
Training and Inference for IntegerBased Semantic Segmentation Network
Semantic segmentation has been a major topic in research and industry in...
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Going Deeper With DirectlyTrained Larger Spiking Neural Networks
Spiking neural networks (SNNs) are promising in a bioplausible coding f...
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Rubik: A Hierarchical Architecture for Efficient Graph Learning
Graph convolutional network (GCN) emerges as a promising direction to le...
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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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Hybrid Tensor Decomposition in Neural Network Compression
Deep neural networks (DNNs) have enabled impressive breakthroughs in var...
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DelayConstrained TopologyTransparent Distributed Scheduling for MANETs
Transparent topology is common in many mobile ad hoc networks (MANETs) s...
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GNNAdvisor: An Efficient Runtime System for GNN Acceleration on GPUs
As the emerging trend of the graphbased deep learning, Graph Neural Net...
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Braininspired globallocal hybrid learning towards humanlike intelligence
The combination of neuroscienceoriented and computerscienceoriented a...
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Comparing SNNs and RNNs on Neuromorphic Vision Datasets: Similarities and Differences
Neuromorphic data, recording frameless spike events, have attracted cons...
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Characterizing and Understanding GCNs on GPU
Graph convolutional neural networks (GCNs) have achieved stateofthear...
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HyGCN: A GCN Accelerator with Hybrid Architecture
In this work, we first characterize the hybrid execution patterns of GCN...
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Exploring Adversarial Attack in Spiking Neural Networks with SpikeCompatible Gradient
Recently, backpropagation through time inspired learning algorithms are ...
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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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Transfer Learning in General Lensless Imaging through Scattering Media
Recently deep neural networks (DNNs) have been successfully introduced t...
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Lossless Compression for 3DCNNs Based on Tensor Train Decomposition
Three dimensional convolutional neural networks (3DCNNs) have been appli...
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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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Training HighPerformance and LargeScale Deep Neural Networks with Full 8bit Integers
Deep neural network (DNN) quantization converting floatingpoint (FP) da...
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AccD: A Compilerbased Framework for Accelerating Distancerelated Algorithms on CPUFPGA Platforms
As a promising solution to boost the performance of distancerelated alg...
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KPynq: A WorkEfficient TriangleInequality based Kmeans on FPGA
Kmeans is a popular but computationintensive algorithm for unsupervise...
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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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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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Batch Normalization Sampling
Deep Neural Networks (DNNs) thrive in recent years in which Batch Normal...
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Dynamic Sparse Graph for Efficient Deep Learning
We propose to execute deep neural networks (DNNs) with dynamic and spars...
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Inmemory multiplication engine with SOTMRAM based stochastic computing
Processinginmemory (PIM) turns out to be a promising solution to break...
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Direct Training for Spiking Neural Networks: Faster, Larger, Better
Spiking neural networks (SNNs) are gaining more attention as a promising...
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DelayConstrained InputQueued Switch
In this paper, we study the delayconstrained inputqueued switch where ...
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Crossbaraware neural network pruning
Crossbar architecture based devices have been widely adopted in neural n...
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L1Norm 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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DevicetoDevice Load Balancing for Cellular Networks
Smallcell architecture is widely adopted by cellular network operators ...
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SpatioTemporal Backpropagation for Training Highperformance Spiking Neural Networks
Compared with artificial neural networks (ANNs), spiking neural networks...
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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...
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Lei Deng
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