Papers Edge-computing
“Edge-computing” 태그가 달린 논문 1,279편 · 필터 해제
A Real-Time Active Speaker Detection System Integrating an Audio-Visual Signal with a Spatial Querying Mechanism
We introduce a distinctive real-time, causal, neural network-based active speaker detection system optimized for low-power edge computing. This system drives a virtual cinematography module and is deployed on a commercia…
Active Speaker DetectionEdge-computingDNNShifter: An Efficient DNN Pruning System for Edge Computing
Deep neural networks (DNNs) underpin many machine learning applications. Production quality DNN models achieve high inference accuracy by training millions of DNN parameters which has a significant resource footprint. Th…
Edge-computingThe online learning architecture with edge computing for high-level control for assisting patients
The prevalence of mobility impairments due to conditions such as spinal cord injuries, strokes, and degenerative diseases is on the rise globally. Lower-limb exoskeletons have been increasingly recognized as a viable sol…
Edge-computingEnabling Intelligent Vehicular Networks Through Distributed Learning in the Non-Terrestrial Networks 6G Vision
The forthcoming 6G-enabled Intelligent Transportation System (ITS) is set to redefine conventional transportation networks with advanced intelligent services and applications. These technologies, including edge computing…
Edge-computingFederated LearningTransfer LearningSymED: Adaptive and Online Symbolic Representation of Data on the Edge
The edge computing paradigm helps handle the Internet of Things (IoT) generated data in proximity to its source. Challenges occur in transferring, storing, and processing this rapidly growing amount of data on resource-c…
Anomaly DetectionData CompressionDynamic Time WarpingEdge-computingDynamic Encoding and Decoding of Information for Split Learning in Mobile-Edge Computing: Leveraging Information Bottleneck Theory
Split learning is a privacy-preserving distributed learning paradigm in which an ML model (e.g., a neural network) is split into two parts (i.e., an encoder and a decoder). The encoder shares so-called latent representat…
DecoderEdge-computingInformativenessPrivacy PreservingCache-assisted Mobile Edge Computing over Space-Air-Ground Integrated Networks for Extended Reality Applications
Extended reality-enabled Internet of Things (XRI) provides the new user experience and the sense of immersion by adding virtual elements to the real world through Internet of Things (IoT) devices and emerging 6G technolo…
Edge-computingPersonalized Federated Deep Reinforcement Learning-based Trajectory Optimization for Multi-UAV Assisted Edge Computing
In the era of 5G mobile communication, there has been a significant surge in research focused on unmanned aerial vehicles (UAVs) and mobile edge computing technology. UAVs can serve as intelligent servers in edge computi…
Deep Reinforcement LearningEdge-computingFederated Learningreinforcement-learning+1A Multi-Head Ensemble Multi-Task Learning Approach for Dynamical Computation Offloading
Computation offloading has become a popular solution to support computationally intensive and latency-sensitive applications by transferring computing tasks to mobile edge servers (MESs) for execution, which is known as …
Edge-computingMulti-Task LearningSPAIC: A sub-$μ$W/Channel, 16-Channel General-Purpose Event-Based Analog Front-End with Dual-Mode Encoders
Low-power event-based analog front-ends (AFE) are a crucial component required to build efficient end-to-end neuromorphic processing systems for edge computing. Although several neuromorphic chips have been developed for…
Edge-computingLAMBO: Large AI Model Empowered Edge Intelligence
Next-generation edge intelligence is anticipated to benefit various applications via offloading techniques. However, traditional offloading architectures face several issues, including heterogeneous constraints, partial …
Active LearningDecision MakingDecoderEdge-computing+3Computation-efficient Deep Learning for Computer Vision: A Survey
Over the past decade, deep learning models have exhibited considerable advancements, reaching or even exceeding human-level performance in a range of visual perception tasks. This remarkable progress has sparked interest…
Autonomous VehiclesDeep LearningEdge-computingModel Compression+1MST-compression: Compressing and Accelerating Binary Neural Networks with Minimum Spanning Tree
Binary neural networks (BNNs) have been widely adopted to reduce the computational cost and memory storage on edge-computing devices by using one-bit representation for activations and weights. However, as neural network…
Edge-computingRobust Computation Offloading and Trajectory Optimization for Multi-UAV-Assisted MEC: A Multi-Agent DRL Approach
For multiple Unmanned-Aerial-Vehicles (UAVs) assisted Mobile Edge Computing (MEC) networks, we study the problem of combined computation and communication for user equipments deployed with multi-type tasks. Specifically,…
Deep Reinforcement LearningEdge-computingRobust DesignMobility-Aware Computation Offloading for Swarm Robotics using Deep Reinforcement Learning
Swarm robotics is envisioned to automate a large number of dirty, dangerous, and dull tasks. Robots have limited energy, computation capability, and communication resources. Therefore, current swarm robotics have a small…
Deep Reinforcement LearningEdge-computingreinforcement-learningSchedulingMalware Classification using Deep Neural Networks: Performance Evaluation and Applications in Edge Devices
With the increasing extent of malware attacks in the present day along with the difficulty in detecting modern malware, it is necessary to evaluate the effectiveness and performance of Deep Neural Networks (DNNs) for mal…
Edge-computingMalware ClassificationMalware DetectionIntelligent Reflecting Surface Aided Multi-Tier Hybrid Computing
The digital twin edge network (DITEN) aims to integrate mobile edge computing (MEC) and digital twin (DT) to provide real-time system configuration and flexible resource allocation for the sixth-generation network. This …
Edge-computingLearning Lightweight Object Detectors via Multi-Teacher Progressive Distillation
Resource-constrained perception systems such as edge computing and vision-for-robotics require vision models to be both accurate and lightweight in computation and memory usage. While knowledge distillation is a proven s…
Edge-computingInstance SegmentationKnowledge DistillationObject+3Joint Task Offloading and Resource Allocation in Aerial-Terrestrial UAV Networks with Edge and Fog Computing for Post-Disaster Rescue
Unmanned aerial vehicles (UAVs) play an increasingly important role in assisting fast-response post-disaster rescue due to their fast deployment, flexible mobility, and low cost. However, UAVs face the challenges of limi…
Edge-computingHyperSNN: A new efficient and robust deep learning model for resource constrained control applications
In light of the increasing adoption of edge computing in areas such as intelligent furniture, robotics, and smart homes, this paper introduces HyperSNN, an innovative method for control tasks that uses spiking neural net…
AcrobotEdge-computingModel Predictive Control