328 citations · 614 across the 7 of their papers we have counts for
3 papers · 1 filter
GraphACT: Accelerating GCN Training on CPU-FPGA Heterogeneous Platforms
Hanqing Zeng, Viktor Prasanna
Graph Convolutional Networks (GCNs) have emerged as the state-of-the-art deep learning model for representation learning on graphs. It is challenging to accelerate training of GCNs…
SPEC2: SPECtral SParsE CNN Accelerator on FPGAs
Yue Niu, Hanqing Zeng, Ajitesh Srivastava +4
To accelerate inference of Convolutional Neural Networks (CNNs), various techniques have been proposed to reduce computation redundancy. Converting convolutional layers into freque…
GraphSAINT: Graph Sampling Based Inductive Learning Method
Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava +2
Graph Convolutional Networks (GCNs) are powerful models for learning representations of attributed graphs. To scale GCNs to large graphs, state-of-the-art methods use various layer…