Papers Edge-computing
“Edge-computing” 태그가 달린 논문 1,279편 · 필터 해제
FedEdge AI-TC: A Semi-supervised Traffic Classification Method based on Trusted Federated Deep Learning for Mobile Edge Computing
As a typical entity of MEC (Mobile Edge Computing), 5G CPE (Customer Premise Equipment)/HGU (Home Gateway Unit) has proven to be a promising alternative to traditional Smart Home Gateway. Network TC (Traffic Classificati…
Edge-computingFederated LearningModel CompressionTraffic ClassificationA Fog-based Smart Agriculture System to Detect Animal Intrusion
Smart agriculture is one of the most promising areas where IoT-enabled technologies have the potential to substantially improve the quality and quantity of the crops and reduce the associated operational cost. However, b…
Cloud ComputingEdge-computingCost-effective On-device Continual Learning over Memory Hierarchy with Miro
Continual learning (CL) trains NN models incrementally from a continuous stream of tasks. To remember previously learned knowledge, prior studies store old samples over a memory hierarchy and replay them when new tasks a…
Continual LearningEdge-computingMachine Learning aided Computer Architecture Design for CNN Inferencing Systems
Efficient and timely calculations of Machine Learning (ML) algorithms are essential for emerging technologies like autonomous driving, the Internet of Things (IoT), and edge computing. One of the primary ML algorithms us…
Autonomous DrivingEdge-computingReal-time FPGA Implementation of CNN-based Distributed Fiber Optic Vibration Event Recognition Method
Utilizing optical fibers to detect and pinpoint vibrations, Distributed Optical Fiber Vibration Sensing (DVS) technology provides real-time monitoring and surveillance of wide-reaching areas. This field has been leveragi…
CPUEdge-computingGPUCore interface optimization for multi-core neuromorphic processors
Hardware implementations of Spiking Neural Networks (SNNs) represent a promising approach to edge-computing for applications that require low-power and low-latency, and which cannot resort to external cloud-based computi…
Edge-computingKnowledge-Driven Multi-Agent Reinforcement Learning for Computation Offloading in Cybertwin-Enabled Internet of Vehicles
By offloading computation-intensive tasks of vehicles to roadside units (RSUs), mobile edge computing (MEC) in the Internet of Vehicles (IoV) can relieve the onboard computation burden. However, existing model-based task…
Edge-computingMulti-agent Reinforcement LearningBenchmarking Jetson Edge Devices with an End-to-end Video-based Anomaly Detection System
Innovative enhancement in embedded system platforms, specifically hardware accelerations, significantly influence the application of deep learning in real-world scenarios. These innovations translate human labor efforts …
Anomaly DetectionAutonomous DrivingBenchmarkingDeep Learning+2Training Latency Minimization for Model-Splitting Allowed Federated Edge Learning
To alleviate the shortage of computing power faced by clients in training deep neural networks (DNNs) using federated learning (FL), we leverage the edge computing and split learning to propose a model-splitting allowed …
Edge-computingFederated LearningDigital Modeling on Large Kernel Metamaterial Neural Network
Deep neural networks (DNNs) utilized recently are physically deployed with computational units (e.g., CPUs and GPUs). Such a design might lead to a heavy computational burden, significant latency, and intensive power con…
Edge-computingJoint Service Caching, Communication and Computing Resource Allocation in Collaborative MEC Systems: A DRL-based Two-timescale Approach
Meeting the strict Quality of Service (QoS) requirements of terminals has imposed a signiffcant challenge on Multiaccess Edge Computing (MEC) systems, due to the limited multidimensional resources. To address this challe…
Deep Reinforcement LearningEdge-computingEnhancing Network Slicing Architectures with Machine Learning, Security, Sustainability and Experimental Networks Integration
Network Slicing (NS) is an essential technique extensively used in 5G networks computing strategies, mobile edge computing, mobile cloud computing, and verticals like the Internet of Vehicles and industrial IoT, among ot…
Cloud ComputingEdge-computingNonlinear optical encoding enabled by recurrent linear scattering
Optical information processing and computing can potentially offer enhanced performance, scalability and energy efficiency. However, achieving nonlinearity-a critical component of computation-remains challenging in the o…
Data CompressionDecoderDeep LearningEdge-computing+5A Fast Task Offloading Optimization Framework for IRS-Assisted Multi-Access Edge Computing System
Terahertz communication networks and intelligent reflecting surfaces exhibit significant potential in advancing wireless networks, particularly within the domain of aerial-based multi-access edge computing systems. These…
Combinatorial OptimizationEdge-computingCorticomorphic Hybrid CNN-SNN Architecture for EEG-based Low-footprint Low-latency Auditory Attention Detection
In a multi-speaker "cocktail party" scenario, a listener can selectively attend to a speaker of interest. Studies into the human auditory attention network demonstrate cortical entrainment to speech envelopes resulting i…
Edge-computingEEGEfficient Task Offloading Algorithm for Digital Twin in Edge/Cloud Computing Environment
In the era of Internet of Things (IoT), Digital Twin (DT) is envisioned to empower various areas as a bridge between physical objects and the digital world. Through virtualization and simulation techniques, multiple func…
Cloud ComputingDecision MakingEdge-computingNeuromorphic analog circuits for robust on-chip always-on learning in spiking neural networks
Mixed-signal neuromorphic systems represent a promising solution for solving extreme-edge computing tasks without relying on external computing resources. Their spiking neural network circuits are optimized for processin…
Edge-computingSearch-time Efficient Device Constraints-Aware Neural Architecture Search
Edge computing aims to enable edge devices, such as IoT devices, to process data locally instead of relying on the cloud. However, deep learning techniques like computer vision and natural language processing can be comp…
Edge-computingimage-classificationImage ClassificationNeural Architecture SearchEdge Storage Management Recipe with Zero-Shot Data Compression for Road Anomaly Detection
Recent studies show edge computing-based road anomaly detection systems which may also conduct data collection simultaneously. However, the edge computers will have small data storage but we need to store the collected a…
Anomaly DetectionAudio CompressionAudio Super-ResolutionData Compression+3Social-Mobility-Aware Joint Communication and Computation Resource Management in NOMA-Enabled Vehicular Networks
The existing computation and communication (2C) optimization schemes for vehicular edge computing (VEC) networks mainly focus on the physical domain without considering the influence from the social domain. This may grea…
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