Papers Edge-computing
“Edge-computing” 태그가 달린 논문 1,279편 · 필터 해제
Latency optimized Deep Neural Networks (DNNs): An Artificial Intelligence approach at the Edge using Multiprocessor System on Chip (MPSoC)
Almost in every heavily computation-dependent application, from 6G communication systems to autonomous driving platforms, a large portion of computing should be near to the client side. Edge computing (AI at Edge) in mob…
Autonomous DrivingEdge-computingQuality Scalable Quantization Methodology for Deep Learning on Edge
Deep Learning Architectures employ heavy computations and bulk of the computational energy is taken up by the convolution operations in the Convolutional Neural Networks. The objective of our proposed work is to reduce t…
Deep LearningEdge-computingQuantizationTowards Efficient Deployment of Hybrid SNNs on Neuromorphic and Edge AI Hardware
This paper explores the synergistic potential of neuromorphic and edge computing to create a versatile machine learning (ML) system tailored for processing data captured by dynamic vision sensors. We construct and train …
Edge-computingDistributed Deep Reinforcement Learning Based Gradient Quantization for Federated Learning Enabled Vehicle Edge Computing
Federated Learning (FL) can protect the privacy of the vehicles in vehicle edge computing (VEC) to a certain extent through sharing the gradients of vehicles' local models instead of local data. The gradients of vehicles…
Deep Reinforcement LearningEdge-computingFederated LearningQuantizationResource Allocation for Twin Maintenance and Computing Task Processing in Digital Twin Vehicular Edge Computing Network
As a promising technology, vehicular edge computing (VEC) can provide computing and caching services by deploying VEC servers near vehicles. However, VEC networks still face challenges such as high vehicle mobility. Digi…
Deep Reinforcement LearningEdge-computingSchedulingMobile Edge Intelligence for Large Language Models: A Contemporary Survey
On-device large language models (LLMs), referring to running LLMs on edge devices, have raised considerable interest since they are more cost-effective, latency-efficient, and privacy-preserving compared with the cloud p…
Cloud ComputingEdge-computingPrivacy PreservingSurveyAdaPI: Facilitating DNN Model Adaptivity for Efficient Private Inference in Edge Computing
Private inference (PI) has emerged as a promising solution to execute computations on encrypted data, safeguarding user privacy and model parameters in edge computing. However, existing PI methods are predominantly devel…
Edge-computingExploiting Heterogeneity in Timescales for Sparse Recurrent Spiking Neural Networks for Energy-Efficient Edge Computing
Spiking Neural Networks (SNNs) represent the forefront of neuromorphic computing, promising energy-efficient and biologically plausible models for complex tasks. This paper weaves together three groundbreaking studies th…
Edge-computingEdge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligence
Recent years have witnessed a thriving growth of computing facilities connected at the network edge, cultivating edge networks as a fundamental infrastructure for supporting miscellaneous intelligent services.Meanwhile, …
Edge-computingGraph LearningGraph Representation LearningMiscellaneous+1Feature Attenuation of Defective Representation Can Resolve Incomplete Masking on Anomaly Detection
In unsupervised anomaly detection (UAD) research, while state-of-the-art models have reached a saturation point with extensive studies on public benchmark datasets, they adopt large-scale tailor-made neural networks (NN)…
Anomaly DetectionEdge-computingUnsupervised Anomaly DetectionMulti-Time Scale Service Caching and Pricing in MEC Systems with Dynamic Program Popularity
In mobile edge computing systems, base stations (BSs) equipped with edge servers can provide computing services to users to reduce their task execution time. However, there is always a conflict of interest between the BS…
Deep Reinforcement LearningEdge-computingQET: Enhancing Quantized LLM Parameters and KV cache Compression through Element Substitution and Residual Clustering
The matrix quantization entails representing matrix elements in a more space-efficient form to reduce storage usage, with dequantization restoring the original matrix for use. We formulate the Quantization Error Minimiza…
Computational EfficiencyEdge-computingImage CompressionLarge Language Model+4YOLOv5, YOLOv8 and YOLOv10: The Go-To Detectors for Real-time Vision
This paper presents a comprehensive review of the evolution of the YOLO (You Only Look Once) object detection algorithm, focusing on YOLOv5, YOLOv8, and YOLOv10. We analyze the architectural advancements, performance imp…
Edge-computingobject-detectionObject DetectionMulti-Task Decision-Making for Multi-User 360 Video Processing over Wireless Networks
We study a multi-task decision-making problem for 360 video processing in a wireless multi-user virtual reality (VR) system that includes an edge computing unit (ECU) to deliver 360 videos to VR users and offer computing…
Decision MakingDeep Reinforcement LearningEdge-computingOptimizing Age of Information in Vehicular Edge Computing with Federated Graph Neural Network Multi-Agent Reinforcement Learning
With the rapid development of intelligent vehicles and Intelligent Transport Systems (ITS), the sensors such as cameras and LiDAR installed on intelligent vehicles provides higher capacity of executing computation-intens…
Deep Reinforcement LearningEdge-computingFederated LearningGraph Neural Network+1Terahertz Communication Multi-UAV-Assisted Mobile Edge Computing System
Mobile edge computing (MEC) and terahertz (THz)enabled unmanned aerial vehicle (UAV) communication systems are gaining significant attention for improving user service delays in future mobile networks. This article intro…
Edge-computingKoopman based trajectory model and computation offloading for high mobility paradigm in ISAC enabled IoT system
User experience on mobile devices is constrained by limited battery capacity and processing power, but 6G technology advancements are diving rapidly into mobile technical evolution. Mobile edge computing (MEC) offers a s…
Edge-computingIntegrated sensing and communicationISACTrajectory PredictionRepAct: The Re-parameterizable Adaptive Activation Function
Addressing the imperative need for efficient artificial intelligence in IoT and edge computing, this study presents RepAct, a re-parameterizable adaptive activation function tailored for optimizing lightweight neural net…
Edge-computingimage-classificationImage Classificationobject-detection+1Decentralized Task Offloading and Load-Balancing for Mobile Edge Computing in Dense Networks
We study the problem of decentralized task offloading and load-balancing in a dense network with numerous devices and a set of edge servers. Solving this problem optimally is complicated due to the unknown network inform…
Decision MakingEdge-computingDigital Twinning of a Pressurized Water Reactor Startup Operation and Partial Computational Offloading in In-network Computing-Assisted Multiaccess Edge Computing
This paper addresses the challenge of representing complex human action (HA) in a nuclear power plant (NPP) digital twin (DT) and minimizing latency in partial computation offloading (PCO) in sixth-generation-enabled com…
Edge-computing