17 citations · 27 across the 9 of their papers we have counts for
11 papers
Characterizing and Understanding Distributed GNN Training on GPUs
Haiyang Lin, Mingyu Yan, Xiaocheng Yang +4
Graph neural network (GNN) has been demonstrated to be a powerful model in many domains for its effectiveness in learning over graphs. To scale GNN training for large graphs, a wid…
Alleviating Datapath Conflicts and Design Centralization in Graph Analytics Acceleration
Haiyang Lin, Mingyu Yan, Duo Wang +5
Previous graph analytics accelerators have achieved great improvement on throughput by alleviating irregular off-chip memory accesses. However, on-chip side datapath conflicts and…
Survey on Graph Neural Network Acceleration: An Algorithmic Perspective
Xin Liu, Mingyu Yan, Lei Deng +5
Graph neural networks (GNNs) have been a hot spot of recent research and are widely utilized in diverse applications. However, with the use of huger data and deeper models, an urge…
Tackling Variabilities in Autonomous Driving
Yuqiong Qi, Yang Hu, Haibin Wu +5
The state-of-the-art driving automation system demands extreme computational resources to meet rigorous accuracy and latency requirements. Though emerging driving automation comput…
RISC-NN: Use RISC, NOT CISC as Neural Network Hardware Infrastructure
Taoran Xiang, Lunkai Zhang, Shuqian An +9
Neural Networks (NN) have been proven to be powerful tools to analyze Big Data. However, traditional CPUs cannot achieve the desired performance and/or energy efficiency for NN app…
Sampling methods for efficient training of graph convolutional networks: A survey
Xin Liu, Mingyu Yan, Lei Deng +3
Graph Convolutional Networks (GCNs) have received significant attention from various research fields due to the excellent performance in learning graph representations. Although GC…