b'Lei Deng'

research

∙ 09/28/2022

Attention Spiking Neural Networks

Benefiting from the event-driven and sparse spiking characteristics of t...

0 Man Yao, et al. ∙

research

∙ 06/20/2022

The Story of 1/e: ALOHA-based and Reinforcement-Learning-based Random Access for Delay-Constrained Communications

Motivated by the proliferation of real-time applications in multimedia c...

0 Lei Deng, et al. ∙

research

∙ 06/20/2022

An Input-Queueing TSN Switching Architecture to Achieve Zero Packet Loss for Timely Traffic

Zero packet loss with bounded latency is a must for many applications su...

0 Ming Li, et al. ∙

research

∙ 05/04/2022

Reinforcement Learning for Improved Random Access in Delay-Constrained Heterogeneous Wireless Networks

In this paper, we for the first time investigate the random access probl...

0 Lei Deng, et al. ∙

research

∙ 04/12/2022

Toward Robust Spiking Neural Network Against Adversarial Perturbation

As spiking neural networks (SNNs) are deployed increasingly in real-worl...

0 Ling Liang, et al. ∙

research

∙ 03/14/2022

Spiking Neural Network Integrated Circuits: A Review of Trends and Future Directions

In this paper, we reviewed Spiking neural network (SNN) integrated circu...

0 Arindam Basu, et al. ∙

research

∙ 02/21/2022

A Deep Learning Approach to Predicting Ventilator Parameters for Mechanically Ventilated Septic Patients

We develop a deep learning approach to predicting a set of ventilator pa...

3 Zhijun Zeng, et al. ∙

research

∙ 02/10/2022

Survey on Graph Neural Network Acceleration: An Algorithmic Perspective

Graph neural networks (GNNs) have been a hot spot of recent research and...

0 Xin Liu, et al. ∙

research

∙ 12/15/2021

Advancing Residual Learning towards Powerful Deep Spiking Neural Networks

Despite the rapid progress of neuromorphic computing, inadequate capacit...

0 Yifan Hu, et al. ∙

research

∙ 12/09/2021

Advancing Deep Residual Learning by Solving the Crux of Degradation in Spiking Neural Networks

Despite the rapid progress of neuromorphic computing, the inadequate dep...

0 Yifan Hu, et al. ∙

research

∙ 10/23/2021

ES-ImageNet: A Million Event-Stream Classification Dataset for Spiking Neural Networks

With event-driven algorithms, especially the spiking neural networks (SN...

0 Yihan Lin, et al. ∙

research

∙ 08/14/2021

Graph2MDA: a multi-modal variational graph embedding model for predicting microbe-drug associations

Accumulated clinical studies show that microbes living in humans interac...

7 Lei Deng, et al. ∙

research

∙ 07/25/2021

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

Although spiking neural networks (SNNs) take benefits from the bio-plaus...

0 Ling Liang, et al. ∙

research

∙ 06/30/2021

Exploiting Spiking Dynamics with Spatial-temporal Feature Normalization in Graph Learning

Biological spiking neurons with intrinsic dynamics underlie the powerful...

0 Mingkun Xu, et al. ∙

research

∙ 05/27/2021

Towards Efficient Full 8-bit Integer DNN Online Training on Resource-limited Devices without Batch Normalization

Huge computational costs brought by convolution and batch normalization ...

0 Yukuan Yang, et al. ∙

research

∙ 03/27/2021

Reinforcement Learning Random Access for Delay-Constrained Heterogeneous Wireless Networks: A Two-User Case

In this paper, we investigate the random access problem for a delay-cons...

0 Danzhou Wu, et al. ∙

research

∙ 03/10/2021

Sampling methods for efficient training of graph convolutional networks: A survey

Graph Convolutional Networks (GCNs) have received significant attention ...

0 Xin Liu, et al. ∙

research

∙ 03/01/2021

Technical Report for A Joint User Scheduling and Trajectory Planning Data Collection Strategy for the UAV-assisted WSN

Unmanned aerial vehicles (UAVs) are usually dispatched as mobile sinks t...

0 Xindi Wang, et al. ∙

research

∙ 11/30/2020

Training and Inference for Integer-Based Semantic Segmentation Network

Semantic segmentation has been a major topic in research and industry in...

0 Jiayi Yang, et al. ∙

research

∙ 10/29/2020

Going Deeper With Directly-Trained Larger Spiking Neural Networks

Spiking neural networks (SNNs) are promising in a bio-plausible coding f...

0 Hanle Zheng, et al. ∙

research

∙ 09/26/2020

Rubik: A Hierarchical Architecture for Efficient Graph Learning

Graph convolutional network (GCN) emerges as a promising direction to le...

0 Xiaobing Chen, et al. ∙

research

∙ 08/21/2020

Kronecker CP Decomposition with Fast Multiplication for Compressing RNNs

Recurrent neural networks (RNNs) are powerful in the tasks oriented to s...

0 Dingheng Wang, et al. ∙

research

∙ 06/29/2020

Hybrid Tensor Decomposition in Neural Network Compression

Deep neural networks (DNNs) have enabled impressive breakthroughs in var...

0 Bijiao Wu, et al. ∙

research

∙ 06/13/2020

Delay-Constrained Topology-Transparent Distributed Scheduling for MANETs

Transparent topology is common in many mobile ad hoc networks (MANETs) s...

