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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…
LIAF-Net: Leaky Integrate and Analog Fire Network for Lightweight and Efficient Spatiotemporal Information Processing
Zhenzhi Wu, Hehui Zhang, Yihan Lin +3
Spiking neural networks (SNNs) based on Leaky Integrate and Fire (LIF) model have been applied to energy-efficient temporal and spatiotemporal processing tasks. Thanks to the bio-p…
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…
Dynamic Sparse Graph for Efficient Deep Learning
Liu Liu, Lei Deng, Xing Hu +4
We propose to execute deep neural networks (DNNs) with dynamic and sparse graph (DSG) structure for compressive memory and accelerative execution during both training and inference…
L1-Norm Batch Normalization for Efficient Training of Deep Neural Networks
Shuang Wu, Guoqi Li, Lei Deng +3
Batch Normalization (BN) has been proven to be quite effective at accelerating and improving the training of deep neural networks (DNNs). However, BN brings additional computation,…
Training and Inference with Integers in Deep Neural Networks
Shuang Wu, Guoqi Li, Feng Chen +1
Researches on deep neural networks with discrete parameters and their deployment in embedded systems have been active and promising topics. Although previous works have successfull…
GXNOR-Net: Training deep neural networks with ternary weights and activations without full-precision memory under a unified discretization framework
Lei Deng, Peng Jiao, Jing Pei +2
There is a pressing need to build an architecture that could subsume these networks under a unified framework that achieves both higher performance and less overhead. To this end,…