177 citations · 689 across the 74 of their papers we have counts for
76 papers
LinGCN: Structural Linearized Graph Convolutional Network for Homomorphically Encrypted Inference
Hongwu Peng, Ran Ran, Yukui Luo +8
The growth of Graph Convolution Network (GCN) model sizes has revolutionized numerous applications, surpassing human performance in areas such as personal healthcare and financial…
AdaDiff: Accelerating Diffusion Models through Step-Wise Adaptive Computation
Shengkun Tang, Yaqing Wang, Caiwen Ding +3
Diffusion models achieve great success in generating diverse and high-fidelity images, yet their widespread application, especially in real-time scenarios, is hampered by their inh…
Tango: rethinking quantization for graph neural network training on GPUs
Shiyang Chen, Da Zheng, Caiwen Ding +3
Graph Neural Networks (GNNs) are becoming increasingly popular due to their superior performance in critical graph-related tasks. While quantization is widely used to accelerate GN…
Accel-GCN: High-Performance GPU Accelerator Design for Graph Convolution Networks
Xi Xie, Hongwu Peng, Amit Hasan +7
Graph Convolutional Networks (GCNs) are pivotal in extracting latent information from graph data across various domains, yet their acceleration on mainstream GPUs is challenged by…
AutoReP: Automatic ReLU Replacement for Fast Private Network Inference
Hongwu Peng, Shaoyi Huang, Tong Zhou +11
The growth of the Machine-Learning-As-A-Service (MLaaS) market has highlighted clients' data privacy and security issues. Private inference (PI) techniques using cryptographic prim…
Towards Zero Memory Footprint Spiking Neural Network Training
Bin Lei, Sheng Lin, Pei-Hung Lin +2
Biologically-inspired Spiking Neural Networks (SNNs), processing information using discrete-time events known as spikes rather than continuous values, have garnered significant att…
Boosting Logical Reasoning in Large Language Models through a New Framework: The Graph of Thought
Bin Lei, pei-Hung Lin, Chunhua Liao +1
Recent advancements in large-scale models, such as GPT-4, have showcased remarkable capabilities in addressing standard queries. However, when facing complex problems that require…
Spectral-DP: Differentially Private Deep Learning through Spectral Perturbation and Filtering
Ce Feng, Nuo Xu, Wujie Wen +2
Differential privacy is a widely accepted measure of privacy in the context of deep learning algorithms, and achieving it relies on a noisy training approach known as differentiall…
Creating a Dataset for High-Performance Computing Code Translation using LLMs: A Bridge Between OpenMP Fortran and C++
Bin Lei, Caiwen Ding, Le Chen +2
In this study, we present a novel dataset for training machine learning models translating between OpenMP Fortran and C++ code. To ensure reliability and applicability, the dataset…
PASNet: Polynomial Architecture Search Framework for Two-party Computation-based Secure Neural Network Deployment
Hongwu Peng, Shanglin Zhou, Yukui Luo +9
Two-party computation (2PC) is promising to enable privacy-preserving deep learning (DL). However, the 2PC-based privacy-preserving DL implementation comes with high comparison pro…
Attacking All Tasks at Once Using Adversarial Examples in Multi-Task Learning
Lijun Zhang, Xiao Liu, Kaleel Mahmood +2
Visual content understanding frequently relies on multi-task models to extract robust representations of a single visual input for multiple downstream tasks. However, in comparison…
Neurogenesis Dynamics-inspired Spiking Neural Network Training Acceleration
Shaoyi Huang, Haowen Fang, Kaleel Mahmood +7
Biologically inspired Spiking Neural Networks (SNNs) have attracted significant attention for their ability to provide extremely energy-efficient machine intelligence through event…
Surrogate Lagrangian Relaxation: A Path To Retrain-free Deep Neural Network Pruning
Shanglin Zhou, Mikhail A. Bragin, Lynn Pepin +3
Network pruning is a widely used technique to reduce computation cost and model size for deep neural networks. However, the typical three-stage pipeline significantly increases the…
Physics-aware Roughness Optimization for Diffractive Optical Neural Networks
Shanglin Zhou, Yingjie Li, Minhan Lou +4
As a representative next-generation device/circuit technology beyond CMOS, diffractive optical neural networks (DONNs) have shown promising advantages over conventional deep neural…
Collaborative Multi-Object Tracking with Conformal Uncertainty Propagation
Sanbao Su, Songyang Han, Yiming Li +4
Object detection and multiple object tracking (MOT) are essential components of self-driving systems. Accurate detection and uncertainty quantification are both critical for onboar…
RRNet: Towards ReLU-Reduced Neural Network for Two-party Computation Based Private Inference
Hongwu Peng, Shanglin Zhou, Yukui Luo +11
The proliferation of deep learning (DL) has led to the emergence of privacy and security concerns. To address these issues, secure Two-party computation (2PC) has been proposed as…
Shared Information-Based Safe And Efficient Behavior Planning For Connected Autonomous Vehicles
Songyang Han, Shanglin Zhou, Lynn Pepin +3
The recent advancements in wireless technology enable connected autonomous vehicles (CAVs) to gather data via vehicle-to-vehicle (V2V) communication, such as processed LIDAR and ca…
All-in-One: A Highly Representative DNN Pruning Framework for Edge Devices with Dynamic Power Management
Yifan Gong, Zheng Zhan, Pu Zhao +6
During the deployment of deep neural networks (DNNs) on edge devices, many research efforts are devoted to the limited hardware resource. However, little attention is paid to the i…
Accelerating Dataset Distillation via Model Augmentation
Lei Zhang, Jie Zhang, Bowen Lei +6
Dataset Distillation (DD), a newly emerging field, aims at generating much smaller but efficient synthetic training datasets from large ones. Existing DD methods based on gradient…
Efficient Traffic State Forecasting using Spatio-Temporal Network Dependencies: A Sparse Graph Neural Network Approach
Bin Lei, Shaoyi Huang, Caiwen Ding +1
Traffic state prediction in a transportation network is paramount for effective traffic operations and management, as well as informed user and system-level decision-making. Howeve…
Dynamic Sparse Training via Balancing the Exploration-Exploitation Trade-off
Shaoyi Huang, Bowen Lei, Dongkuan Xu +4
Over-parameterization of deep neural networks (DNNs) has shown high prediction accuracy for many applications. Although effective, the large number of parameters hinders its popula…
Game Theoretic Mixed Experts for Combinational Adversarial Machine Learning
Ethan Rathbun, Kaleel Mahmood, Sohaib Ahmad +2
Recent advances in adversarial machine learning have shown that defenses considered to be robust are actually susceptible to adversarial attacks which are specifically customized t…
You Need Multiple Exiting: Dynamic Early Exiting for Accelerating Unified Vision Language Model
Shengkun Tang, Yaqing Wang, Zhenglun Kong +6
Large-scale Transformer models bring significant improvements for various downstream vision language tasks with a unified architecture. The performance improvements come with incre…
Aerial Manipulation Using a Novel Unmanned Aerial Vehicle Cyber-Physical System
Caiwu Ding, Hongwu Peng, Lu Lu +1
Unmanned Aerial Vehicles(UAVs) are attaining more and more maneuverability and sensory ability as a promising teleoperation platform for intelligent interaction with the environmen…