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Reconfigurable and Dynamically Transformable In-Cache-MPUF System With True Randomness Based on the SOT-MRAM

In this paper, we present a reconfigurable Physically Unclonable Functions (PUF) based on the Spin-Orbit-Torque Magnetic Random-Access …

Zhengyi Hou, Zhaohao Wang, Chao Wang, Min Wang, You Wang, Xueyan Wang, Cenlin Duan, Jianlei Yang

Reconfigurable and Dynamically Transformable In-Cache-MPUF System With True Randomness Based on the SOT-MRAM

NAND-SPIN-Based Processing-in-MRAM Architecture for Convolutional Neural Network Acceleration

The performance and efficiency of running large-scale datasets on traditional computing systems exhibit critical bottlenecks due to the …

Yinglin Zhao, Jianlei Yang, Bing Li, Xingzhou Cheng, Xucheng Ye, Xueyan Wang, Xiaotao Jia, Zhaohao Wang, Youguang Zhang, Weisheng Zhao

Accelerating Graph Connected Component Computation with Emerging Processing-In-Memory Architecture

Computing the connected component of a graph is a basic graph computing problem, which has numerous applications like graph …

Xuhang Chen, Xueyan Wang, Xiaotao Jia, Jianlei Yang, Gang Qu, Weisheng Zhao

Accelerating Graph Connected Component Computation with Emerging Processing-In-Memory Architecture

Eventor: An Efficient Event-Based Monocular Multi-View Stereo Accelerator on FPGA Platform

Event cameras are bio-inspired vision sensors that asynchronously represent pixel-level brightness changes as event streams. …

Mingjun Li, Jianlei Yang, Yingjie Qi, Meng Dong, Yuhao Yang, Runze Liu, Weitao Pan, Bei Yu, Weisheng Zhao

Eventor: An Efficient Event-Based Monocular Multi-View Stereo Accelerator on FPGA Platform

Triangle Counting Accelerations: From Algorithm to In-Memory Computing Architecture

Triangles are the basic substructure of networks and triangle counting (TC) has been a fundamental graph computing problem in numerous …

Xueyan Wang, Jianlei Yang, Yinglin Zhao, Xiaotao Jia, Rong Yin, Xuhang Chen, Gang Qu, Weisheng Zhao

Triangle Counting Accelerations: From Algorithm to In-Memory Computing Architecture

FedSkel: Efficient Federated Learning on Heterogeneous Systems with Skeleton Gradients Update

Federated learning aims to protect users’ privacy while performing data analysis from different participants. However, it is …

Junyu Luo, Jianlei Yang, Xucheng Ye, Xin Guo, Weisheng Zhao

FedSkel: Efficient Federated Learning on Heterogeneous Systems with Skeleton Gradients Update

S2Engine: A Novel Systolic Architecture for Sparse Convolutional Neural Networks

Convolutional neural networks (CNNs) have achieved great success in performing cognitive tasks. However, execution of CNNs requires a …

Jianlei Yang, Wenzhi Fu, Xingzhou Cheng, Xucheng Ye, Pengcheng Dai, Weisheng Zhao

S2Engine: A Novel Systolic Architecture for Sparse Convolutional Neural Networks

optimizing Memory Efficiency of Graph Neural Networks on Edge Computing Platforms

Graph neural networks (GNN) have achieved stateof-the-art performance on various industrial tasks. However, the poor efficiency of GNN …

Ao Zhou, Jianlei Yang, Yeqi Gao, Tong Qiao, Yingjie Qi, Xiaoyi Wang, Yunli Chen, Pengcheng Dai, Weisheng Zhao, Chunming Hu

Accelerating CNN Training by Pruning Activation Gradients

Sparsification is an efficient approach to accelerate CNN inference, but it is challenging to take advantage of sparsity in training …

Xucheng Ye, Pengcheng Dai, Junyu Luo, Xin Guo, Yingjie Qi, Jianlei Yang, Yiran Chen

Accelerating CNN Training by Pruning Activation Gradients

Computing-in-Memory Architecture Based on Field-Free SOT-MRAM with Self-Reference Method

On the current computing platforms, the memory wall between processor and memory has become the toughest challenge for the traditional …

Chao Wang, Zhaohao Wang, Yansong Xu, Jianlei Yang, Youguang Zhang

Computing-in-Memory Architecture Based on Field-Free SOT-MRAM with Self-Reference Method