no code implementations • 16 Aug 2023 • Shuwen Lu, Zhihui Zhang, Cong Guo, Jingwen Leng, Yangjie Zhou, Minyi Guo
However, designing GNN accelerators faces two fundamental challenges: the high bandwidth requirement of GNN models and the diversity of GNN models.
no code implementations • 27 May 2023 • Yangjie Zhou, Yaoxu Song, Jingwen Leng, Zihan Liu, Weihao Cui, Zhendong Zhang, Cong Guo, Quan Chen, Li Li, Minyi Guo
Graph neural networks (GNNs) are powerful tools for exploring and learning from graph structures and features.
no code implementations • 25 Aug 2022 • Zhengyi Li, Cong Guo, Zhanda Zhu, Yangjie Zhou, Yuxian Qiu, Xiaotian Gao, Jingwen Leng, Minyi Guo
To deal with the runtime overhead, we use a coarse-grained version of the border function.
no code implementations • 18 Feb 2020 • Cong Guo, Yangjie Zhou, Jingwen Leng, Yuhao Zhu, Zidong Du, Quan Chen, Chao Li, Bin Yao, Minyi Guo
We propose Simultaneous Multi-mode Architecture (SMA), a novel architecture design and execution model that offers general-purpose programmability on DNN accelerators in order to accelerate end-to-end applications.