20 citations · 40 across the 4 of their papers we have counts for
4 papers
NAND-SPIN-Based Processing-in-MRAM Architecture for Convolutional Neural Network Acceleration
Yinglin Zhao, Jianlei Yang, Bing Li +7
The performance and efficiency of running large-scale datasets on traditional computing systems exhibit critical bottlenecks due to the existing "power wall" and "memory wall" prob…
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…
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…
TCIM: Triangle Counting Acceleration With Processing-In-MRAM Architecture
Xueyan Wang, Jianlei Yang, Yinglin Zhao +7
Triangle counting (TC) is a fundamental problem in graph analysis and has found numerous applications, which motivates many TC acceleration solutions in the traditional computing p…