Papers Edge-computing
“Edge-computing” 태그가 달린 논문 1,279편 · 필터 해제
Assisted Physical Interaction: Autonomous Aerial Robots with Neural Network Detection, Navigation, and Safety Layers
The paper introduces a novel framework for safe and autonomous aerial physical interaction in industrial settings. It comprises two main components: a neural network-based target detection system enhanced with edge compu…
Edge-computingPose EstimationRepresentation Similarity: A Better Guidance of DNN Layer Sharing for Edge Computing without Training
Edge computing has emerged as an alternative to reduce transmission and processing delay and preserve privacy of the video streams. However, the ever-increasing complexity of Deep Neural Networks (DNNs) used in video-bas…
Edge-computingobject-detectionObject DetectionRSSI-Assisted CSI-Based Passenger Counting with Multiple Wi-Fi Receivers
Passenger counting is crucial for public transport vehicle scheduling and traffic capacity evaluation. However, most existing methods are either costly or with low counting accuracy, leading to the recent use of Wi-Fi si…
Edge-computingSchedulingDifferentiable Weightless Neural Networks
We introduce the Differentiable Weightless Neural Network (DWN), a model based on interconnected lookup tables. Training of DWNs is enabled by a novel Extended Finite Difference technique for approximate differentiation …
Edge-computingImproving accuracy and convergence of federated learning edge computing methods for generalized DER forecasting applications in power grid
This proposal aims to develop more accurate federated learning (FL) methods with faster convergence properties and lower communication requirements, specifically for forecasting distributed energy resources (DER) such as…
Edge-computingFederated LearningLoad ForecastingTime SeriesAn Ensemble Scheme for Proactive Dominant Data Migration of Pervasive Tasks at the Edge
Nowadays, a significant focus within the research community on the intelligent management of data at the confluence of the Internet of Things (IoT) and Edge Computing (EC) is observed. In this manuscript, we propose a sc…
Edge-computingEfficient Multi-Object Tracking on Edge Devices via Reconstruction-Based Channel Pruning
The advancement of multi-object tracking (MOT) technologies presents the dual challenge of maintaining high performance while addressing critical security and privacy concerns. In applications such as pedestrian tracking…
Edge-computingMulti-Object TrackingNetwork PruningObject TrackingEdge AI Collaborative Learning: Bayesian Approaches to Uncertainty Estimation
Recent advancements in edge computing have significantly enhanced the AI capabilities of Internet of Things (IoT) devices. However, these advancements introduce new challenges in knowledge exchange and resource managemen…
Edge-computingTowards AI-Native Software Engineering (SE 3.0): A Vision and a Challenge Roadmap
The rise of AI-assisted software engineering (SE 2.0), powered by Foundation Models (FMs) and FM-powered copilots, has shown promise in improving developer productivity. However, it has also exposed inherent limitations,…
Edge-computingVariable Resolution Pixel Quantization for Low Power Machine Vision Application on Edge
This work describes an approach towards pixel quantization using variable resolution which is made feasible using image transformation in the analog domain. The main aim is to reduce the average bits-per-pixel (BPP) nece…
Edge-computingimage-classificationImage ClassificationQuantizationA 9T4R RRAM-Based ACAM for Analogue Template Matching at the Edge
The continuous shift of computational bottlenecks to the memory access and data transfer, especially for AI applications, poses the urgent needs of re-engineering the computer architecture fundamentals. Many edge computi…
Edge-computingTemplate MatchingA Novel Framework of Horizontal-Vertical Hybrid Federated Learning for EdgeIoT
This letter puts forth a new hybrid horizontal-vertical federated learning (HoVeFL) for mobile edge computing-enabled Internet of Things (EdgeIoT). In this framework, certain EdgeIoT devices train local models using the …
Edge-computingFederated LearningVertical Federated LearningDeep Learning and Machine Learning, Advancing Big Data Analytics and Management: Unveiling AI's Potential Through Tools, Techniques, and Applications
Artificial intelligence (AI), machine learning, and deep learning have become transformative forces in big data analytics and management, enabling groundbreaking advancements across diverse industries. This article delve…
AutoMLEdge-computingFairnessManagement+1IoV-Oriented Integrated Sensing, Computation, and Communication: System Design and Resource Allocation
—In future mobile communication systems, the deep integration of communication, sensing, and computation has become a new trend. This article paper designs an integrated sensing, computation, and communication (ISCC) sy…
Deep Reinforcement LearningEdge-computingIntegrated sensing and communicationISACEfficient Driving Behavior Narration and Reasoning on Edge Device Using Large Language Models
Deep learning architectures with powerful reasoning capabilities have driven significant advancements in autonomous driving technology. Large language models (LLMs) applied in this field can describe driving scenes and b…
Autonomous DrivingEdge-computingResource Allocation for Stable LLM Training in Mobile Edge Computing
As mobile devices increasingly become focal points for advanced applications, edge computing presents a viable solution to their inherent computational limitations, particularly in deploying large language models (LLMs).…
Edge-computingparameter-efficient fine-tuningEnergy-Efficient Computation with DVFS using Deep Reinforcement Learning for Multi-Task Systems in Edge Computing
Finding an optimal energy-efficient policy that is adaptable to underlying edge devices while meeting deadlines for tasks has always been challenging. This research studies generalized systems with multi-task, multi-dead…
Deep Reinforcement LearningEdge-computingreinforcement-learningReinforcement LearningBenchmarking Deep Learning Models for Object Detection on Edge Computing Devices
Modern applications, such as autonomous vehicles, require deploying deep learning algorithms on resource-constrained edge devices for real-time image and video processing. However, there is limited understanding of the e…
Autonomous VehiclesBenchmarkingEdge-computingobject-detection+2Asynchronous Fractional Multi-Agent Deep Reinforcement Learning for Age-Minimal Mobile Edge Computing
In the realm of emerging real-time networked applications like cyber-physical systems (CPS), the Age of Information (AoI) has merged as a pivotal metric for evaluating the timeliness. To meet the high computational deman…
Deep Reinforcement LearningEdge-computingReinforcement Learning (RL)SchedulingMean Age of Information in Partial Offloading Mobile Edge Computing Networks
The age of information (AoI) performance analysis is essential for evaluating the information freshness in the large-scale mobile edge computing (MEC) networks. This work proposes the earliest analysis of the mean AoI (M…
Edge-computing