE: Conversational RTL Design Space Exploration with LLM-based Multi-Agent Systems
著者: Lukas Krupp, Maximilian Schöffel, Elias Biehl, Norbert Wehn
分類: cs.RO, cs.AR, cs.LG, cs.MA
原文アブストラクト
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.