46 citations · 114 across the 16 of their papers we have counts for
8 papers · 1 filter
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…
Towards Efficient Full 8-bit Integer DNN Online Training on Resource-limited Devices without Batch Normalization
Yukuan Yang, Xiaowei Chi, Lei Deng +3
Huge computational costs brought by convolution and batch normalization (BN) have caused great challenges for the online training and corresponding applications of deep neural netw…
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…
A Comprehensive and Modularized Statistical Framework for Gradient Norm Equality in Deep Neural Networks
Zhaodong Chen, Lei Deng, Bangyan Wang +2
In recent years, plenty of metrics have been proposed to identify networks that are free of gradient explosion and vanishing. However, due to the diversity of network components an…
Training High-Performance and Large-Scale Deep Neural Networks with Full 8-bit Integers
Yukuan Yang, Shuang Wu, Lei Deng +3
Deep neural network (DNN) quantization converting floating-point (FP) data in the network to integers (INT) is an effective way to shrink the model size for memory saving and simpl…
Batch Normalization Sampling
Zhaodong Chen, Lei Deng, Guoqi Li +4
Deep Neural Networks (DNNs) thrive in recent years in which Batch Normalization (BN) plays an indispensable role. However, it has been observed that BN is costly due to the reducti…