Papers Edge-computing
“Edge-computing” 태그가 달린 논문 1,279편 · 필터 해제
Mobile Traffic Prediction at the Edge Through Distributed and Deep Transfer Learning
Traffic prediction represents one of the crucial tasks for smartly optimizing the mobile network. Recently, Artificial Intelligence (AI) has attracted attention to solve this problem thanks to its ability in cognizing th…
Edge-computingExplainable artificial intelligenceTraffic PredictionTransfer LearningUnveiling Energy Efficiency in Deep Learning: Measurement, Prediction, and Scoring across Edge Devices
Today, deep learning optimization is primarily driven by research focused on achieving high inference accuracy and reducing latency. However, the energy efficiency aspect is often overlooked, possibly due to a lack of su…
Deep LearningEdge-computingCloud-Magnetic Resonance Imaging System: In the Era of 6G and Artificial Intelligence
Magnetic Resonance Imaging (MRI) plays an important role in medical diagnosis, generating petabytes of image data annually in large hospitals. This voluminous data stream requires a significant amount of network bandwidt…
Cloud ComputingDiagnosticEdge-computingFederated Learning+2A Blockchain-empowered Multi-Aggregator Federated Learning Architecture in Edge Computing with Deep Reinforcement Learning Optimization
Federated learning (FL) is emerging as a sought-after distributed machine learning architecture, offering the advantage of model training without direct exposure of raw data. With advancements in network infrastructure, …
Deep Reinforcement LearningEdge-computingFederated Learningreinforcement-learning+1Performance/power assessment of CNN packages on embedded automotive platforms
The rise of power-efficient embedded computers based on highly-parallel accelerators opens a number of opportunities and challenges for researchers and engineers, and paved the way to the era of edge computing. At the sa…
Autonomous DrivingEdge-computingobject-detectionObject DetectionA Lightweight Video Anomaly Detection Model with Weak Supervision and Adaptive Instance Selection
Video anomaly detection is to determine whether there are any abnormal events, behaviors or objects in a given video, which enables effective and intelligent public safety management. As video anomaly labeling is both ti…
Anomaly DetectionEdge-computingVideo Anomaly DetectionWeakly-supervised Learning+1Edge Computing-Enabled Road Condition Monitoring: System Development and Evaluation
Real-time pavement condition monitoring provides highway agencies with timely and accurate information that could form the basis of pavement maintenance and rehabilitation policies. Existing technologies rely heavily on …
Edge-computingEdgeFD: An Edge-Friendly Drift-Aware Fault Diagnosis System for Industrial IoT
Recent transfer learning (TL) approaches in industrial intelligent fault diagnosis (FD) mostly follow the "pre-train and fine-tuning" paradigm to address data drift, which emerges from variable working conditions. Howeve…
Continual LearningDrift DetectionEdge-computingFault Diagnosis+1Federated Fine-Tuning of LLMs on the Very Edge: The Good, the Bad, the Ugly
Large Language Models (LLM) and foundation models are popular as they offer new opportunities for individuals and businesses to improve natural language processing, interact with data, and retrieve information faster. Ho…
Computational EfficiencyEdge-computingFederated LearningGPU+1DYNAP-SE2: a scalable multi-core dynamic neuromorphic asynchronous spiking neural network processor
With the remarkable progress that technology has made, the need for processing data near the sensors at the edge has increased dramatically. The electronic systems used in these applications must process data continuousl…
Edge-computingQuantum-Based Feature Selection for Multi-classification Problem in Complex Systems with Edge Computing
The complex systems with edge computing require a huge amount of multi-feature data to extract appropriate insights for their decision making, so it is important to find a feasible feature selection method to improve the…
Computational EfficiencyDecision MakingEdge-computingfeature selectionQuantization of Deep Neural Networks to facilitate self-correction of weights on Phase Change Memory-based analog hardware
In recent years, hardware-accelerated neural networks have gained significant attention for edge computing applications. Among various hardware options, crossbar arrays, offer a promising avenue for efficient storage and…
Edge-computingQuantizationAdversarial Machine Learning in Latent Representations of Neural Networks
Distributed deep neural networks (DNNs) have been shown to reduce the computational burden of mobile devices and decrease the end-to-end inference latency in edge computing scenarios. While distributed DNNs have been stu…
Adversarial RobustnessEdge-computingEnergy-Efficient Secure Offloading System Designed via UAV-Mounted Intelligent Reflecting Surface for Resilience Enhancement
With increasing interest in mmWave and THz communication systems, an unmanned aerial vehicle (UAV)-mounted intelligent reflecting surface (IRS) has been suggested as a key enabling technology to establish robust line-of-…
Disaster ResponseEdge-computingPushing Large Language Models to the 6G Edge: Vision, Challenges, and Opportunities
Large language models (LLMs), which have shown remarkable capabilities, are revolutionizing AI development and potentially shaping our future. However, given their multimodality, the status quo cloud-based deployment fac…
Edge-computingparameter-efficient fine-tuningQuantizationOnline Resource Allocation for Semantic-Aware Edge Computing Systems
In this paper, we propose a semantic-aware joint communication and computation resource allocation framework for MEC systems. In the considered system, random tasks arrive at each terminal device (TD), which needs to be …
Edge-computingResource Allocation for Semantic-Aware Mobile Edge Computing Systems
In this paper, a semantic-aware joint communication and computation resource allocation framework is proposed for mobile edge computing (MEC) systems. In the considered system, each terminal device (TD) has a computation…
Edge-computingManagementContaining Analog Data Deluge at Edge through Frequency-Domain Compression in Collaborative Compute-in-Memory Networks
Edge computing is a promising solution for handling high-dimensional, multispectral analog data from sensors and IoT devices for applications such as autonomous drones. However, edge devices' limited storage and computin…
Edge-computingTask Graph offloading via Deep Reinforcement Learning in Mobile Edge Computing
Various mobile applications that comprise dependent tasks are gaining widespread popularity and are increasingly complex. These applications often have low-latency requirements, resulting in a significant surge in demand…
Deep Reinforcement LearningEdge-computingreinforcement-learningSchedulingExploration of TPUs for AI Applications
Tensor Processing Units (TPUs) are specialized hardware accelerators for deep learning developed by Google. This paper aims to explore TPUs in cloud and edge computing focusing on its applications in AI. We provide an ov…
BenchmarkingEdge-computing