ChipNeMo: Domain-Adapted LLMs for Chip Design: Conclusions
by Writings, Papers and Blogs on Text ModelsJune 6th, 2024
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Researchers present ChipNeMo, using domain adaptation to enhance LLMs for chip design, achieving up to 5x model size reduction with better performance.
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
- Abstract and Intro
- Dataset
- ChipNemo Domain Adaptation Methods
- LLM Applications
- Evaluations
- Discussion
- Related Works
- Conclusions
- Acknowledgments, Contributions and References
- Appendix
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.
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