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Found 15 papers, 8 papers with code
Matching DNN Compression and Cooperative Training with Resources and Data Availability
To make machine learning (ML) sustainable and apt to run on the diverse devices where relevant data is, it is essential to compress ML models as needed, while still meeting the required learning quality and time performance.
SC2: Supervised Compression for Split Computing
Split computing distributes the execution of a neural network (e. g., for a classification task) between a mobile device and a more powerful edge server.
SmartDet: Context-Aware Dynamic Control of Edge Task Offloading for Mobile Object Detection
We extensively evaluate SmartDet on a real-world testbed composed of a JetSon Nano as mobile device and a GTX 980 Ti as edge server, connected through a Wi-Fi link.
BottleFit: Learning Compressed Representations in Deep Neural Networks for Effective and Efficient Split Computing
We show that BottleFit decreases power consumption and latency respectively by up to 49% and 89% with respect to (w. r. t.)
Federated Deep Reinforcement Learning for the Distributed Control of NextG Wireless Networks
Next Generation (NextG) networks are expected to support demanding tactile internet applications such as augmented reality and connected autonomous vehicles.
Spatio-Temporal Split Learning for Autonomous Aerial Surveillance using Urban Air Mobility (UAM) Networks
This paper utilizes surveillance UAVs for the purpose of detecting the presence of a fire in the streets.
Supervised Compression for Resource-Constrained Edge Computing Systems
There has been much interest in deploying deep learning algorithms on low-powered devices, including smartphones, drones, and medical sensors.
Personalized Stress Monitoring using Wearable Sensors in Everyday Settings
Since stress contributes to a broad range of mental and physical health problems, the objective assessment of stress is essential for behavioral and physiological studies.
A Deep Learning Approach to Predict Blood Pressure from PPG Signals
Based on these similarities, in recent years several methods were proposed to predict BP from the PPG signal.
Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges
Mobile devices such as smartphones and autonomous vehicles increasingly rely on deep neural networks (DNNs) to execute complex inference tasks such as image classification and speech recognition, among others.
Head Network Distillation: Splitting Distilled Deep Neural Networks for Resource-Constrained Edge Computing Systems
In this paper, we propose to modify the structure and training process of DNN models for complex image classification tasks to achieve in-network compression in the early network layers.
Frequency-based Multi Task learning With Attention Mechanism for Fault Detection In Power Systems
The prompt and accurate detection of faults and abnormalities in electric transmission lines is a critical challenge in smart grid systems.
Neural Compression and Filtering for Edge-assisted Real-time Object Detection in Challenged Networks
However, poor conditions of the wireless channel connecting the mobile devices to the edge servers may degrade the overall capture-to-output delay achieved by edge offloading.
Split Computing for Complex Object Detectors: Challenges and Preliminary Results
Following the trends of mobile and edge computing for DNN models, an intermediate option, split computing, has been attracting attentions from the research community.
Distilled Split Deep Neural Networks for Edge-Assisted Real-Time Systems
Offloading the execution of complex Deep Neural Networks (DNNs) models to compute-capable devices at the network edge, that is, edge servers, can significantly reduce capture-to-output delay.
