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Found 11 papers, 0 papers with code
Implementation of Big AI Models for Wireless Networks with Collaborative Edge Computing
Motivated by this, in this article, we propose collaborative edge training, a novel training mechanism that orchestrates a group of trusted edge devices as a resource pool for expedited, sustainable big AI model training at the edge.
A3D: Adaptive, Accurate, and Autonomous Navigation for Edge-Assisted Drones
Accurate navigation is of paramount importance to ensure flight safety and efficiency for autonomous drones.
Serving Graph Neural Networks With Distributed Fog Servers For Smart IoT Services
Graph Neural Networks (GNNs) have gained growing interest in miscellaneous applications owing to their outstanding ability in extracting latent representation on graph structures.
Real-Time High-Resolution Pedestrian Detection in Crowded Scenes via Parallel Edge Offloading
To identify dense and small-size pedestrians in surveillance systems, high-resolution cameras are widely deployed, where high-resolution images are captured and delivered to off-the-shelf pedestrian detection models.
GNN at the Edge: Cost-Efficient Graph Neural Network Processing over Distributed Edge Servers
Edge intelligence has arisen as a promising computing paradigm for supporting miscellaneous smart applications that rely on machine learning techniques.
Edge Robotics: Edge-Computing-Accelerated Multi-Robot Simultaneous Localization and Mapping
With the wide penetration of smart robots in multifarious fields, Simultaneous Localization and Mapping (SLAM) technique in robotics has attracted growing attention in the community.
CoEdge: Cooperative DNN Inference with Adaptive Workload Partitioning over Heterogeneous Edge Devices
CoEdge utilizes available computation and communication resources at the edge and dynamically partitions the DNN inference workload adaptive to devices' computing capabilities and network conditions.
Joint Multi-User DNN Partitioning and Computational Resource Allocation for Collaborative Edge Intelligence
With the assistance of edge servers, user equipments (UEs) are able to run deep neural network (DNN) based AI applications, which are generally resource-hungry and compute-intensive, such that an individual UE can hardly afford by itself in real time.
Knowledge Distillation for Mobile Edge Computation Offloading
In this article, we propose an edge computation offloading framework based on Deep Imitation Learning (DIL) and Knowledge Distillation (KD), which assists end devices to quickly make fine-grained decisions to optimize the delay of computation tasks online.
Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computing
As a key technology of enabling Artificial Intelligence (AI) applications in 5G era, Deep Neural Networks (DNNs) have quickly attracted widespread attention.
Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing
To this end, we conduct a comprehensive survey of the recent research efforts on edge intelligence.
