Papers Edge-computing
“Edge-computing” 태그가 달린 논문 1,279편 · 필터 해제
Rapid Deployment of DNNs for Edge Computing via Structured Pruning at Initialization
Edge machine learning (ML) enables localized processing of data on devices and is underpinned by deep neural networks (DNNs). However, DNNs cannot be easily run on devices due to their substantial computing, memory and e…
Edge-computingModel CompressionNeural Architecture SearchSocialized Learning: A Survey of the Paradigm Shift for Edge Intelligence in Networked Systems
Amidst the robust impetus from artificial intelligence (AI) and big data, edge intelligence (EI) has emerged as a nascent computing paradigm, synthesizing AI with edge computing (EC) to become an exemplary solution for u…
Edge-computingTowards Multi-agent Reinforcement Learning based Traffic Signal Control through Spatio-temporal Hypergraphs
Traffic signal control systems (TSCSs) are integral to intelligent traffic management, fostering efficient vehicle flow. Traditional approaches often simplify road networks into standard graphs, which results in a failur…
Edge-computingManagementMulti-agent Reinforcement LearningTraffic Signal ControlComprehensive Survey of Model Compression and Speed up for Vision Transformers
Vision Transformers (ViT) have marked a paradigm shift in computer vision, outperforming state-of-the-art models across diverse tasks. However, their practical deployment is hampered by high computational and memory dema…
Computational EfficiencyEdge-computingKnowledge DistillationModel Compression+1Arena: A Patch-of-Interest ViT Inference Acceleration System for Edge-Assisted Video Analytics
The advent of edge computing has made real-time intelligent video analytics feasible. Previous works, based on traditional model architecture (e.g., CNN, RNN, etc.), employ various strategies to filter out non-region-of-…
Edge-computingA Lightweight Spatiotemporal Network for Online Eye Tracking with Event Camera
Event-based data are commonly encountered in edge computing environments where efficiency and low latency are critical. To interface with such data and leverage their rich temporal features, we propose a causal spatiotem…
Edge-computingMitigating Challenges of the Space Environment for Onboard Artificial Intelligence: Design Overview of the Imaging Payload on SpIRIT
Artificial intelligence (AI) and autonomous edge computing in space are emerging areas of interest to augment capabilities of nanosatellites, where modern sensors generate orders of magnitude more data than can typically…
Edge-computingGPUImage CompressionAnti-Byzantine Attacks Enabled Vehicle Selection for Asynchronous Federated Learning in Vehicular Edge Computing
In vehicle edge computing (VEC), asynchronous federated learning (AFL) is used, where the edge receives a local model and updates the global model, effectively reducing the global aggregation latency.Due to different amo…
Deep Reinforcement LearningEdge-computingFederated LearningEasyACIM: An End-to-End Automated Analog CIM with Synthesizable Architecture and Agile Design Space Exploration
Analog Computing-in-Memory (ACIM) is an emerging architecture to perform efficient AI edge computing. However, current ACIM designs usually have unscalable topology and still heavily rely on manual efforts. These drawbac…
Edge-computingDavid and Goliath: An Empirical Evaluation of Attacks and Defenses for QNNs at the Deep Edge
ML is shifting from the cloud to the edge. Edge computing reduces the surface exposing private data and enables reliable throughput guarantees in real-time applications. Of the panoply of devices deployed at the edge, re…
Edge-computingQuantizationResistive Memory-based Neural Differential Equation Solver for Score-based Diffusion Model
Human brains image complicated scenes when reading a novel. Replicating this imagination is one of the ultimate goals of AI-Generated Content (AIGC). However, current AIGC methods, such as score-based diffusion, are stil…
Edge-computingDecision Transformers for Wireless Communications: A New Paradigm of Resource Management
As the next generation of mobile systems evolves, artificial intelligence (AI) is expected to deeply integrate with wireless communications for resource management in variable environments. In particular, deep reinforcem…
Deep Reinforcement LearningEdge-computingManagementStochastic OptimizationHawkDrive: A Transformer-driven Visual Perception System for Autonomous Driving in Night Scene
Many established vision perception systems for autonomous driving scenarios ignore the influence of light conditions, one of the key elements for driving safety. To address this problem, we present HawkDrive, a novel per…
Autonomous DrivingDepth EstimationEdge-computingSegmentation+1A Two Time-Scale Joint Optimization Approach for UAV-assisted MEC
Unmanned aerial vehicles (UAV)-assisted mobile edge computing (MEC) is emerging as a promising paradigm to provide aerial-terrestrial computing services close to mobile devices (MDs). However, meeting the demands of comp…
Edge-computingSDPose: Tokenized Pose Estimation via Circulation-Guide Self-Distillation
Recently, transformer-based methods have achieved state-of-the-art prediction quality on human pose estimation(HPE). Nonetheless, most of these top-performing transformer-based models are too computation-consuming and st…
Edge-computingPose EstimationDistributed Task Offloading and Resource Allocation for Latency Minimization in Mobile Edge Computing Networks
The growth in artificial intelligence (AI) technology has attracted substantial interests in latency-aware task offloading of mobile edge computing (MEC)-namely, minimizing service latency. Additionally, the use of MEC s…
Combinatorial OptimizationEdge-computingDNN Memory Footprint Reduction via Post-Training Intra-Layer Multi-Precision Quantization
The imperative to deploy Deep Neural Network (DNN) models on resource-constrained edge devices, spurred by privacy concerns, has become increasingly apparent. To facilitate the transition from cloud to edge computing, th…
Edge-computingQuantizationDriving Intelligent IoT Monitoring and Control through Cloud Computing and Machine Learning
This article explores how to drive intelligent iot monitoring and control through cloud computing and machine learning. As iot and the cloud continue to generate large and diverse amounts of data as sensor devices in the…
Cloud ComputingDistributed ComputingEdge-computingFault DetectionFOOL: Addressing the Downlink Bottleneck in Satellite Computing with Neural Feature Compression
Nanosatellite constellations equipped with sensors capturing large geographic regions provide unprecedented opportunities for Earth observation. As constellation sizes increase, network contention poses a downlink bottle…
Earth ObservationEdge-computingFeature CompressionImage Compression+2TJCCT: A Two-timescale Approach for UAV-assisted Mobile Edge Computing
Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) is emerging as a promising paradigm to provide aerial-terrestrial computing services in close proximity to mobile devices (MDs). However, meeting the dem…
Edge-computing