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DecGNN: a framework for mapping decoupled GNN models onto CPU-FPGA heterogeneous platform
Bingyi Zhang, Hanqing Zeng, Viktor Prasanna
Decoupling the depth and scope of Graph Neural Networks

Old GNN models + New data perspective = Surpassing 1-WL, Avoiding oversmoothing & Overcoming neighborhood explosion
Hanqing Zeng, Muhan Zhang, Yinglong Xia, Ajitesh Srivastava, Andrey Malevich, Rajgopal Kannan, Viktor Prasanna, Long Jin, Ren Chen
Decoupling the depth and scope of Graph Neural Networks
Accelerating large scale real-time GNN inference using channel pruning
Hongkuan Zhou, Ajitesh Srivastava, Hanqing Zeng, Rajgopal Kannan, Viktor Prasanna
Accurate, efficient and scalable training of Graph Neural Networks
Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, Viktor Prasanna
Hardware acceleration of large scale GCN inference
Bingyi Zhang, Hanqing Zeng, Viktor Prasanna
Accelerating large scale GCN inference on FPGA
Bingyi Zhang, Hanqing Zeng, Viktor Prasanna
GraphSAINT: Graph sampling based inductive learning method
Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, Viktor Prasanna
GraphSAINT: Graph sampling based inductive learning method
GraphACT: Accelerating GCN training on CPU-FPGA heterogeneous platforms
Hanqing Zeng, Viktor Prasanna
GraphACT: Accelerating GCN training on CPU-FPGA heterogeneous platforms
SPEC2: Spectral sparse CNN accelerator on FPGAs
Yue Niu, Hanqing Zeng, Ajitesh Srivastava, Kartik Lakhotia, Rajgopal Kannan, Yanzhi Wang, Viktor Prasanna
A flexible design automation tool for accelerating quantized spectral CNNs
Rachit Rajat, Hanqing Zeng, Viktor Prasanna