Papers Edge-computing
“Edge-computing” 태그가 달린 논문 1,279편 · 필터 해제
Efficient Deployment of Spiking Neural Networks on SpiNNaker2 for DVS Gesture Recognition Using Neuromorphic Intermediate Representation
Spiking Neural Networks (SNNs) are highly energy-efficient during inference, making them particularly suitable for deployment on neuromorphic hardware. Their ability to process event-driven inputs, such as data from dyna…
Edge-computingGesture RecognitionQuantizationQTCAJOSA: Low-Complexity Joint Offloading and Subchannel Allocation for NTN-Enabled IoMT
In this work, we consider the resource allocation problem for task offloading from Internet of Medical Things (IoMT) devices, to a non-terrestrial network. The architecture considers clusters of IoMT devices that offload…
Edge-computingEfficient Training of Large-Scale AI Models Through Federated Mixture-of-Experts: A System-Level Approach
The integration of Federated Learning (FL) and Mixture-of-Experts (MoE) presents a compelling pathway for training more powerful, large-scale artificial intelligence models (LAMs) on decentralized data while preserving p…
Edge-computingFederated LearningMixture-of-ExpertsECORE: Energy-Conscious Optimized Routing for Deep Learning Models at the Edge
Edge computing enables data processing closer to the source, significantly reducing latency an essential requirement for real-time vision-based analytics such as object detection in surveillance and smart city environmen…
Edge-computingObjectobject-detectionObject Detection+1QS4D: Quantization-aware training for efficient hardware deployment of structured state-space sequential models
Structured State Space models (SSM) have recently emerged as a new class of deep learning models, particularly well-suited for processing long sequences. Their constant memory footprint, in contrast to the linearly scali…
Computational EfficiencyEdge-computingQuantizationState Space ModelsHierarchical Task Offloading for UAV-Assisted Vehicular Edge Computing via Deep Reinforcement Learning
With the emergence of compute-intensive and delay-sensitive applications in vehicular networks, unmanned aerial vehicles (UAVs) have emerged as a promising complement for vehicular edge computing due to the high mobility…
Deep Reinforcement LearningEdge-computingSchedulingTrajectory PlanningMAIZX: A Carbon-Aware Framework for Optimizing Cloud Computing Emissions
Cloud computing drives innovation but also poses significant environmental challenges due to its high-energy consumption and carbon emissions. Data centers account for 2-4% of global energy usage, and the ICT sector's sh…
Cloud ComputingEdge-computingFederated Learning-Based Data Collaboration Method for Enhancing Edge Cloud AI System Security Using Large Language Models
With the widespread application of edge computing and cloud systems in AI-driven applications, how to maintain efficient performance while ensuring data privacy has become an urgent security issue. This paper proposes a …
Edge-computingFederated LearningModel PoisoningJoint Computation Offloading and Resource Allocation for Uncertain Maritime MEC via Cooperation of UAVs and Vessels
The computation demands from the maritime Internet of Things (MIoT) increase rapidly in recent years, and the unmanned aerial vehicles (UAVs) and vessels based multi-access edge computing (MEC) can fulfill these MIoT req…
Edge-computingOne-Shot Neural Architecture Search with Network Similarity Directed Initialization for Pathological Image Classification
Deep learning-based pathological image analysis presents unique challenges due to the practical constraints of network design. Most existing methods apply computer vision models directly to medical tasks, neglecting the …
Domain AdaptationEdge-computingimage-classificationImage Classification+1Dual‑detector Re‑optimization for Federated Weakly Supervised Video Anomaly Detection Via Adaptive Dynamic Recursive Mapping
Federated weakly supervised video anomaly detection represents a significant advancement in privacy-preserving collaborative learning, enabling distributed clients to train anomaly detectors using only video-level annota…
Anomaly DetectionAnomaly Detection In Surveillance VideosEdge-computingFederated Learning+5Multi-dimensional Autoscaling of Processing Services: A Comparison of Agent-based Methods
Edge computing breaks with traditional autoscaling due to strict resource constraints, thus, motivating more flexible scaling behaviors using multiple elasticity dimensions. This work introduces an agent-based autoscalin…
Edge-computingSLED: A Speculative LLM Decoding Framework for Efficient Edge Serving
Regardless of the advancements in device capabilities, efficient inferencing advanced large language models (LLMs) at the edge remains challenging due to limited device memory and power constraints. Existing strategies, …
Edge-computingQuantizationHASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems
Split federated learning (SFL) has emerged as a promising paradigm to democratize machine learning (ML) on edge devices by enabling layer-wise model partitioning. However, existing SFL approaches suffer significantly fro…
Edge-computingFederated LearningAgile Reinforcement Learning for Real-Time Task Scheduling in Edge Computing
Soft real-time applications are becoming increasingly complex, posing significant challenges for scheduling offloaded tasks in edge computing environments while meeting task timing constraints. Moreover, the exponential …
Edge-computingreinforcement-learningReinforcement LearningSchedulingOptimal Task Offloading with Firm Deadlines for Mobile Edge Computing Systems
Under a dramatic increase in mobile data traffic, a promising solution for edge computing systems to maintain their local service is the task migration that may be implemented by means of Autonomous mobile agents (AMA). …
Edge-computingFederated Learning on Stochastic Neural Networks
Federated learning is a machine learning paradigm that leverages edge computing on client devices to optimize models while maintaining user privacy by ensuring that local data remains on the device. However, since all da…
Edge-computingFederated LearningComputation Capacity Maximization for Pinching Antennas-Assisted Wireless Powered MEC Systems
In this paper,we investigate a novel wireless powered mobile edge computing (MEC) system assisted by pinching antennas (PAs), where devices first harvest energy from a base station and then offload computation-intensive …
Edge-computingDesign and Implementation of a RISC-V SoC with Custom DSP Accelerators for Edge Computing
This paper presents a comprehensive analysis of the RISC-V instruction set architecture, focusing on its modular design, implementation challenges, and performance characteristics. We examine the RV32I base instruction s…
Edge-computingEdge-Enabled Collaborative Object Detection for Real-Time Multi-Vehicle Perception
Accurate and reliable object detection is critical for ensuring the safety and efficiency of Connected Autonomous Vehicles (CAVs). Traditional on-board perception systems have limited accuracy due to occlusions and blind…
Autonomous DrivingAutonomous VehiclesEdge-computingObject+2