Papers Edge-computing
“Edge-computing” 태그가 달린 논문 1,279편 · 필터 해제
tuGEMM: Area-Power-Efficient Temporal Unary GEMM Architecture for Low-Precision Edge AI
General matrix multiplication (GEMM) is a ubiquitous computing kernel/algorithm for data processing in diverse applications, including artificial intelligence (AI) and deep learning (DL). Recent shift towards edge comput…
Edge-computingLearning for Cross-Layer Resource Allocation in MEC-Aided Cell-Free Networks
Cross-layer resource allocation over mobile edge computing (MEC)-aided cell-free networks can sufficiently exploit the transmitting and computing resources to promote the data rate. However, the technical bottlenecks of …
Edge-computingSelf-Supervised LearningTransfer LearningA Tutorial on Non-Terrestrial Networks: Towards Global and Ubiquitous 6G Connectivity
The International Mobile Telecommunications (IMT)-2030 framework recently adopted by the International Telecommunication Union Radiocommunication Sector (ITU-R) envisions 6G networks to deliver intelligent, seamless conn…
Cloud ComputingDeep Reinforcement LearningEdge-computingTheory of Mixture-of-Experts for Mobile Edge Computing
In mobile edge computing (MEC) networks, mobile users generate diverse machine learning tasks dynamically over time. These tasks are typically offloaded to the nearest available edge server, by considering communication …
Computational EfficiencyContinual LearningEdge-computingMixture-of-ExpertsThreshold Neuron: A Brain-inspired Artificial Neuron for Efficient On-device Inference
Enhancing the computational efficiency of on-device Deep Neural Networks (DNNs) remains a significant challengein mobile and edge computing. As we aim to execute increasingly complex tasks with constrained computational …
Computational EfficiencyEdge-computingLaMI-GO: Latent Mixture Integration for Goal-Oriented Communications Achieving High Spectrum Efficiency
The recent rise of semantic-style communications includes the development of goal-oriented communications (GOCOMs) remarkably efficient multimedia information transmissions. The concept of GO-COMS leverages advanced arti…
Edge-computingGenerative Adversarial NetworkEfficient Speech Command Recognition Leveraging Spiking Neural Network and Curriculum Learning-based Knowledge Distillation
The intrinsic dynamics and event-driven nature of spiking neural networks (SNNs) make them excel in processing temporal information by naturally utilizing embedded time sequences as time steps. Recent studies adopting th…
Edge-computingKnowledge DistillationRepresentation LearningLatency Minimization for Movable Antennas-Enabled Relay-aided D2D Mobile Edge Computing Communication Systems
Device-to-device (D2D)-assisted mobile edge computing (MEC) is one of the critical technologies of future sixth generation (6G) networks. The core of D2D-assisted MEC is to reduce system latency for network edge UEs by s…
Cloud ComputingEdge-computingEndangered Alert: A Field-Validated Self-Training Scheme for Detecting and Protecting Threatened Wildlife on Roads and Roadsides
Traffic accidents are a global safety concern, resulting in numerous fatalities each year. A considerable number of these deaths are caused by animal-vehicle collisions (AVCs), which not only endanger human lives but als…
Edge-computingLanguage ModelingLanguage Modellingobject-detection+1AgroXAI: Explainable AI-Driven Crop Recommendation System for Agriculture 4.0
Today, crop diversification in agriculture is a critical issue to meet the increasing demand for food and improve food safety and quality. This issue is considered to be the most important challenge for the next generati…
counterfactualDiversityEdge-computingExplainable artificial intelligence+1AI-Driven Health Monitoring of Distributed Computing Architecture: Insights from XGBoost and SHAP
With the rapid development of artificial intelligence technology, its application in the optimization of complex computer systems is becoming more and more extensive. Edge computing is an efficient distributed computing …
Anomaly DetectionDistributed ComputingEdge-computingSchedulingDeployment Pipeline from Rockpool to Xylo for Edge Computing
Deploying Spiking Neural Networks (SNNs) on the Xylo neuromorphic chip via the Rockpool framework represents a significant advancement in achieving ultra-low-power consumption and high computational efficiency for edge a…
Computational EfficiencyEdge-computingEdge AI-based Radio Frequency Fingerprinting for IoT Networks
The deployment of the Internet of Things (IoT) in smart cities and critical infrastructure has enhanced connectivity and real-time data exchange but introduced significant security challenges. While effective, cryptograp…
Edge-computingHybrid variable spiking graph neural networks for energy-efficient scientific machine learning
Graph-based representations for samples of computational mechanics-related datasets can prove instrumental when dealing with problems like irregular domains or molecular structures of materials, etc. To effectively analy…
Edge-computingregressionDistribution free uncertainty quantification in neuroscience-inspired deep operators
Energy-efficient deep learning algorithms are essential for a sustainable future and feasible edge computing setups. Spiking neural networks (SNNs), inspired from neuroscience, are a positive step in the direction of ach…
Conformal PredictionEdge-computingSuper-ResolutionUncertainty QuantificationLearnable Sparse Customization in Heterogeneous Edge Computing
To effectively manage and utilize massive distributed data at the network edge, Federated Learning (FL) has emerged as a promising edge computing paradigm across data silos. However, FL still faces two challenges: system…
Edge-computingFederated LearningCloud Platforms for Developing Generative AI Solutions: A Scoping Review of Tools and Services
Generative AI is transforming enterprise application development by enabling machines to create content, code, and designs. These models, however, demand substantial computational power and data management. Cloud computi…
Cloud ComputingEdge-computingManagementDapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices
Federated learning (FL) has emerged as a prominent machine learning paradigm in edge computing environments, enabling edge devices to collaboratively optimize a global model without sharing their private data. However, e…
Edge-computingFederated LearningHierarchical Learning for IRS-Assisted MEC Systems with Rate-Splitting Multiple Access
Intelligent reflecting surface (IRS)-assisted mobile edge computing (MEC) systems have shown notable improvements in efficiency, such as reduced latency, higher data rates, and better energy efficiency. However, the reso…
Deep Reinforcement LearningEdge-computingDeep Learning, Machine Learning, Advancing Big Data Analytics and Management
Advancements in artificial intelligence, machine learning, and deep learning have catalyzed the transformation of big data analytics and management into pivotal domains for research and application. This work explores th…
Anomaly DetectionDeep LearningDimensionality ReductionDistributed Computing+4