10 citations · 18 across the 3 of their papers we have counts for
3 papers
cs.LG2024★ 7 cited
FastGL: A GPU-Efficient Framework for Accelerating Sampling-Based GNN Training at Large Scale
Zeyu Zhu, Peisong Wang, Qinghao Hu +3
Graph Neural Networks (GNNs) have shown great superiority on non-Euclidean graph data, achieving ground-breaking performance on various graph-related tasks. As a practical solution…
cs.AR2022★ 1 cited
RePAST: A ReRAM-based PIM Accelerator for Second-order Training of DNN
Yilong Zhao, Li Jiang, Mingyu Gao +6
The second-order training methods can converge much faster than first-order optimizers in DNN training. This is because the second-order training utilizes the inversion of the seco…
cs.LG2022★ 10 cited
BayesFT: Bayesian Optimization for Fault Tolerant Neural Network Architecture
Nanyang Ye, Jingbiao Mei, Zhicheng Fang +4
To deploy deep learning algorithms on resource-limited scenarios, an emerging device-resistive random access memory (ReRAM) has been regarded as promising via analog computing. How…