# ChipNeMo: Domain-Adapted LLMs for Chip Design

**Authors:** Mingjie Liu, Teodor-Dumitru Ene, Robert Kirby, Chris Cheng, Nathaniel Pinckney, Rongjian Liang, Jonah Alben, Himyanshu Anand, Sanmitra Banerjee, Ismet Bayraktaroglu, Bonita Bhaskaran, Bryan Catanzaro, Arjun Chaudhuri, Sharon Clay, Bill Dally, Laura Dang, Parikshit Deshpande, Siddhanth Dhodhi, Sameer Halepete, Eric Hill  
**arXiv:** [2311.00176](https://arxiv.org/abs/2311.00176)  
**Date:** 2023-10-31  
**Summary page:** https://www.summarizepaper.com/en/paper/2311.00176v5/

## Key points

- Project exploring use of large language models in industrial chip design
- Powerful tools utilized in ChipNeMo for specialized applications in chip design
- Strategies employed by ChipNeMo to customize LLMs for specific domains
- Focus of ChipNeMo's evaluation on tasks like chatbot assistance, EDA script generation, and bug summarization and analysis
- Domain-specific knowledge and techniques leveraged by ChipNeMo to improve performance compared to generic LLM counterparts like GPT-4

## Summary

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 A project exploring the use of large language models in industrial chip design. Powerful tools utilized in ChipNeMo for specialized applications in chip design. Strategies employed by ChipNeMo to customize LLMs for specific domains. The focus of ChipNeMo's evaluation, with tasks including chatbot assistance, EDA script generation, and bug summarization and analysis. Domain-specific knowledge and techniques leveraged by ChipNeMo to improve performance compared to generic LLM counterparts like GPT-4.