19 citations · 82 across the 18 of their papers we have counts for
5 papers · 1 filter
QGTC: Accelerating Quantized Graph Neural Networks via GPU Tensor Core
Yuke Wang, Boyuan Feng, Yufei Ding
Over the most recent years, quantized graph neural network (QGNN) attracts lots of research and industry attention due to its high robustness and low computation and memory overhea…
TC-GNN: Bridging Sparse GNN Computation and Dense Tensor Cores on GPUs
Yuke Wang, Boyuan Feng, Zheng Wang +2
Recently, graph neural networks (GNNs), as the backbone of graph-based machine learning, demonstrate great success in various domains (e.g., e-commerce). However, the performance o…
Towards Efficient Ansatz Architecture for Variational Quantum Algorithms
Anbang Wu, Gushu Li, Yuke Wang +3
Variational quantum algorithms are expected to demonstrate the advantage of quantum computing on near-term noisy quantum computers. However, training such variational quantum algor…
APNN-TC: Accelerating Arbitrary Precision Neural Networks on Ampere GPU Tensor Cores
Boyuan Feng, Yuke Wang, Tong Geng +2
Over the years, accelerating neural networks with quantization has been widely studied. Unfortunately, prior efforts with diverse precisions (e.g., 1-bit weights and 2-bit activati…
DSXplore: Optimizing Convolutional Neural Networks via Sliding-Channel Convolutions
Yuke Wang, Boyuan Feng, Yufei Ding
As the key advancement of the convolutional neural networks (CNNs), depthwise separable convolutions (DSCs) are becoming one of the most popular techniques to reduce the computatio…