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;...
significantly improved domain benchmarks with any tokenizer, including Verilog coding . We conclude that augmenting the tokenizer comes with the benefit of improved tokenizer and training efficiency with no degradation on the models general language and domain capabilities. Public Datasets Mix-in: As introduced in Section II-A we included public data in
hyperparameters. However, we note substantial degradation across natural language benchmarks as shown in Table XII, including in-domain chip design. Coding capabilities improved as consistent with the findings of . We highlight that our case differs from that in .
counterparts, with the larger model exhibiting slightly better results. C. Retrieval Model Training Manually generating training samples is very effort intensive, so we elected to implement a process to generate them automatically. Since we are using contrastive learning to finetune our model, each sample requires a set of both positive passages and negative passages, particularly hard negatives to maximize the accuracy.
with 1 epoch. Tokenizer Augmentation: Table IX presents aggregated auto evaluation benchmark results. We note that careful tokenizer augmentation and weight initialization only slightly impacts model performance on general academic benchmarks.
Singapore Latest News, Singapore Headlines
Similar News:You can also read news stories similar to this one that we have collected from other news sources.
ChipNeMo: Domain-Adapted LLMs for Chip Design: ChipNemo Domain Adaptation MethodsResearchers present ChipNeMo, using domain adaptation to enhance LLMs for chip design, achieving up to 5x model size reduction with better performance.
Read more »
ChipNeMo: Domain-Adapted LLMs for Chip Design: LLM ApplicationsResearchers present ChipNeMo, using domain adaptation to enhance LLMs for chip design, achieving up to 5x model size reduction with better performance.
Read more »
ChipNeMo: Domain-Adapted LLMs for Chip Design: Abstract and IntroResearchers present ChipNeMo, using domain adaptation to enhance LLMs for chip design, achieving up to 5x model size reduction with better performance.
Read more »
ChipNeMo: Domain-Adapted LLMs for Chip Design: EvaluationsResearchers present ChipNeMo, using domain adaptation to enhance LLMs for chip design, achieving up to 5x model size reduction with better performance.
Read more »
ChipNeMo: Domain-Adapted LLMs for Chip Design: DatasetResearchers present ChipNeMo, using domain adaptation to enhance LLMs for chip design, achieving up to 5x model size reduction with better performance.
Read more »
ChipNeMo: Domain-Adapted LLMs for Chip Design: DiscussionResearchers present ChipNeMo, using domain adaptation to enhance LLMs for chip design, achieving up to 5x model size reduction with better performance.
Read more »