9 citations · 21 across the 10 of their papers we have counts for
10 papers
Can't Slow me Down: Learning Robust and Hardware-Adaptive Object Detectors against Latency Attacks for Edge Devices
Tianyi Wang, Zichen Wang, Cong Wang +4
Object detection is a fundamental enabler for many real-time downstream applications such as autonomous driving, augmented reality and supply chain management. However, the algorit…
Confidant: Customizing Transformer-based LLMs via Collaborative Edge Training
Yuhao Chen, Yuxuan Yan, Qianqian Yang +3
Transformer-based large language models (LLMs) have demonstrated impressive capabilities in a variety of natural language processing (NLP) tasks. Nonetheless, it is challenging to…
AccEPT: An Acceleration Scheme for Speeding Up Edge Pipeline-parallel Training
Yuhao Chen, Yuxuan Yan, Qianqian Yang +4
It is usually infeasible to fit and train an entire large deep neural network (DNN) model using a single edge device due to the limited resources. To facilitate intelligent applica…
Turbo: Opportunistic Enhancement for Edge Video Analytics
Yan Lu, Shiqi Jiang, Ting Cao +1
Edge computing is being widely used for video analytics. To alleviate the inherent tension between accuracy and cost, various video analytics pipelines have been proposed to optimi…
GEMEL: Model Merging for Memory-Efficient, Real-Time Video Analytics at the Edge
Arthi Padmanabhan, Neil Agarwal, Anand Iyer +5
Video analytics pipelines have steadily shifted to edge deployments to reduce bandwidth overheads and privacy violations, but in doing so, face an ever-growing resource tension. Mo…
Custom Object Detection via Multi-Camera Self-Supervised Learning
Yan Lu, Yuanchao Shu
This paper proposes MCSSL, a self-supervised learning approach for building custom object detection models in multi-camera networks. MCSSL associates bounding boxes between cameras…
Deep Learning in the Era of Edge Computing: Challenges and Opportunities
Mi Zhang, Faen Zhang, Nicholas D. Lane +5
The era of edge computing has arrived. Although the Internet is the backbone of edge computing, its true value lies at the intersection of gathering data from sensors and extractin…
WatchDog: Real-time Vehicle Tracking on Geo-distributed Edge Nodes
Zheng Dong, Yan Lu, Guangmo Tong +3
Vehicle tracking, a core application to smart city video analytics, is becoming more widely deployed than ever before thanks to the increasing number of traffic cameras and recent…
ReXCam: Resource-Efficient, Cross-Camera Video Analytics at Scale
Samvit Jain, Xun Zhang, Yuhao Zhou +4
Enterprises are increasingly deploying large camera networks for video analytics. Many target applications entail a common problem template: searching for and tracking an object or…
Scaling Video Analytics Systems to Large Camera Deployments
Samvit Jain, Ganesh Ananthanarayanan, Junchen Jiang +2
Driven by advances in computer vision and the falling costs of camera hardware, organizations are deploying video cameras en masse for the spatial monitoring of their physical prem…