0 Lei Deng, et al. ∙

research

∙ 06/11/2020

GNNAdvisor: An Efficient Runtime System for GNN Acceleration on GPUs

As the emerging trend of the graph-based deep learning, Graph Neural Net...

1 Yuke Wang, et al. ∙

research

∙ 06/05/2020

Brain-inspired global-local hybrid learning towards human-like intelligence

The combination of neuroscience-oriented and computer-science-oriented a...

0 Yujie Wu, et al. ∙

research

∙ 05/02/2020

Comparing SNNs and RNNs on Neuromorphic Vision Datasets: Similarities and Differences

Neuromorphic data, recording frameless spike events, have attracted cons...

11 Weihua He, et al. ∙

research

∙ 01/28/2020

Characterizing and Understanding GCNs on GPU

Graph convolutional neural networks (GCNs) have achieved state-of-the-ar...

0 Mingyu Yan, et al. ∙

research

∙ 01/07/2020

HyGCN: A GCN Accelerator with Hybrid Architecture

In this work, we first characterize the hybrid execution patterns of GCN...

0 Mingyu Yan, et al. ∙

research

∙ 01/01/2020

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

Recently, backpropagation through time inspired learning algorithms are ...

14 Ling Liang, et al. ∙

research

∙ 01/01/2020

A Comprehensive and Modularized Statistical Framework for Gradient Norm Equality in Deep Neural Networks

In recent years, plenty of metrics have been proposed to identify networ...

28 Zhaodong Chen, et al. ∙

research

∙ 12/28/2019

Transfer Learning in General Lensless Imaging through Scattering Media

Recently deep neural networks (DNNs) have been successfully introduced t...

15 Yukuan Yang, et al. ∙

research

∙ 12/08/2019

Lossless Compression for 3DCNNs Based on Tensor Train Decomposition

Three dimensional convolutional neural networks (3DCNNs) have been appli...

27 Dingheng Wang, et al. ∙

research

∙ 11/03/2019

Comprehensive SNN Compression Using ADMM Optimization and Activity Regularization

Spiking neural network is an important family of models to emulate the b...

18 Lei Deng, et al. ∙

research

∙ 09/05/2019

Training High-Performance and Large-Scale Deep Neural Networks with Full 8-bit Integers

Deep neural network (DNN) quantization converting floating-point (FP) da...

0 Yukuan Yang, et al. ∙

research

∙ 08/26/2019

AccD: A Compiler-based Framework for Accelerating Distance-related Algorithms on CPU-FPGA Platforms

As a promising solution to boost the performance of distance-related alg...

0 Yuke Wang, et al. ∙

research

∙ 05/22/2019

KPynq: A Work-Efficient Triangle-Inequality based K-means on FPGA

K-means is a popular but computation-intensive algorithm for unsupervise...

0 Yuke Wang, et al. ∙

research

∙ 03/10/2019

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

As neural networks continue their reach into nearly every aspect of soft...

0 Xing Hu, et al. ∙

research

∙ 11/10/2018

On Stability Condition of Wireless Networked Control Systems under Joint Design of Control Policy and Network Scheduling Policy

In this paper, we study a wireless networked control system (WNCS) with ...

0 Lei Deng, et al. ∙

research

∙ 10/25/2018

Batch Normalization Sampling

Deep Neural Networks (DNNs) thrive in recent years in which Batch Normal...

0 Zhaodong Chen, et al. ∙

research

∙ 10/01/2018

Dynamic Sparse Graph for Efficient Deep Learning

We propose to execute deep neural networks (DNNs) with dynamic and spars...

0 Liu Liu, et al. ∙

research

∙ 09/22/2018

In-memory multiplication engine with SOT-MRAM based stochastic computing

Processing-in-memory (PIM) turns out to be a promising solution to break...

0 Xin Ma, et al. ∙

research

∙ 09/16/2018

Direct Training for Spiking Neural Networks: Faster, Larger, Better

Spiking neural networks (SNNs) are gaining more attention as a promising...

0 Yujie Wu, et al. ∙

research

∙ 09/08/2018

Delay-Constrained Input-Queued Switch

In this paper, we study the delay-constrained input-queued switch where ...

0 Lei Deng, et al. ∙

research

∙ 07/25/2018

Crossbar-aware neural network pruning

Crossbar architecture based devices have been widely adopted in neural n...

0 Ling Liang, et al. ∙

research

∙ 02/27/2018

L1-Norm Batch Normalization for Efficient Training of Deep Neural Networks

Batch Normalization (BN) has been proven to be quite effective at accele...

0 Shuang Wu, et al. ∙

research

∙ 10/07/2017

Device-to-Device Load Balancing for Cellular Networks

Small-cell architecture is widely adopted by cellular network operators ...

0 Lei Deng, et al. ∙

research

∙ 06/08/2017

Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networks

Compared with artificial neural networks (ANNs), spiking neural networks...

0 Yujie Wu, et al. ∙

research

∙ 05/25/2017

Gated XNOR Networks: Deep Neural Networks with Ternary Weights and Activations under a Unified Discretization Framework

There is a pressing need to build an architecture that could subsume the...

0 Lei Deng, et al. ∙

Lei Deng

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