no code implementations • 15 Apr 2024 • Tong Qiao, Jianlei Yang, Yingjie Qi, Ao Zhou, Chen Bai, Bei Yu, Weisheng Zhao, Chunming Hu
Graph Neural Networks (GNNs) succeed significantly in many applications recently.
no code implementations • 8 Apr 2024 • Ao Zhou, Jianlei Yang, Tong Qiao, Yingjie Qi, Zhi Yang, Weisheng Zhao, Chunming Hu
GCoDE abstracts the device communication process into an explicit operation and fuses the search of architecture and the operations mapping in a unified space for joint-optimization.
no code implementations • 31 Oct 2023 • Cenlin Duan, Jianlei Yang, Xiaolin He, Yingjie Qi, Yikun Wang, Yiou Wang, Ziyan He, Bonan Yan, Xueyan Wang, Xiaotao Jia, Weitao Pan, Weisheng Zhao
Processing-in-memory (PIM), as a novel computing paradigm, provides significant performance benefits from the aspect of effective data movement reduction.
no code implementations • 18 Oct 2023 • Yingjie Qi, Jianlei Yang, Ao Zhou, Tong Qiao, Chunming Hu
Graph neural networks (GNNs) have gained significant popularity due to the powerful capability to extract useful representations from graph data.
no code implementations • 20 Mar 2023 • Ao Zhou, Jianlei Yang, Yingjie Qi, Yumeng Shi, Tong Qiao, Weisheng Zhao, Chunming Hu
Moreover, HGNAS achieves hardware awareness during the GNN architecture design by leveraging a hardware performance predictor, which could balance the GNN model accuracy and efficiency corresponding to the characteristics of targeted devices.
no code implementations • 29 Mar 2022 • Mingjun Li, Jianlei Yang, Yingjie Qi, Meng Dong, Yuhao Yang, Runze Liu, Weitao Pan, Bei Yu, Weisheng Zhao
In this paper, Eventor is proposed as a fast and efficient EMVS accelerator by realizing the most critical and time-consuming stages including event back-projection and volumetric ray-counting on FPGA.
1 code implementation • 7 Apr 2021 • Ao Zhou, Jianlei Yang, Yeqi Gao, Tong Qiao, Yingjie Qi, Xiaoyi Wang, Yunli Chen, Pengcheng Dai, Weisheng Zhao, Chunming Hu
Graph neural networks (GNN) have achieved state-of-the-art performance on various industrial tasks.
no code implementations • ECCV 2020 • Xucheng Ye, Pengcheng Dai, Junyu Luo, Xin Guo, Yingjie Qi, Jianlei Yang, Yiran Chen
Sparsification is an efficient approach to accelerate CNN inference, but it is challenging to take advantage of sparsity in training procedure because the involved gradients are dynamically changed.