Papers Edge-computing
“Edge-computing” 태그가 달린 논문 1,279편 · 필터 해제
dRG-MEC: Decentralized Reinforced Green Offloading for MEC-enabled Cloud Network
Multi-access-Mobile Edge Computing (MEC) is a promising solution for computationally demanding rigorous applications, that can meet 6G network service requirements. However, edge servers incur high computation costs duri…
Edge-computingWidthFormer: Toward Efficient Transformer-based BEV View Transformation
We present WidthFormer, a novel transformer-based module to compute Bird's-Eye-View (BEV) representations from multi-view cameras for real-time autonomous-driving applications. WidthFormer is computationally efficient, r…
3D Object DetectionAutonomous DrivingDecoderEdge-computing+4Dynamic and Memory-efficient Shape Based Methodologies for User Type Identification in Smart Grid Applications
Detecting behind-the-meter (BTM) equipment and major appliances at the residential level and tracking their changes in real time is important for aggregators and traditional electricity utilities. In our previous work, w…
Dimensionality ReductionEdge-computingA Cost-Efficient FPGA Implementation of Tiny Transformer Model using Neural ODE
Transformer has been adopted to image recognition tasks and shown to outperform CNNs and RNNs while it suffers from high training cost and computational complexity. To address these issues, a hybrid approach has become a…
CPUEdge-computingQuantizationImage-based Deep Learning for Smart Digital Twins: a Review
Smart Digital twins (SDTs) are being increasingly used to virtually replicate and predict the behaviors of complex physical systems through continual data assimilation enabling the optimization of the performance of thes…
Anomaly DetectionData AugmentationDeep LearningEdge-computingEdge Computing based Human-Robot Cognitive Fusion: A Medical Case Study in the Autism Spectrum Disorder Therapy
In recent years, edge computing has served as a paradigm that enables many future technologies like AI, Robotics, IoT, and high-speed wireless sensor networks (like 5G) by connecting cloud computing facilities and servic…
Cloud ComputingEdge-computingMeta Reinforcement Learning for Multi-Task Offloading in Vehicular Edge Computing
Mobile edge computing has been a promising solution to enable real-time service in vehicular networks. However, due to high dynamics of mobile environment and heterogeneous features of vehicular services, traditional exp…
Edge-computingMeta-LearningMeta Reinforcement Learningreinforcement-learning+1Device-Wise Federated Network Pruning
Neural network pruning particularly channel pruning is a widely used technique for compressing deep learning models to enable their deployment on edge devices with limited resources. Typically redundant weights or st…
Edge-computingFederated LearningNetwork PruningBlock Pruning for Enhanced Efficiency in Convolutional Neural Networks
This paper presents a novel approach to network pruning, targeting block pruning in deep neural networks for edge computing environments. Our method diverges from traditional techniques that utilize proxy metrics, instea…
Edge-computingNetwork PruningHigh Efficiency Inference Accelerating Algorithm for NOMA-based Mobile Edge Computing
Splitting the inference model between device, edge server, and cloud can improve the performance of EI greatly. Additionally, the non-orthogonal multiple access (NOMA), which is the key supporting technologies of B5G/6G,…
Edge-computingA Bayesian Framework of Deep Reinforcement Learning for Joint O-RAN/MEC Orchestration
Multi-access Edge Computing (MEC) can be implemented together with Open Radio Access Network (O-RAN) over commodity platforms to offer low-cost deployment and bring the services closer to end-users. In this paper, a join…
Deep Reinforcement LearningEdge-computingEfficient ExplorationReinforcement Learning (RL)FedMS: Federated Learning with Mixture of Sparsely Activated Foundations Models
Foundation models have shown great success in natural language processing, computer vision, and multimodal tasks. FMs have a large number of model parameters, thus requiring a substantial amount of data to help optimize …
Edge-computingFederated LearningCombining SNNs with Filtering for Efficient Neural Decoding in Implantable Brain-Machine Interfaces
While it is important to make implantable brain-machine interfaces (iBMI) wireless to increase patient comfort and safety, the trend of increased channel count in recent neural probes poses a challenge due to the concomi…
BenchmarkingDecoderEdge-computingHuman-Centric Resource Allocation for the Metaverse With Multiaccess Edge Computing
Multi-access edge computing (MEC) is a promising solution to the computation-intensive, low-latency rendering tasks of the metaverse. However, how to optimally allocate limited communication and computation resources at …
Edge-computingZodiacEdge: a Datalog Engine With Incremental Rule Set Maintenance
In this paper, we tackle the incremental maintenance of Datalog inference materialisation when the rule set can be updated. This is particularly relevant in the context of the Internet of Things and Edge computing where …
Edge-computingNegationAn effective and efficient green federated learning method for one-layer neural networks
Nowadays, machine learning algorithms continue to grow in complexity and require a substantial amount of computational resources and energy. For these reasons, there is a growing awareness of the development of new green…
Edge-computingFederated LearningControl Aspects for Using RIS in Latency-Constrained Mobile Edge Computing
This paper investigates the role and the impact of control operations for dynamic mobile edge computing (MEC) empowered by Reconfigurable Intelligent Surfaces (RISs), in which multiple devices offload their computation t…
Edge-computingMISA: Unveiling the Vulnerabilities in Split Federated Learning
\textit{Federated learning} (FL) and \textit{split learning} (SL) are prevailing distributed paradigms in recent years. They both enable shared global model training while keeping data localized on users' devices. The fo…
Edge-computingFederated LearningFractional Deep Reinforcement Learning for Age-Minimal Mobile Edge Computing
Mobile edge computing (MEC) is a promising paradigm for real-time applications with intensive computational needs (e.g., autonomous driving), as it can reduce the processing delay. In this work, we focus on the timelines…
Autonomous DrivingDeep Reinforcement LearningEdge-computingreinforcement-learning+3Conformer-Based Speech Recognition On Extreme Edge-Computing Devices
With increasingly more powerful compute capabilities and resources in today's devices, traditionally compute-intensive automatic speech recognition (ASR) has been moving from the cloud to devices to better protect user p…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Edge-computingspeech-recognition+1