142 citations · 552 across the 32 of their papers we have counts for
4 papers · 1 filter
Rapid-INR: Storage Efficient CPU-free DNN Training Using Implicit Neural Representation
Hanqiu Chen, Hang Yang, Stephen Fitzmeyer +1
Implicit Neural Representation (INR) is an innovative approach for representing complex shapes or objects without explicitly defining their geometry or surface structure. Instead,…
DGNN-Booster: A Generic FPGA Accelerator Framework For Dynamic Graph Neural Network Inference
Hanqiu Chen, Cong Hao
Dynamic Graph Neural Networks (DGNNs) are becoming increasingly popular due to their effectiveness in analyzing and predicting the evolution of complex interconnected graph-based s…
GNNBuilder: An Automated Framework for Generic Graph Neural Network Accelerator Generation, Simulation, and Optimization
Stefan Abi-Karam, Cong Hao
There are plenty of graph neural network (GNN) accelerators being proposed. However, they highly rely on users' hardware expertise and are usually optimized for one specific GNN mo…
Gamora: Graph Learning based Symbolic Reasoning for Large-Scale Boolean Networks
Nan Wu, Yingjie Li, Cong Hao +3
Reasoning high-level abstractions from bit-blasted Boolean networks (BNs) such as gate-level netlists can significantly benefit functional verification, logic minimization, datapat…