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09/28/2022
Attention Spiking Neural Networks
Benefiting from the event-driven and sparse spiking characteristics of t...
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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...
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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...
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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...
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04/12/2022
Toward Robust Spiking Neural Network Against Adversarial Perturbation
As spiking neural networks (SNNs) are deployed increasingly in real-worl...
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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...
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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...
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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...
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12/15/2021
Advancing Residual Learning towards Powerful Deep Spiking Neural Networks
Despite the rapid progress of neuromorphic computing, inadequate capacit...
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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...
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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...
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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...
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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...
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06/30/2021
Exploiting Spiking Dynamics with Spatial-temporal Feature Normalization in Graph Learning
Biological spiking neurons with intrinsic dynamics underlie the powerful...
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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 ...
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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...
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03/10/2021
Sampling methods for efficient training of graph convolutional networks: A survey
Graph Convolutional Networks (GCNs) have received significant attention ...
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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...
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11/30/2020
Training and Inference for Integer-Based Semantic Segmentation Network
Semantic segmentation has been a major topic in research and industry in...
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10/29/2020
Going Deeper With Directly-Trained Larger Spiking Neural Networks
Spiking neural networks (SNNs) are promising in a bio-plausible coding f...
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09/26/2020
Rubik: A Hierarchical Architecture for Efficient Graph Learning
Graph convolutional network (GCN) emerges as a promising direction to le...
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08/21/2020
Kronecker CP Decomposition with Fast Multiplication for Compressing RNNs
Recurrent neural networks (RNNs) are powerful in the tasks oriented to s...
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06/29/2020
Hybrid Tensor Decomposition in Neural Network Compression
Deep neural networks (DNNs) have enabled impressive breakthroughs in var...
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06/13/2020
Delay-Constrained Topology-Transparent Distributed Scheduling for MANETs
Transparent topology is common in many mobile ad hoc networks (MANETs) s...
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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...
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06/05/2020
Brain-inspired global-local hybrid learning towards human-like intelligence
The combination of neuroscience-oriented and computer-science-oriented a...
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05/02/2020
Comparing SNNs and RNNs on Neuromorphic Vision Datasets: Similarities and Differences
Neuromorphic data, recording frameless spike events, have attracted cons...
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01/28/2020
Characterizing and Understanding GCNs on GPU
Graph convolutional neural networks (GCNs) have achieved state-of-the-ar...
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01/07/2020
HyGCN: A GCN Accelerator with Hybrid Architecture
In this work, we first characterize the hybrid execution patterns of GCN...
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01/01/2020
Exploring Adversarial Attack in Spiking Neural Networks with Spike-Compatible Gradient
Recently, backpropagation through time inspired learning algorithms are ...
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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...
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12/28/2019
Transfer Learning in General Lensless Imaging through Scattering Media
Recently deep neural networks (DNNs) have been successfully introduced t...
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12/08/2019
Lossless Compression for 3DCNNs Based on Tensor Train Decomposition
Three dimensional convolutional neural networks (3DCNNs) have been appli...
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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...
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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...
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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...
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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...
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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...
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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 ...
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10/25/2018
Batch Normalization Sampling
Deep Neural Networks (DNNs) thrive in recent years in which Batch Normal...
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10/01/2018
Dynamic Sparse Graph for Efficient Deep Learning
We propose to execute deep neural networks (DNNs) with dynamic and spars...
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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...
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09/16/2018
Direct Training for Spiking Neural Networks: Faster, Larger, Better
Spiking neural networks (SNNs) are gaining more attention as a promising...
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09/08/2018
Delay-Constrained Input-Queued Switch
In this paper, we study the delay-constrained input-queued switch where ...
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07/25/2018
Crossbar-aware neural network pruning
Crossbar architecture based devices have been widely adopted in neural n...
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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...
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10/07/2017
Device-to-Device Load Balancing for Cellular Networks
Small-cell architecture is widely adopted by cellular network operators ...
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06/08/2017
Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networks
Compared with artificial neural networks (ANNs), spiking neural networks...
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05/25/2017