5 citations · 13 across the 11 of their papers we have counts for
4 papers · 1 filter
MaxK-GNN: Extremely Fast GPU Kernel Design for Accelerating Graph Neural Networks Training
Hongwu Peng, Xi Xie, Kaustubh Shivdikar +6
In the acceleration of deep neural network training, the GPU has become the mainstream platform. GPUs face substantial challenges on GNNs, such as workload imbalance and memory acc…
Accel-GCN: High-Performance GPU Accelerator Design for Graph Convolution Networks
Xi Xie, Hongwu Peng, Amit Hasan +7
Graph Convolutional Networks (GCNs) are pivotal in extracting latent information from graph data across various domains, yet their acceleration on mainstream GPUs is challenged by…
AutoReP: Automatic ReLU Replacement for Fast Private Network Inference
Hongwu Peng, Shaoyi Huang, Tong Zhou +11
The growth of the Machine-Learning-As-A-Service (MLaaS) market has highlighted clients' data privacy and security issues. Private inference (PI) techniques using cryptographic prim…
RRNet: Towards ReLU-Reduced Neural Network for Two-party Computation Based Private Inference
Hongwu Peng, Shanglin Zhou, Yukui Luo +11
The proliferation of deep learning (DL) has led to the emergence of privacy and security concerns. To address these issues, secure Two-party computation (2PC) has been proposed as…