Researchers present ChipNeMo, using domain adaptation to enhance LLMs for chip design, achieving up to 5x model size reduction with better performance.
Authors: Mingjie Liu, NVIDIA {Equal contribution}; Teodor-Dumitru Ene, NVIDIA {Equal contribution}; Robert Kirby, NVIDIA {Equal contribution}; Chris Cheng, NVIDIA {Equal contribution}; Nathaniel Pinckney, NVIDIA {Equal contribution}; Rongjian Liang, NVIDIA {Equal contribution}; Jonah Alben, NVIDIA; Himyanshu Anand, NVIDIA; Sanmitra Banerjee, NVIDIA; Ismet Bayraktaroglu, NVIDIA; Bonita Bhaskaran, NVIDIA; Bryan Catanzaro, NVIDIA; Arjun Chaudhuri, NVIDIA; Sharon Clay, NVIDIA; Bill Dally, NVIDIA;...
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.
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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