Data is all you need: Finetuning LLMs for Chip Design via an Automated design-data augmentation framework

no code implementations • 17 Mar 2024 • Kaiyan Chang, Kun Wang, Nan Yang, Ying Wang, Dantong Jin, Wenlong Zhu, Zhirong Chen, Cangyuan Li, Hao Yan, Yunhao Zhou, Zhuoliang Zhao, Yuan Cheng, Yudong Pan, Yiqi Liu, Mengdi Wang, Shengwen Liang, Yinhe Han, Huawei Li, Xiaowei Li

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.

Data Augmentation

Moving Towards Centers: Re-ranking with Attention and Memory for Re-identification

no code implementations • 4 May 2021 • Yunhao Zhou, Yi Wang, Lap-Pui Chau

Specifically, all the feature embeddings of query and gallery images are expanded and enhanced by a linear combination of their neighbors, with the correlation prediction serving as discriminative combination weights.

Re-Ranking Retrieval +1

Rethinking and Designing a High-performing Automatic License Plate Recognition Approach

no code implementations • 30 Nov 2020 • Yi Wang, Zhen-Peng Bian, Yunhao Zhou, Lap-Pui Chau

Our study illustrates the outstanding design of ALPR with four insights: (1) the resampling-based cascaded framework is beneficial to both speed and accuracy; (2) the highly efficient license plate recognition should abundant additional character segmentation and recurrent neural network (RNN), but adopt a plain convolutional neural network (CNN); (3) in the case of CNN, taking advantage of vertex information on license plates improves the recognition performance; and (4) the weight-sharing character classifier addresses the lack of training images in small-scale datasets.

Data Augmentation License Plate Detection +2