Search Results for author:
Found 9 papers, 1 papers with code
Confidant: Customizing Transformer-based LLMs via Collaborative Edge Training
Transformer-based large language models (LLMs) have demonstrated impressive capabilities in a variety of natural language processing (NLP) tasks.
AccEPT: An Acceleration Scheme for Speeding Up Edge Pipeline-parallel Training
Moreover, we propose a bit-level computation-efficient data compression scheme to compress the data to be transmitted between devices during training.
Turbo: Opportunistic Enhancement for Edge Video Analytics
Edge computing is being widely used for video analytics.
GEMEL: Model Merging for Memory-Efficient, Real-Time Video Analytics at the Edge
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.
Custom Object Detection via Multi-Camera Self-Supervised Learning
This paper proposes MCSSL, a self-supervised learning approach for building custom object detection models in multi-camera networks.
Ekya: Continuous Learning of Video Analytics Models on Edge Compute Servers
Compressed models that are deployed on the edge servers for inference suffer from data drift, where the live video data diverges from the training data.
Deep Learning in the Era of Edge Computing: Challenges and Opportunities
The era of edge computing has arrived.
ReXCam: Resource-Efficient, Cross-Camera Video Analytics at Scale
Enterprises are increasingly deploying large camera networks for video analytics.
Scaling Video Analytics Systems to Large Camera Deployments
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 premises.
