no code implementations • 25 Jul 2018 • Ling Liang, Lei Deng, Yueling Zeng, Xing Hu, Yu Ji, Xin Ma, Guoqi Li, Yuan Xie
Crossbar architecture based devices have been widely adopted in neural network accelerators by taking advantage of the high efficiency on vector-matrix multiplication (VMM) operations.
no code implementations • NeurIPS 2018 • Yu Ji, Ling Liang, Lei Deng, Youyang Zhang, Youhui Zhang, Yuan Xie
Increasing the sparsity granularity can lead to better hardware utilization, but it will compromise the sparsity for maintaining accuracy.
no code implementations • 10 Mar 2019 • Xing Hu, Ling Liang, Lei Deng, Shuangchen Li, Xinfeng Xie, Yu Ji, Yufei Ding, Chang Liu, Timothy Sherwood, Yuan Xie
As neural networks continue their reach into nearly every aspect of software operations, the details of those networks become an increasingly sensitive subject.
Cryptography and Security Hardware Architecture
no code implementations • ICLR 2019 • Zhaodong Chen, Lei Deng, Guoqi Li, Jiawei Sun, Xing Hu, Ling Liang, YufeiDing, Yuan Xie
We identify that the effectiveness expects less data correlation while the efficiency expects regular execution pattern.
no code implementations • 12 Aug 2019 • Zhaohong Deng, Chen Cui, Peng Xu, Ling Liang, Haoran Chen, Te Zhang, Shitong Wang
How to exploit the relation-ship between different views effectively using the characteristic of multi-view data has become a crucial challenge.
1 code implementation • 3 Nov 2019 • Lei Deng, Yujie Wu, Yifan Hu, Ling Liang, Guoqi Li, Xing Hu, Yufei Ding, Peng Li, Yuan Xie
As well known, the huge memory and compute costs of both artificial neural networks (ANNs) and spiking neural networks (SNNs) greatly hinder their deployment on edge devices with high efficiency.
no code implementations • 1 Jan 2020 • Ling Liang, Xing Hu, Lei Deng, Yujie Wu, Guoqi Li, Yufei Ding, Peng Li, Yuan Xie
Recently, backpropagation through time inspired learning algorithms are widely introduced into SNNs to improve the performance, which brings the possibility to attack the models accurately given Spatio-temporal gradient maps.
1 code implementation • 7 Jan 2020 • Mingyu Yan, Lei Deng, Xing Hu, Ling Liang, Yujing Feng, Xiaochun Ye, Zhimin Zhang, Dongrui Fan, Yuan Xie
In this work, we first characterize the hybrid execution patterns of GCNs on Intel Xeon CPU.
Distributed, Parallel, and Cluster Computing
no code implementations • 26 Sep 2020 • Xiaobing Chen, yuke wang, Xinfeng Xie, Xing Hu, Abanti Basak, Ling Liang, Mingyu Yan, Lei Deng, Yufei Ding, Zidong Du, Yunji Chen, Yuan Xie
Graph convolutional network (GCN) emerges as a promising direction to learn the inductive representation in graph data commonly used in widespread applications, such as E-commerce, social networks, and knowledge graphs.
Hardware Architecture
1 code implementation • 28 May 2021 • Heng Yang, Ling Liang, Luca Carlone, Kim-Chuan Toh
In particular, we first design a globally convergent inexact projected gradient method (iPGM) for solving the SDP that serves as the backbone of our framework.
no code implementations • 25 Jul 2021 • Ling Liang, Zheng Qu, Zhaodong Chen, Fengbin Tu, Yujie Wu, Lei Deng, Guoqi Li, Peng Li, Yuan Xie
Although spiking neural networks (SNNs) take benefits from the bio-plausible neural modeling, the low accuracy under the common local synaptic plasticity learning rules limits their application in many practical tasks.
no code implementations • NeurIPS 2021 • Husheng Han, Kaidi Xu, Xing Hu, Xiaobing Chen, Ling Liang, Zidong Du, Qi Guo, Yanzhi Wang, Yunji Chen
Our experimental results show that the certified accuracy is increased from 36. 3% (the state-of-the-art certified detection) to 60. 4% on the ImageNet dataset, largely pushing the certified defenses for practical use.
no code implementations • 12 Apr 2022 • Ling Liang, Kaidi Xu, Xing Hu, Lei Deng, Yuan Xie
To the best of our knowledge, this is the first analysis on robust training of SNNs.
no code implementations • 29 Apr 2022 • Ching-pei Lee, Ling Liang, Tianyun Tang, Kim-Chuan Toh
This work proposes a rapid algorithm, BM-Global, for nuclear-norm-regularized convex and low-rank matrix optimization problems.
no code implementations • 23 Jan 2024 • Ling Liang, Haizhao Yang
We consider a regularized expected reward optimization problem in the non-oblivious setting that covers many existing problems in reinforcement learning (RL).
no code implementations • 8 Feb 2024 • Ling Liang, Kim-Chuan Toh, Jia-Jie Zhu
The Halpern iteration for solving monotone inclusion problems has gained increasing interests in recent years due to its simple form and appealing convergence properties.