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Found 6 papers, 0 papers with code
Efficient Prompting Methods for Large Language Models: A Survey
In this paper, we present a comprehensive overview of these methods.
Data is all you need: Finetuning LLMs for Chip Design via an Automated design-data augmentation framework
Our 13B model (ChipGPT-FT) has a pass rate improvement compared with GPT-3. 5 in Verilog generation and outperforms in EDA script (i. e., SiliconCompiler) generation with only 200 EDA script data.
Learning Evaluation Models from Large Language Models for Sequence Generation
Large language models achieve state-of-the-art performance on sequence generation evaluation, but typically have a large number of parameters.
ChipGPT: How far are we from natural language hardware design
As large language models (LLMs) like ChatGPT exhibited unprecedented machine intelligence, it also shows great performance in assisting hardware engineers to realize higher-efficiency logic design via natural language interaction.
ArchNet: Data Hiding Model in Distributed Machine Learning System
Specifically, our design can improve the accuracy on MNIST up to 97. 26% compared with RC4. The accuracies on the datasets encrypted by ArchNet are 97. 26%, 84. 15% and 79. 80%, and they are 97. 31%, 82. 31% and 80. 22% on the original datasets, which shows that the encrypted accuracy of ArchNet has the same performance as the base model.
Team Performance Evaluation Model based on Network Feature Extraction
Teamwork is increasingly important in today's society.
Social and Information Networks Computers and Society
