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 GPT-3.5

CurveFormer++: 3D Lane Detection by Curve Propagation with Temporal Curve Queries and Attention

no code implementations • 9 Feb 2024 • Yifeng Bai, Zhirong Chen, Pengpeng Liang, Erkang Cheng

A curve cross-attention module is introduced in the Transformer decoder to calculate similarities between image features and curve queries of lanes.

3D Lane Detection Autonomous Driving

CurveFormer: 3D Lane Detection by Curve Propagation with Curve Queries and Attention

no code implementations • 16 Sep 2022 • Yifeng Bai, Zhirong Chen, Zhangjie Fu, Lang Peng, Pengpeng Liang, Erkang Cheng

In this paper, we propose CurveFormer, a single-stage Transformer-based method that directly calculates 3D lane parameters and can circumvent the difficult view transformation step.

3D Lane Detection Autonomous Driving

BEVSegFormer: Bird's Eye View Semantic Segmentation From Arbitrary Camera Rigs

no code implementations • 8 Mar 2022 • Lang Peng, Zhirong Chen, Zhangjie Fu, Pengpeng Liang, Erkang Cheng

Semantic segmentation in bird's eye view (BEV) is an important task for autonomous driving.

Autonomous Driving Bird's-Eye View Semantic Segmentation +1