ChipNeMo aims to explore the applications of large language models (LLMs) for
industrial chip design. Instead of directly deploying off-the-shelf commercial
or open-source LLMs, we instead adopt the following domain adaptation
techniques: custom tokenizers, domain-adaptive continued pretraining,
supervised fine-tuning (SFT) with domain-specific instructions, and
domain-adapted retrieval models. We evaluate these methods on three selected
LLM applications for chip design: an engineering assistant chatbot, EDA script
generation, and bug summarization and analysis. Our results show that these
domain adaptation techniques enable significant LLM performance improvements
over general-purpose base models across the three evaluated applications,
enabling up to 5x model size reduction with similar or better performance on a
range of design tasks. Our findings also indicate that there's still room for
improvement between our current results and ideal outcomes. We believe that
further investigation of domain-adapted LLM approaches will help close this gap
in the future.

ChipNeMo 通过领域自适应技术在工业芯片设计中探索了大型语言模型的应用。我们评估了这些方法在芯片设计的三个 LLM 应用上的性能，并展示了这些领域自适应技术能够显著提升 LLM 的性能，并实现模型大小减少 5 倍但在一系列设计任务上具有相似或更好的性能。我们的研究结果表明，还有进一步改进的空间，我们相信对领域自适应 LLM 方法的进一步研究将有助于填补这一差距。