Papers Edge-computing
“Edge-computing” 태그가 달린 논문 1,279편 · 필터 해제
GiPH: Generalizable Placement Learning for Adaptive Heterogeneous Computing
Careful placement of a computational application within a target device cluster is critical for achieving low application completion time. The problem is challenging due to its NP-hardness and combinatorial nature. In re…
Edge-computingGraph Neural NetworkSpatiotemporal Attention-based Semantic Compression for Real-time Video Recognition
This paper studies the computational offloading of video action recognition in edge computing. To achieve effective semantic information extraction and compression, following semantic communication we propose a novel spa…
Action RecognitionDecoderEdge-computingSemantic Communication+3LightESD: Fully-Automated and Lightweight Anomaly Detection Framework for Edge Computing
Anomaly detection is widely used in a broad range of domains from cybersecurity to manufacturing, finance, and so on. Deep learning based anomaly detection has recently drawn much attention because of its superior capabi…
Anomaly DetectionCPUEdge-computingAttention-based QoE-aware Digital Twin Empowered Edge Computing for Immersive Virtual Reality
Metaverse applications such as virtual reality (VR) content streaming, require optimal resource allocation strategies for mobile edge computing (MEC) to ensure a high-quality user experience. In contrast to online reinfo…
Continual LearningEdge-computingFairnessReinforcement Learning (RL)MetaMorphosis: Task-oriented Privacy Cognizant Feature Generation for Multi-task Learning
With the growth of computer vision applications, deep learning, and edge computing contribute to ensuring practical collaborative intelligence (CI) by distributing the workload among edge devices and the cloud. However, …
Deep LearningDepth EstimationEdge-computingMulti-Task Learning+2Deep Reinforcement Learning Based Resource Allocation for Cloud Native Wireless Network
Cloud native technology has revolutionized 5G beyond and 6G communication networks, offering unprecedented levels of operational automation, flexibility, and adaptability. However, the vast array of cloud native services…
Cloud ComputingDeep Reinforcement LearningEdge-computingreinforcement-learningArchitectural Vision for Quantum Computing in the Edge-Cloud Continuum
Quantum processing units (QPUs) are currently exclusively available from cloud vendors. However, with recent advancements, hosting QPUs is soon possible everywhere. Existing work has yet to draw from research in edge com…
Collaborative InferenceDistributed ComputingEdge-computingImage Classification+1A Feasibility Study on Indoor Localization and Multi-person Tracking Using Sparsely Distributed Camera Network with Edge Computing
Camera-based activity monitoring systems are becoming an attractive solution for smart building applications with the advances in computer vision and edge computing technologies. In this paper, we present a feasibility s…
Edge-computingHuman DetectionIndoor LocalizationMulti-Person Pose Estimation+4Dynamic resource allocation scheme for mobile edge computing
Mobile edge computing is a promising paradigm that provides edge users with dependable computing services. However, due to the dynamic nature of mobile users and the limited resources of edge servers, it is essential t…
Edge-computingGenerative Adversarial NetworkEfficient Federated Learning with Enhanced Privacy via Lottery Ticket Pruning in Edge Computing
Federated learning (FL) is a collaborative learning paradigm for decentralized private data from mobile terminals (MTs). However, it suffers from issues in terms of communication, resource of MTs, and privacy. Existing p…
Edge-computingFederated LearningPrivacy PreservingMoccasin: Efficient Tensor Rematerialization for Neural Networks
The deployment and training of neural networks on edge computing devices pose many challenges. The low memory nature of edge devices is often one of the biggest limiting factors encountered in the deployment of large neu…
Edge-computingCausal Semantic Communication for Digital Twins: A Generalizable Imitation Learning Approach
A digital twin (DT) leverages a virtual representation of the physical world, along with communication (e.g., 6G), computing (e.g., edge computing), and artificial intelligence (AI) technologies to enable many connected …
Causal InferenceDecision MakingDecoderEdge-computing+3Towards Carbon-Neutral Edge Computing: Greening Edge AI by Harnessing Spot and Future Carbon Markets
Provisioning dynamic machine learning (ML) inference as a service for artificial intelligence (AI) applications of edge devices faces many challenges, including the trade-off among accuracy loss, carbon emission, and unk…
Edge-computingA Survey on Deep Neural Network Partition over Cloud, Edge and End Devices
Deep neural network (DNN) partition is a research problem that involves splitting a DNN into multiple parts and offloading them to specific locations. Because of the recent advancement in multi-access edge computing and …
Edge-computingHeterogeneous Integration of In-Memory Analog Computing Architectures with Tensor Processing Units
Tensor processing units (TPUs), specialized hardware accelerators for machine learning tasks, have shown significant performance improvements when executing convolutional layers in convolutional neural networks (CNNs). H…
Edge-computingReal Time Bearing Fault Diagnosis Based on Convolutional Neural Network and STM32 Microcontroller
With the rapid development of big data and edge computing, many researchers focus on improving the accuracy of bearing fault classification using deep learning models, and implementing the deep learning classification mo…
Deep LearningEdge-computingFault DiagnosisSpeck: A Smart event-based Vision Sensor with a low latency 327K Neuron Convolutional Neuronal Network Processing Pipeline
Edge computing solutions that enable the extraction of high-level information from a variety of sensors is in increasingly high demand. This is due to the increasing number of smart devices that require sensory processin…
Edge-computingEvent-based visionEdge-cloud Collaborative Learning with Federated and Centralized Features
Federated learning (FL) is a popular way of edge computing that doesn't compromise users' privacy. Current FL paradigms assume that data only resides on the edge, while cloud servers only perform model averaging. However…
Edge-computingFederated LearningRecommendation SystemsTransfer LearningRESPECT: Reinforcement Learning based Edge Scheduling on Pipelined Coral Edge TPUs
Deep neural networks (DNNs) have substantial computational and memory requirements, and the compilation of its computational graphs has a great impact on the performance of resource-constrained (e.g., computation, I/O, a…
Edge-computingreinforcement-learningReinforcement Learning (RL)SchedulingPerspectives on AI Architectures and Co-design for Earth System Predictability
Recently, the U.S. Department of Energy (DOE), Office of Science, Biological and Environmental Research (BER), and Advanced Scientific Computing Research (ASCR) programs organized and held the Artificial Intelligence for…
Edge-computingUncertainty Quantification