
H2Learn: HighEfficiency Learning Accelerator for HighAccuracy Spiking Neural Networks
Although spiking neural networks (SNNs) take benefits from the bioplaus...
read it

Exploiting Spiking Dynamics with Spatialtemporal Feature Normalization in Graph Learning
Biological spiking neurons with intrinsic dynamics underlie the powerful...
read it

Towards Efficient Full 8bit Integer DNN Online Training on Resourcelimited Devices without Batch Normalization
Huge computational costs brought by convolution and batch normalization ...
read it

Reinforcement Learning Random Access for DelayConstrained Heterogeneous Wireless Networks: A TwoUser Case
In this paper, we investigate the random access problem for a delaycons...
read it

Sampling methods for efficient training of graph convolutional networks: A survey
Graph Convolutional Networks (GCNs) have received significant attention ...
read it

Technical Report for A Joint User Scheduling and Trajectory Planning Data Collection Strategy for the UAVassisted WSN
Unmanned aerial vehicles (UAVs) are usually dispatched as mobile sinks t...
read it

Training and Inference for IntegerBased Semantic Segmentation Network
Semantic segmentation has been a major topic in research and industry in...
read it

Going Deeper With DirectlyTrained Larger Spiking Neural Networks
Spiking neural networks (SNNs) are promising in a bioplausible coding f...
read it

Rubik: A Hierarchical Architecture for Efficient Graph Learning
Graph convolutional network (GCN) emerges as a promising direction to le...
read it

Kronecker CP Decomposition with Fast Multiplication for Compressing RNNs
Recurrent neural networks (RNNs) are powerful in the tasks oriented to s...
read it

Hybrid Tensor Decomposition in Neural Network Compression
Deep neural networks (DNNs) have enabled impressive breakthroughs in var...
read it

DelayConstrained TopologyTransparent Distributed Scheduling for MANETs
Transparent topology is common in many mobile ad hoc networks (MANETs) s...
read it

GNNAdvisor: An Efficient Runtime System for GNN Acceleration on GPUs
As the emerging trend of the graphbased deep learning, Graph Neural Net...
read it

Braininspired globallocal hybrid learning towards humanlike intelligence
The combination of neuroscienceoriented and computerscienceoriented a...
read it

Comparing SNNs and RNNs on Neuromorphic Vision Datasets: Similarities and Differences
Neuromorphic data, recording frameless spike events, have attracted cons...
read it

Characterizing and Understanding GCNs on GPU
Graph convolutional neural networks (GCNs) have achieved stateofthear...
read it

HyGCN: A GCN Accelerator with Hybrid Architecture
In this work, we first characterize the hybrid execution patterns of GCN...
read it

Exploring Adversarial Attack in Spiking Neural Networks with SpikeCompatible Gradient
Recently, backpropagation through time inspired learning algorithms are ...
read it

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...
read it

Transfer Learning in General Lensless Imaging through Scattering Media
Recently deep neural networks (DNNs) have been successfully introduced t...
read it

Lossless Compression for 3DCNNs Based on Tensor Train Decomposition
Three dimensional convolutional neural networks (3DCNNs) have been appli...
read it

Comprehensive SNN Compression Using ADMM Optimization and Activity Regularization
Spiking neural network is an important family of models to emulate the b...
read it

Training HighPerformance and LargeScale Deep Neural Networks with Full 8bit Integers
Deep neural network (DNN) quantization converting floatingpoint (FP) da...
read it

AccD: A Compilerbased Framework for Accelerating Distancerelated Algorithms on CPUFPGA Platforms
As a promising solution to boost the performance of distancerelated alg...
read it

KPynq: A WorkEfficient TriangleInequality based Kmeans on FPGA
Kmeans is a popular but computationintensive algorithm for unsupervise...
read it

Neural Network Model Extraction Attacks in Edge Devices by Hearing Architectural Hints
As neural networks continue their reach into nearly every aspect of soft...
read it

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 ...
read it

Batch Normalization Sampling
Deep Neural Networks (DNNs) thrive in recent years in which Batch Normal...
read it

Dynamic Sparse Graph for Efficient Deep Learning
We propose to execute deep neural networks (DNNs) with dynamic and spars...
read it

Inmemory multiplication engine with SOTMRAM based stochastic computing
Processinginmemory (PIM) turns out to be a promising solution to break...
read it

Direct Training for Spiking Neural Networks: Faster, Larger, Better
Spiking neural networks (SNNs) are gaining more attention as a promising...
read it

DelayConstrained InputQueued Switch
In this paper, we study the delayconstrained inputqueued switch where ...
read it

Crossbaraware neural network pruning
Crossbar architecture based devices have been widely adopted in neural n...
read it

L1Norm Batch Normalization for Efficient Training of Deep Neural Networks
Batch Normalization (BN) has been proven to be quite effective at accele...
read it

DevicetoDevice Load Balancing for Cellular Networks
Smallcell architecture is widely adopted by cellular network operators ...
read it

SpatioTemporal Backpropagation for Training Highperformance Spiking Neural Networks
Compared with artificial neural networks (ANNs), spiking neural networks...
read it

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...
read it
Lei Deng
is this you? claim profile