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;...
Considerations for Domain Adaptation Although domain-adapted ChipNeMo models achieve significant improvements over their corresponding foundation models, we also observe that the larger LLaMA2 70B can sometimes achieve similar accuracy as ChipNeMo, as seen in Figures 8, 9, and 10. Recent work has leveraged these powerful models to perform chip design tasks. However, it is important to consider the cost-efficiency benefits gained from the use of a smaller model. Pope et al.
Considerations for Domain Adaptation A. Considerations for Domain Adaptation Although domain-adapted ChipNeMo models achieve significant improvements over their corresponding foundation models, we also observe that the larger LLaMA2 70B can sometimes achieve similar accuracy as ChipNeMo, as seen in Figures 8, 9, and 10. Recent work has leveraged these powerful models to perform chip design tasks.
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