328 citations · 553 across the 5 of their papers we have counts for
5 papers
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…
Stochastic Sparse Learning with Momentum Adaptation for Imprecise Memristor Networks
Yaoyuan Wang, Shuang Wu, Ziyang Zhang +2
Memristor based neural networks have great potentials in on-chip neuromorphic computing systems due to the fast computation and low-energy consumption. However, the imprecise prope…
Convolution with even-sized kernels and symmetric padding
Shuang Wu, Guanrui Wang, Pei Tang +2
Compact convolutional neural networks gain efficiency mainly through depthwise convolutions, expanded channels and complex topologies, which contrarily aggravate the training proce…
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…