16 citations · 54 across the 20 of their papers we have counts for
7 papers · 1 filter
MGG: Accelerating Graph Neural Networks with Fine-grained intra-kernel Communication-Computation Pipelining on Multi-GPU Platforms
Yuke Wang, Boyuan Feng, Zheng Wang +4
The increasing size of input graphs for graph neural networks (GNNs) highlights the demand for using multi-GPU platforms. However, existing multi-GPU GNN systems optimize the compu…
GMI-DRL: Empowering Multi-GPU Deep Reinforcement Learning with GPU Spatial Multiplexing
Yuke Wang, Boyuan Feng, Zheng Wang +3
With the increasing popularity of robotics in industrial control and autonomous driving, deep reinforcement learning (DRL) raises the attention of various fields. However, DRL comp…
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…
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…
GNNAdvisor: An Adaptive and Efficient Runtime System for GNN Acceleration on GPUs
Yuke Wang, Boyuan Feng, Gushu Li +4
As the emerging trend of graph-based deep learning, Graph Neural Networks (GNNs) excel for their capability to generate high-quality node feature vectors (embeddings). However, the…
AccD: A Compiler-based Framework for Accelerating Distance-related Algorithms on CPU-FPGA Platforms
Yuke Wang, Boyuan Feng, Gushu Li +3
As a promising solution to boost the performance of distance-related algorithms (e.g., K-means and KNN), FPGA-based acceleration attracts lots of attention, but also comes with num…
KPynq: A Work-Efficient Triangle-Inequality based K-means on FPGA
Yuke Wang, Zhaorui Zeng, Boyuan Feng +2
K-means is a popular but computation-intensive algorithm for unsupervised learning. To address this issue, we present KPynq, a work-efficient triangle-inequality based K-means on F…