ChipNeMo: Domain-Adapted LLMs for Chip Design

TL;DR

ChipNeMo demonstrates that domain-adaptive training of large language models significantly improves their performance in chip design tasks, surpassing general models like GPT-4 in specific applications.

Contribution

The paper introduces domain adaptation techniques for LLMs tailored to chip design, achieving superior task performance without sacrificing general capabilities.

Findings

ChipNeMo-70B outperforms GPT-4 in engineering chatbot and script generation.

Domain-adaptive pretraining enhances LLM performance in chip design tasks.

Models maintain generic capabilities despite domain specialization.

Abstract

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: domain-adaptive tokenization, domain-adaptive continued pretraining, model alignment 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 evaluations demonstrate that domain-adaptive pretraining of language models, can lead to superior performance in domain related downstream tasks compared to their base LLaMA2 counterparts, without degradations in generic capabilities. In particular, our largest model, ChipNeMo-70B, outperforms the highly capable GPT-4 on two of our use cases, namely engineering assistant chatbot and EDA scripts generation, while exhibiting competitive performance on bug summarization and analysis. These results underscore the potential of domain-specific customization for enhancing the effectiveness of large language models in specialized applications.