ChipNeMo: Domain-Adapted LLMs for Chip Design: Conclusions

Written by textmodels | Published 2024/06/06
Tech Story Tags: chipnemo | chip-design | llms | domain-adaptation | custom-tokenizers | bug-summarization | eda-script-generation | llm-performance-evaluation

TLDRResearchers present ChipNeMo, using domain adaptation to enhance LLMs for chip design, achieving up to 5x model size reduction with better performance.via the TL;DR App

Authors:

(1) Mingjie Liu, NVIDIA {Equal contribution};

(2) Teodor-Dumitru Ene, NVIDIA {Equal contribution};

(3) Robert Kirby, NVIDIA {Equal contribution};

(4) Chris Cheng, NVIDIA {Equal contribution};

(5) Nathaniel Pinckney, NVIDIA {Equal contribution};

(6) Rongjian Liang, NVIDIA {Equal contribution};

(7) Jonah Alben, NVIDIA;

(8) Himyanshu Anand, NVIDIA;

(9) Sanmitra Banerjee, NVIDIA;

(10) Ismet Bayraktaroglu, NVIDIA;

(11) Bonita Bhaskaran, NVIDIA;

(12) Bryan Catanzaro, NVIDIA;

(13) Arjun Chaudhuri, NVIDIA;

(14) Sharon Clay, NVIDIA;

(15) Bill Dally, NVIDIA;

(16) Laura Dang, NVIDIA;

(17) Parikshit Deshpande, NVIDIA;

(18) Siddhanth Dhodhi, NVIDIA;

(19) Sameer Halepete, NVIDIA;

(20) Eric Hill, NVIDIA;

(21) Jiashang Hu, NVIDIA;

(22) Sumit Jain, NVIDIA;

(23) Brucek Khailany, NVIDIA;

(24) George Kokai, NVIDIA;

(25) Kishor Kunal, NVIDIA;

(26) Xiaowei Li, NVIDIA;

(27) Charley Lind, NVIDIA;

(28) Hao Liu, NVIDIA;

(29) Stuart Oberman, NVIDIA;

(30) Sujeet Omar, NVIDIA;

(31) Sreedhar Pratty, NVIDIA;

(23) Jonathan Raiman, NVIDIA;

(33) Ambar Sarkar, NVIDIA;

(34) Zhengjiang Shao, NVIDIA;

(35) Hanfei Sun, NVIDIA;

(36) Pratik P Suthar, NVIDIA;

(37) Varun Tej, NVIDIA;

(38) Walker Turner, NVIDIA;

(39) Kaizhe Xu, NVIDIA;

(40) Haoxing Ren, NVIDIA.

Table of Links

VIII. CONCLUSIONS

The authors would like to thank: NVIDIA IT teams for their support on NVBugs integration; NVIDIA Hardware Security team for their support on security issues; NVIDIA NeMo teams for their support and guidance on training and inference of ChipNeMo models; NVIDIA Infrastructure teams for supporting the GPU training and inference resources for the project; NVIDIA Hardware design teams for their support and insight.

This paper is available on arxiv under CC 4.0 license.


Written by textmodels | We publish the best academic papers on rule-based techniques, LLMs, & the generation of text that resembles human text.
Published by HackerNoon on 2024/06/06