Search Results for author: Ling Liang

Found 16 papers, 3 papers with code

Crossbar-aware neural network pruning

no code implementations25 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.

Network Pruning

TETRIS: TilE-matching the TRemendous Irregular Sparsity

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.

Neural Network Model Extraction Attacks in Edge Devices by Hearing Architectural Hints

no code implementations10 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

Multi-View Fuzzy Clustering with The Alternative Learning between Shared Hidden Space and Partition

no code implementations12 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.

Clustering

Comprehensive SNN Compression Using ADMM Optimization and Activity Regularization

1 code implementation3 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.

Model Compression Quantization

Exploring Adversarial Attack in Spiking Neural Networks with Spike-Compatible Gradient

no code implementations1 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.

Adversarial Attack

HyGCN: A GCN Accelerator with Hybrid Architecture

1 code implementation7 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

Rubik: A Hierarchical Architecture for Efficient Graph Learning

no code implementations26 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

An Inexact Projected Gradient Method with Rounding and Lifting by Nonlinear Programming for Solving Rank-One Semidefinite Relaxation of Polynomial Optimization

1 code implementation28 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.

H2Learn: High-Efficiency Learning Accelerator for High-Accuracy Spiking Neural Networks

no code implementations25 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.

Vocal Bursts Intensity Prediction

ScaleCert: Scalable Certified Defense against Adversarial Patches with Sparse Superficial Layers

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.

Toward Robust Spiking Neural Network Against Adversarial Perturbation

no code implementations12 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.

Accelerating nuclear-norm regularized low-rank matrix optimization through Burer-Monteiro decomposition

no code implementations29 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.

Recommendation Systems

On the Stochastic (Variance-Reduced) Proximal Gradient Method for Regularized Expected Reward Optimization

no code implementations23 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).

Reinforcement Learning (RL)

An Inexact Halpern Iteration with Application to Distributionally Robust Optimization

no code implementations8 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.

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