17 citations · 65 across the 25 of their papers we have counts for
25 papers
Characterizing and Understanding HGNN Training on GPUs
Dengke Han, Mingyu Yan, Xiaochun Ye +1
Owing to their remarkable representation capabilities for heterogeneous graph data, Heterogeneous Graph Neural Networks (HGNNs) have been widely adopted in many critical real-world…
ADE-HGNN: Accelerating HGNNs through Attention Disparity Exploitation
Dengke Han, Meng Wu, Runzhen Xue +3
Heterogeneous Graph Neural Networks (HGNNs) have recently demonstrated great power in handling heterogeneous graph data, rendering them widely applied in many critical real-world d…
Disttack: Graph Adversarial Attacks Toward Distributed GNN Training
Yuxiang Zhang, Xin Liu, Meng Wu +4
Graph Neural Networks (GNNs) have emerged as potent models for graph learning. Distributing the training process across multiple computing nodes is the most promising solution to a…
GDR-HGNN: A Heterogeneous Graph Neural Networks Accelerator Frontend with Graph Decoupling and Recoupling
Runzhen Xue, Mingyu Yan, Dengke Han +4
Heterogeneous Graph Neural Networks (HGNNs) have broadened the applicability of graph representation learning to heterogeneous graphs. However, the irregular memory access pattern…
Revisiting Edge Perturbation for Graph Neural Network in Graph Data Augmentation and Attack
Xin Liu, Yuxiang Zhang, Meng Wu +6
Edge perturbation is a basic method to modify graph structures. It can be categorized into two veins based on their effects on the performance of graph neural networks (GNNs), i.e.…
A Survey of Graph Pre-processing Methods: From Algorithmic to Hardware Perspectives
Zhengyang Lv, Mingyu Yan, Xin Liu +4
Graph-related applications have experienced significant growth in academia and industry, driven by the powerful representation capabilities of graph. However, efficiently executing…
HiHGNN: Accelerating HGNNs through Parallelism and Data Reusability Exploitation
Runzhen Xue, Dengke Han, Mingyu Yan +8
Heterogeneous graph neural networks (HGNNs) have emerged as powerful algorithms for processing heterogeneous graphs (HetGs), widely used in many critical fields. To capture both st…
A Comprehensive Survey on Distributed Training of Graph Neural Networks
Haiyang Lin, Mingyu Yan, Xiaochun Ye +4
Graph neural networks (GNNs) have been demonstrated to be a powerful algorithmic model in broad application fields for their effectiveness in learning over graphs. To scale GNN tra…
Rethinking Efficiency and Redundancy in Training Large-scale Graphs
Xin Liu, Xunbin Xiong, Mingyu Yan +4
Large-scale graphs are ubiquitous in real-world scenarios and can be trained by Graph Neural Networks (GNNs) to generate representation for downstream tasks. Given the abundant inf…
Characterizing and Understanding HGNNs on GPUs
Mingyu Yan, Mo Zou, Xiaocheng Yang +4
Heterogeneous graph neural networks (HGNNs) deliver powerful capacity in heterogeneous graph representation learning. The execution of HGNNs is usually accelerated by GPUs. Therefo…
Multi-node Acceleration for Large-scale GCNs
Gongjian Sun, Mingyu Yan, Duo Wang +5
Limited by the memory capacity and compute power, singe-node graph convolutional neural network (GCN) accelerators cannot complete the execution of GCNs within a reasonable amount…
Simple and Efficient Heterogeneous Graph Neural Network
Xiaocheng Yang, Mingyu Yan, Shirui Pan +2
Heterogeneous graph neural networks (HGNNs) have powerful capability to embed rich structural and semantic information of a heterogeneous graph into node representations. Existing…
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…
GNNSampler: Bridging the Gap between Sampling Algorithms of GNN and Hardware
Xin Liu, Mingyu Yan, Shuhan Song +5
Sampling is a critical operation in Graph Neural Network (GNN) training that helps reduce the cost. Previous literature has explored improving sampling algorithms via mathematical…
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…
Video Face Recognition System: RetinaFace-mnet-faster and Secondary Search
Qian Li, Nan Guo, Xiaochun Ye +2
Face recognition is widely used in the scene. However, different visual environments require different methods, and face recognition has a difficulty in complex environments. There…
Top-Related Meta-Learning Method for Few-Shot Object Detection
Qian Li, Nan Guo, Xiaochun Ye +3
Many meta-learning methods are proposed for few-shot detection. However, previous most methods have two main problems, poor detection APs, and strong bias because of imbalance and…
Characterizing and Understanding GCNs on GPU
Mingyu Yan, Zhaodong Chen, Lei Deng +4
Graph convolutional neural networks (GCNs) have achieved state-of-the-art performance on graph-structured data analysis. Like traditional neural networks, training and inference of…
HyGCN: A GCN Accelerator with Hybrid Architecture
Mingyu Yan, Lei Deng, Xing Hu +6
In this work, we first characterize the hybrid execution patterns of GCNs on Intel Xeon CPU. Guided by the characterization, we design a GCN accelerator, HyGCN, using a hybrid arch…
Pixel-Semantic Revise of Position Learning A One-Stage Object Detector with A Shared Encoder-Decoder
Qian Li, Nan Guo, Xiaochun Ye +2
Recently, many methods have been proposed for object detection. They cannot detect objects by semantic features, adaptively. In this work, according to channel and spatial attentio…