CRADLE: Conversational RTL Design Space Exploration with LLM-based Multi-Agent Systems
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866909733968936960 |
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| author | Krupp, Lukas Schöffel, Maximilian Biehl, Elias Wehn, Norbert |
| author_facet | Krupp, Lukas Schöffel, Maximilian Biehl, Elias Wehn, Norbert |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_08709 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CRADLE: Conversational RTL Design Space Exploration with LLM-based Multi-Agent Systems Krupp, Lukas Schöffel, Maximilian Biehl, Elias Wehn, Norbert Robotics Hardware Architecture Machine Learning Multiagent Systems 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. |
| title | CRADLE: Conversational RTL Design Space Exploration with LLM-based Multi-Agent Systems |
| topic | Robotics Hardware Architecture Machine Learning Multiagent Systems |
| url | https://arxiv.org/abs/2508.08709 |