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08/20/2023
AutoReP: Automatic ReLU Replacement for Fast Private Network Inference
The growth of the Machine-Learning-As-A-Service (MLaaS) market has highl...
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07/29/2023
Improving Realistic Worst-Case Performance of NVCiM DNN Accelerators through Training with Right-Censored Gaussian Noise
Compute-in-Memory (CiM), built upon non-volatile memory (NVM) devices, i...
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07/25/2023
Spectral-DP: Differentially Private Deep Learning through Spectral Perturbation and Filtering
Differential privacy is a widely accepted measure of privacy in the cont...
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06/27/2023
PASNet: Polynomial Architecture Search Framework for Two-party Computation-based Secure Neural Network Deployment
Two-party computation (2PC) is promising to enable privacy-preserving de...
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05/23/2023
Negative Feedback Training: A Novel Concept to Improve Robustness of NVCiM DNN Accelerators
Compute-in-Memory (CiM) utilizing non-volatile memory (NVM) devices pres...
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04/24/2023
Neurogenesis Dynamics-inspired Spiking Neural Network Training Acceleration
Biologically inspired Spiking Neural Networks (SNNs) have attracted sign...
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02/05/2023
RRNet: Towards ReLU-Reduced Neural Network for Two-party Computation Based Private Inference
The proliferation of deep learning (DL) has led to the emergence of priv...
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09/24/2022
CryptoGCN: Fast and Scalable Homomorphically Encrypted Graph Convolutional Network Inference
Recently cloud-based graph convolutional network (GCN) has demonstrated ...
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09/20/2022
PolyMPCNet: Towards ReLU-free Neural Architecture Search in Two-party Computation Based Private Inference
The rapid growth and deployment of deep learning (DL) has witnessed emer...
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09/07/2022
Securing the Spike: On the Transferabilty and Security of Spiking Neural Networks to Adversarial Examples
Spiking neural networks (SNNs) have attracted much attention for their h...
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08/07/2022
A Length Adaptive Algorithm-Hardware Co-design of Transformer on FPGA Through Sparse Attention and Dynamic Pipelining
Transformers are considered one of the most important deep learning mode...
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07/14/2022
EVE: Environmental Adaptive Neural Network Models for Low-power Energy Harvesting System
IoT devices are increasingly being implemented with neural network model...
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06/11/2022
NeuGuard: Lightweight Neuron-Guided Defense against Membership Inference Attacks
Membership inference attacks (MIAs) against machine learning models can ...
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11/04/2020
Do Noises Bother Human and Neural Networks In the Same Way? A Medical Image Analysis Perspective
Deep learning had already demonstrated its power in medical images, incl...
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08/27/2019
Tiny but Accurate: A Pruned, Quantized and Optimized Memristor Crossbar Framework for Ultra Efficient DNN Implementation
The state-of-art DNN structures involve intensive computation and high m...
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06/13/2019
Thread Batching for High-performance Energy-efficient GPU Memory Design
Massive multi-threading in GPU imposes tremendous pressure on memory sub...
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04/09/2019
Machine Vision Guided 3D Medical Image Compression for Efficient Transmission and Accurate Segmentation in the Clouds
Cloud based medical image analysis has become popular recently due to th...
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03/23/2019
Progressive DNN Compression: A Key to Achieve Ultra-High Weight Pruning and Quantization Rates using ADMM
Weight pruning and weight quantization are two important categories of D...
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12/12/2018
E-RNN: Design Optimization for Efficient Recurrent Neural Networks in FPGAs
Recurrent Neural Networks (RNNs) are becoming increasingly important for...
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09/13/2018
Defensive Dropout for Hardening Deep Neural Networks under Adversarial Attacks
Deep neural networks (DNNs) are known vulnerable to adversarial attacks....
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07/29/2018
ADAM-ADMM: A Unified, Systematic Framework of Structured Weight Pruning for DNNs
Weight pruning methods of deep neural networks (DNNs) have been demonstr...
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04/10/2018
A Systematic DNN Weight Pruning Framework using Alternating Direction Method of Multipliers
Weight pruning methods for deep neural networks (DNNs) have been investi...
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03/14/2018
On the Universal Approximation Property and Equivalence of Stochastic Computing-based Neural Networks and Binary Neural Networks
Large-scale deep neural networks are both memory intensive and computati...
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03/14/2018
MT-Spike: A Multilayer Time-based Spiking Neuromorphic Architecture with Temporal Error Backpropagation
Modern deep learning enabled artificial neural networks, such as Deep Ne...
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03/14/2018
PT-Spike: A Precise-Time-Dependent Single Spike Neuromorphic Architecture with Efficient Supervised Learning
One of the most exciting advancements in AI over the last decade is the ...
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03/14/2018
Feature Distillation: DNN-Oriented JPEG Compression Against Adversarial Examples
Deep Neural Networks (DNNs) have achieved remarkable performance in a my...
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03/14/2018
DeepN-JPEG: A Deep Neural Network Favorable JPEG-based Image Compression Framework
As one of most fascinating machine learning techniques, deep neural netw...
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02/14/2018
Security Analysis and Enhancement of Model Compressed Deep Learning Systems under Adversarial Attacks
DNN is presenting human-level performance for many complex intelligent t...
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05/09/2016