4 citations · 11 across the 12 of their papers we have counts for
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cs.LG2023
GNNHLS: Evaluating Graph Neural Network Inference via High-Level Synthesis
Chenfeng Zhao, Zehao Dong, Yixin Chen +2
With the ever-growing popularity of Graph Neural Networks (GNNs), efficient GNN inference is gaining tremendous attention. Field-Programming Gate Arrays (FPGAs) are a promising exe…
cs.LG2023★ 3 cited
Rethinking the Power of Graph Canonization in Graph Representation Learning with Stability
Zehao Dong, Muhan Zhang, Philip R. O. Payne +5
The expressivity of Graph Neural Networks (GNNs) has been studied broadly in recent years to reveal the design principles for more powerful GNNs. Graph canonization is known as a t…