20 citations · 49 across the 5 of their papers we have counts for
6 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…
FedSkel: Efficient Federated Learning on Heterogeneous Systems with Skeleton Gradients Update
Junyu Luo, Jianlei Yang, Xucheng Ye +2
Federated learning aims to protect users' privacy while performing data analysis from different participants. However, it is challenging to guarantee the training efficiency on het…
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…
RoSearch: Search for Robust Student Architectures When Distilling Pre-trained Language Models
Xin Guo, Jianlei Yang, Haoyi Zhou +2
Pre-trained language models achieve outstanding performance in NLP tasks. Various knowledge distillation methods have been proposed to reduce the heavy computation and storage requ…
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…