Large language models (LLMs) have shown promise in generating RTL code from natural-language descriptions, but existing methods remain static and struggle to adapt to evolving design requirements, potentially causing structural drift and costly full regeneration. We propose IncreRTL, a LLM-driven framework for incremental RTL generation under requirement evolution. By constructing requirement-code traceability links to locate and regenerate affected code segments, IncreRTL achieves accurate and consistent updates. Evaluated on our newly constructed EvoRTL-Bench, IncreRTL demonstrates notable improvements in regeneration consistency and efficiency, advancing LLM-based RTL generation toward practical engineering deployment.

翻译：大型语言模型（LLM）在从自然语言描述生成RTL代码方面展现出潜力，但现有方法仍处于静态阶段，难以适应不断变化的设计需求，可能导致结构漂移和高昂的完全重生成成本。我们提出IncreRTL，一种面向需求演化场景的LLM驱动的增量式RTL生成框架。通过构建需求-代码可追溯性链接以定位并重生成受影响的代码段，IncreRTL实现了准确且一致的更新。在我们新构建的EvoRTL-Bench基准上的评估表明，IncreRTL在重生成一致性与效率方面取得了显著提升，推动基于LLM的RTL生成向实际工程部署迈进。