Core Concepts
Large language models can be effectively adapted for specialized chip design tasks through domain-specific pretraining and alignment techniques.
Abstract
ChipNeMo explores the use of large language models (LLMs) for industrial chip design.
Domain adaptation techniques include domain-adaptive tokenization, continued pretraining, model alignment, and retrieval models.
Evaluation on engineering assistant chatbot, EDA script generation, and bug analysis shows superior performance of domain-adapted models.
Domain-adapted LLMs demonstrate potential for enhancing specialized applications in chip design.
Training methods, ablation studies, and cost analysis are detailed.
Related works and future directions in the field are discussed.
Stats
ChipNeMo-70B outperforms GPT-4 on engineering assistant chatbot and EDA script generation.
Domain-adaptive tokenization reduces domain data token count by up to 3.3%.
Fine-tuning ChipNeMo retrieval model with domain-specific data improves retriever hit rate by 30%.
Quotes
"Domain-adaptive pretraining was the primary technique driving enhanced performance in domain-specific tasks."
"Our results show that domain-adaptive pretrained models achieve similar or better results than base LLaMA2 models with minimal additional pretraining compute cost."