328 citations · 614 across the 7 of their papers we have counts for
7 papers
Design and Implementation of Knowledge Base for Runtime Management of Software Defined Hardware
Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan +1
Runtime-reconfigurable software coupled with reconfigurable hardware is highly desirable as a means towards maximizing runtime efficiency without compromising programmability. Comp…
Decoupling the Depth and Scope of Graph Neural Networks
Hanqing Zeng, Muhan Zhang, Yinglong Xia +6
State-of-the-art Graph Neural Networks (GNNs) have limited scalability with respect to the graph and model sizes. On large graphs, increasing the model depth often means exponentia…
Deep Graph Neural Networks with Shallow Subgraph Samplers
Hanqing Zeng, Muhan Zhang, Yinglong Xia +6
While Graph Neural Networks (GNNs) are powerful models for learning representations on graphs, most state-of-the-art models do not have significant accuracy gain beyond two to thre…
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…
Accurate, Efficient and Scalable Graph Embedding
Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava +2
The Graph Convolutional Network (GCN) model and its variants are powerful graph embedding tools for facilitating classification and clustering on graphs. However, a major challenge…