661 citations · 1.2k across the 7 of their papers we have counts for
7 papers
SmoothOut: Smoothing Out Sharp Minima to Improve Generalization in Deep Learning
Wei Wen, Yandan Wang, Feng Yan +4
In Deep Learning, Stochastic Gradient Descent (SGD) is usually selected as a training method because of its efficiency; however, recently, a problem in SGD gains research interest:…
TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning
Wei Wen, Cong Xu, Feng Yan +4
High network communication cost for synchronizing gradients and parameters is the well-known bottleneck of distributed training. In this work, we propose TernGrad that uses ternary…
Coordinating Filters for Faster Deep Neural Networks
Wei Wen, Cong Xu, Chunpeng Wu +3
Very large-scale Deep Neural Networks (DNNs) have achieved remarkable successes in a large variety of computer vision tasks. However, the high computation intensity of DNNs makes i…
Group Scissor: Scaling Neuromorphic Computing Design to Large Neural Networks
Yandan Wang, Wei Wen, Beiye Liu +2
Synapse crossbar is an elementary structure in Neuromorphic Computing Systems (NCS). However, the limited size of crossbars and heavy routing congestion impedes the NCS implementat…
Classification Accuracy Improvement for Neuromorphic Computing Systems with One-level Precision Synapses
Yandan Wang, Wei Wen, Linghao Song +1
Brain inspired neuromorphic computing has demonstrated remarkable advantages over traditional von Neumann architecture for its high energy efficiency and parallel data processing.…
Learning Structured Sparsity in Deep Neural Networks
Wei Wen, Chunpeng Wu, Yandan Wang +2
High demand for computation resources severely hinders deployment of large-scale Deep Neural Networks (DNN) in resource constrained devices. In this work, we propose a Structured S…
A New Learning Method for Inference Accuracy, Core Occupation, and Performance Co-optimization on TrueNorth Chip
Wei Wen, Chunpeng Wu, Yandan Wang +5
IBM TrueNorth chip uses digital spikes to perform neuromorphic computing and achieves ultrahigh execution parallelism and power efficiency. However, in TrueNorth chip, low quantiza…