29 citations · 49 across the 4 of their papers we have counts for
6 papers
S2Engine: A Novel Systolic Architecture for Sparse Convolutional Neural Networks
Jianlei Yang, Wenzhi Fu, Xingzhou Cheng +3
Convolutional neural networks (CNNs) have achieved great success in performing cognitive tasks. However, execution of CNNs requires a large amount of computing resources and genera…
Optimizing Memory Efficiency of Graph Neural Networks on Edge Computing Platforms
Ao Zhou, Jianlei Yang, Yeqi Gao +7
Graph neural networks (GNN) have achieved state-of-the-art performance on various industrial tasks. However, the poor efficiency of GNN inference and frequent Out-Of-Memory (OOM) p…
SparseTrain: Exploiting Dataflow Sparsity for Efficient Convolutional Neural Networks Training
Pengcheng Dai, Jianlei Yang, Xucheng Ye +5
Training Convolutional Neural Networks (CNNs) usually requires a large number of computational resources. In this paper, \textit{SparseTrain} is proposed to accelerate CNN training…
Accelerating CNN Training by Pruning Activation Gradients
Xucheng Ye, Pengcheng Dai, Junyu Luo +4
Sparsification is an efficient approach to accelerate CNN inference, but it is challenging to take advantage of sparsity in training procedure because the involved gradients are dy…
SPINBIS: Spintronics based Bayesian Inference System with Stochastic Computing
Xiaotao Jia, Jianlei Yang, Pengcheng Dai +3
Bayesian inference is an effective approach for solving statistical learning problems, especially with uncertainty and incompleteness. However, Bayesian inference is a computing-in…
A Scalable Pipelined Dataflow Accelerator for Object Region Proposals on FPGA Platform
Wenzhi Fu, Jianlei Yang, Pengcheng Dai +2
Region proposal is critical for object detection while it usually poses a bottleneck in improving the computation efficiency on traditional control-flow architectures. We have obse…