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Edge-computing

Deep Learning on EDGE devices

Papers

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Showing 976–1000 of 1279 papers

TitleStatusHype
Tera-SpaceCom: GNN-based Deep Reinforcement Learning for Joint Resource Allocation and Task Offloading in TeraHertz Band Space Networks—0
The Benefit of the Doubt: Uncertainty Aware Sensing for Edge Computing Platforms—0
The Fusion of Deep Reinforcement Learning and Edge Computing for Real-time Monitoring and Control Optimization in IoT Environments—0
The M-factor: A Novel Metric for Evaluating Neural Architecture Search in Resource-Constrained Environments—0
The online learning architecture with edge computing for high-level control for assisting patients—0
Theory of Mixture-of-Experts for Mobile Edge Computing—0
The Role of LLMs in Sustainable Smart Cities: Applications, Challenges, and Future Directions—0
The Shift to 6G Communications: Vision and Requirements—0
The Streetscape Application Services Stack (SASS): Towards a Distributed Sensing Architecture for Urban Applications—0
The Why, What and How of Artificial General Intelligence Chip Development—0
THOR -- A Neuromorphic Processor with 7.29G TSOP^2/mm^2Js Energy-Throughput Efficiency—0
Threshold Neuron: A Brain-inspired Artificial Neuron for Efficient On-device Inference—0
Time-sensitive Learning for Heterogeneous Federated Edge Intelligence—0
Tiny Classifier Circuits: Evolving Accelerators for Tabular Data—0
TinyML Security: Exploring Vulnerabilities in Resource-Constrained Machine Learning Systems—0
TinyML: Tools, Applications, Challenges, and Future Research Directions—0
TJCCT: A Two-timescale Approach for UAV-assisted Mobile Edge Computing—0
Topology-aware Federated Learning in Edge Computing: A Comprehensive Survey—0
To Risk or Not to Risk: Learning with Risk Quantification for IoT Task Offloading in UAVs—0
To Talk or to Work: Delay Efficient Federated Learning over Mobile Edge Devices—0
Energy Efficient Federated Learning over Heterogeneous Mobile Devices via Joint Design of Weight Quantization and Wireless Transmission—0
To Train or Not to Train: Balancing Efficiency and Training Cost in Deep Reinforcement Learning for Mobile Edge Computing—0
Toward Multi-Service Edge-Intelligence Paradigm: Temporal-Adaptive Prediction for Time-Critical Control over Wireless—0
Toward Runtime-Throttleable Neural Networks—0
Towards AI-Native Software Engineering (SE 3.0): A Vision and a Challenge Roadmap—0
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