A technical paper titled “ChipNeMo: Area-Tailored LLMs for Chip Design” was revealed by researchers at NVIDIA.
Summary:
“ChipNeMo goals to discover the functions of huge language fashions (LLMs) for industrial chip design. As an alternative of instantly deploying off-the-shelf industrial or open-source LLMs, we as an alternative undertake the next area adaptation strategies: customized tokenizers, domain-adaptive continued pretraining, supervised fine-tuning (SFT) with domain-specific directions, and domain-adapted retrieval fashions. We consider these strategies on three chosen LLM functions for chip design: an engineering assistant chatbot, EDA script technology, and bug summarization and evaluation. Our outcomes present that these area adaptation strategies allow vital LLM efficiency enhancements over general-purpose base fashions throughout the three evaluated functions, enabling as much as 5x mannequin dimension discount with comparable or higher efficiency on a spread of design duties. Our findings additionally point out that there’s nonetheless room for enchancment between our present outcomes and very best outcomes. We consider that additional investigation of domain-adapted LLM approaches will assist shut this hole sooner or later.”
Discover the technical paper right here. Printed October 2023.
Liu, M., Ene, T., Kirby, R., Cheng, C., Pinckney, N., Liang, R., Alben, J., Anand, H., Banerjee, S., Bayraktaroglu, Bhaskaran, B., et al. 2023. “ChipNeMo: Area-Tailored LLMs for Chip Design.” https://analysis.nvidia.com/publication/2023-10_chipnemo-domain-adapted-llms-chip-design
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