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Edge-computing

Deep Learning on EDGE devices

Papers

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Showing 1176–1200 of 1279 papers

TitleStatusHype
BottleNet++: An End-to-End Approach for Feature Compression in Device-Edge Co-Inference SystemsCode0
Integrating PHY Security Into NDN-IoT Networks By Exploiting MEC: Authentication Efficiency, Robustness, and Accuracy Enhancement—0
Bandwidth Slicing to Boost Federated Learning in Edge Computing—0
Deep Learning at the Edge—0
Joint Computation and Communication Design for UAV-Assisted Mobile Edge Computing in IoT—0
Latency Minimization for Intelligent Reflecting Surface Aided Mobile Edge Computing—0
Energy-Efficient Task Offloading for Vehicular Edge Computing: Joint Optimization of Offloading and Bit Allocation—0
Mobile Edge Computing in Unmanned Aerial Vehicle Networks—0
xYOLO: A Model For Real-Time Object Detection In Humanoid Soccer On Low-End Hardware—0
Multiplierless and Sparse Machine Learning based on Margin Propagation Networks—0
Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computing—0
LabelSens: Enabling Real-time Sensor Data Labelling at the point of Collection on Edge Computing—0
Distilled Split Deep Neural Networks for Edge-Assisted Real-Time SystemsCode1
EdgeCNN: Convolutional Neural Network Classification Model with small inputs for Edge ComputingCode0
Federated Learning in Mobile Edge Networks: A Comprehensive Survey—0
High performance RNNs with spiking neurons—0
A Lightweight Deep Learning Model for Human Activity Recognition on Edge Devices—0
Core Network Management Procedures for Self-Organized and Sustainable 5G Cellular Networks—0
Dependency-Aware Computation Offloading in Mobile Edge Computing: A Reinforcement Learning Approach—0
High-Throughput In-Memory Computing for Binary Deep Neural Networks with Monolithically Integrated RRAM and 90nm CMOS—0
F-Cooper: Feature based Cooperative Perception for Autonomous Vehicle Edge Computing System Using 3D Point Clouds—0
I-SAFE: Instant Suspicious Activity identiFication at the Edge using Fuzzy Decision Making—0
Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networksCode0
A Deep Reinforcement Learning Approach to Multi-component Job Scheduling in Edge Computing—0
Privacy-Preserved Task Offloading in Mobile Blockchain with Deep Reinforcement Learning—0
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