Papers Edge-computing
“Edge-computing” 태그가 달린 논문 1,279편 · 필터 해제
Hyperspectral Image Classification Using Deep Matrix Capsules
Hyperspectral image (HSI) classification is used in multiple domains like precision agriculture, mineral exploration, remote sensing, and others. Conventionally, couvolutional neural networks (CNNs) were used in HSI clas…
ClassificationEdge-computingHyperspectral Image Classificationimage-classification+1Energy-Efficient Vehicular Edge Computing with One-by-one Access Scheme
With the advent of ever-growing vehicular applications, vehicular edge computing (VEC) has been a promising solution to augment the computing capacity of future smart vehicles. The ultimate challenge to fulfill the quali…
Edge-computingSchedulingScheduling Inference Workloads on Distributed Edge Clusters with Reinforcement Learning
Many real-time applications (e.g., Augmented/Virtual Reality, cognitive assistance) rely on Deep Neural Networks (DNNs) to process inference tasks. Edge computing is considered a key infrastructure to deploy such applica…
Edge-computingManagementreinforcement-learningReinforcement Learning+2SMDP-Based Dynamic Batching for Efficient Inference on GPU-Based Platforms
In up-to-date machine learning (ML) applications on cloud or edge computing platforms, batching is an important technique for providing efficient and economical services at scale. In particular, parallel computing resour…
Edge-computingGPUV2N Service Scaling with Deep Reinforcement Learning
The fifth generation (5G) of wireless networks is set out to meet the stringent requirements of vehicular use cases. Edge computing resources can aid in this direction by moving processing closer to end-users, reducing l…
Deep Reinforcement LearningEdge-computingreinforcement-learningReinforcement Learning+1PhyCV: The First Physics-inspired Computer Vision Library
PhyCV is the first computer vision library which utilizes algorithms directly derived from the equations of physics governing physical phenomena. The algorithms appearing in the current release emulate, in a metaphoric s…
Edge-computingAnticipate, Ensemble and Prune: Improving Convolutional Neural Networks via Aggregated Early Exits
Today, artificial neural networks are the state of the art for solving a variety of complex tasks, especially in image classification. Such architectures consist of a sequence of stacked layers with the aim of extracting…
Edge-computingimage-classificationImage ClassificationTime-sensitive Learning for Heterogeneous Federated Edge Intelligence
Real-time machine learning has recently attracted significant interest due to its potential to support instantaneous learning, adaptation, and decision making in a wide range of application domains, including self-drivin…
Decision MakingEdge-computingFederated LearningPCBDet: An Efficient Deep Neural Network Object Detection Architecture for Automatic PCB Component Detection on the Edge
There can be numerous electronic components on a given PCB, making the task of visual inspection to detect defects very time-consuming and prone to error, especially at scale. There has thus been significant interest in …
Edge-computingobject-detectionObject DetectionResource Optimization for Semantic-Aware Networks with Task Offloading
The limited capabilities of user equipment restrict the local implementation of computation-intensive applications. Edge computing, especially the edge intelligence system, enables local users to offload the computation …
Edge-computingManagementMulti-agent Reinforcement Learningreinforcement-learning+1HiFlash: Communication-Efficient Hierarchical Federated Learning with Adaptive Staleness Control and Heterogeneity-aware Client-Edge Association
Federated learning (FL) is a promising paradigm that enables collaboratively learning a shared model across massive clients while keeping the training data locally. However, for many existing FL systems, clients need to …
Deep Reinforcement LearningEdge-computingFederated LearningOptimization of the energy efficiency in Smart Internet of Vehicles assisted by MEC
Smart Internet of Vehicles (IoV) as a promising application in Internet of Things (IoT) emerges with the development of the fifth generation mobile communication (5G). Nevertheless, the heterogeneous requirements of suff…
CPUEdge-computingAdaptSLAM: Edge-Assisted Adaptive SLAM with Resource Constraints via Uncertainty Minimization
Edge computing is increasingly proposed as a solution for reducing resource consumption of mobile devices running simultaneous localization and mapping (SLAM) algorithms, with most edge-assisted SLAM systems assuming the…
Edge-computingSimultaneous Localization and MappingMarine IoT Systems with Space-Air-Sea Integrated Networks: Hybrid LEO and UAV Edge Computing
Marine Internet of Things (IoT) systems have grown substantially with the development of non-terrestrial networks (NTN) via aerial and space vehicles in the upcoming sixth-generation (6G), thereby assisting environment p…
Edge-computingEnergy Disaggregation & Appliance Identification in a Smart Home: Transfer Learning enables Edge Computing
Non-intrusive load monitoring (NILM) or energy disaggregation aims to extract the load profiles of individual consumer electronic appliances, given an aggregate load profile of the mains of a smart home. This work propos…
Edge-computingNon-Intrusive Load MonitoringTransfer LearningSAFEMYRIDES: Application of Decentralized Control Edge-Computing to Ridesharing Monitoring Services
Edge computing is changing the face of many industries and services. Common edge computing models offload computing which is prone to security risks and privacy violation. However, advances in deep learning enabled Inter…
Edge-computingIn-situ monitoring additive manufacturing process with AI edge computing
In-situ monitoring system can be used to monitor the quality of additive manufacturing (AM) processes. In the case of digital image correlation (DIC) based in-situ monitoring systems, high-speed cameras were used to capt…
ARCEdge-computingSuper-ResolutionVideo Super-ResolutionFedICT: Federated Multi-task Distillation for Multi-access Edge Computing
The growing interest in intelligent services and privacy protection for mobile devices has given rise to the widespread application of federated learning in Multi-access Edge Computing (MEC). Diverse user behaviors call …
Edge-computingFederated LearningKnowledge DistillationMulti-Task LearningAccuracy-Guaranteed Collaborative DNN Inference in Industrial IoT via Deep Reinforcement Learning
Collaboration among industrial Internet of Things (IoT) devices and edge networks is essential to support computation-intensive deep neural network (DNN) inference services which require low delay and high accuracy. Samp…
Deep Reinforcement LearningEdge-computingGeneral Reinforcement Learningreinforcement-learning+1How to Share: Balancing Layer and Chain Sharing in Industrial Microservice Deployment
With the rapid development of smart manufacturing, edge computing-oriented microservice platforms are emerging as an important part of production control. In the containerized deployment of microservices, layer sharing c…
Edge-computing