661 citations · 2k across the 35 of their papers we have counts for
7 papers · 1 filter
GraphR: Accelerating Graph Processing Using ReRAM
Linghao Song, Youwei Zhuo, Xuehai Qian +2
This paper presents GRAPHR, the first ReRAM-based graph processing accelerator. GRAPHR follows the principle of near-data processing and explores the opportunity of performing mass…
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…
A Compact DNN: Approaching GoogLeNet-Level Accuracy of Classification and Domain Adaptation
Chunpeng Wu, Wei Wen, Tariq Afzal +3
Recently, DNN model compression based on network architecture design, e.g., SqueezeNet, attracted a lot attention. No accuracy drop on image classification is observed on these ext…
Generative Poisoning Attack Method Against Neural Networks
Chaofei Yang, Qing Wu, Hai Li +1
Poisoning attack is identified as a severe security threat to machine learning algorithms. In many applications, for example, deep neural network (DNN) models collect public data a…
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.…