177 citations · 689 across the 74 of their papers we have counts for
16 papers · 1 filter
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…
Towards Real-Time Temporal Graph Learning
Deniz Gurevin, Mohsin Shan, Tong Geng +3
In recent years, graph representation learning has gained significant popularity, which aims to generate node embeddings that capture features of graphs. One of the methods to achi…
PolyMPCNet: Towards ReLU-free Neural Architecture Search in Two-party Computation Based Private Inference
Hongwu Peng, Shanglin Zhou, Yukui Luo +10
The rapid growth and deployment of deep learning (DL) has witnessed emerging privacy and security concerns. To mitigate these issues, secure multi-party computation (MPC) has been…
Uncertainty Quantification of Collaborative Detection for Self-Driving
Sanbao Su, Yiming Li, Sihong He +4
Sharing information between connected and autonomous vehicles (CAVs) fundamentally improves the performance of collaborative object detection for self-driving. However, CAVs still…
Towards Sparsification of Graph Neural Networks
Hongwu Peng, Deniz Gurevin, Shaoyi Huang +4
As real-world graphs expand in size, larger GNN models with billions of parameters are deployed. High parameter count in such models makes training and inference on graphs expensiv…
Attacking the Spike: On the Transferability and Security of Spiking Neural Networks to Adversarial Examples
Nuo Xu, Kaleel Mahmood, Haowen Fang +3
Spiking neural networks (SNNs) have attracted much attention for their high energy efficiency and recent advances in classification performance. However, unlike traditional deep le…
A Length Adaptive Algorithm-Hardware Co-design of Transformer on FPGA Through Sparse Attention and Dynamic Pipelining
Hongwu Peng, Shaoyi Huang, Shiyang Chen +8
Transformers are considered one of the most important deep learning models since 2018, in part because it establishes state-of-the-art (SOTA) records and could potentially replace…
EVE: Environmental Adaptive Neural Network Models for Low-power Energy Harvesting System
Sahidul Islam, Shanglin Zhou, Ran Ran +4
IoT devices are increasingly being implemented with neural network models to enable smart applications. Energy harvesting (EH) technology that harvests energy from ambient environm…
An Automatic and Efficient BERT Pruning for Edge AI Systems
Shaoyi Huang, Ning Liu, Yueying Liang +5
With the yearning for deep learning democratization, there are increasing demands to implement Transformer-based natural language processing (NLP) models on resource-constrained de…
A Secure and Efficient Federated Learning Framework for NLP
Jieren Deng, Chenghong Wang, Xianrui Meng +7
In this work, we consider the problem of designing secure and efficient federated learning (FL) frameworks. Existing solutions either involve a trusted aggregator or require heavyw…