Edge-computing
156 papers with code • 0 benchmarks • 0 datasets
Deep Learning on EDGE devices
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Latest papers
A Lightweight Spatiotemporal Network for Online Eye Tracking with Event Camera
PeaBrane/eye_track_spatiotemporal
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Event-based data are commonly encountered in edge computing environments where efficiency and low latency are critical.
David and Goliath: An Empirical Evaluation of Attacks and Defenses for QNNs at the Deep Edge
To fill this gap, we empirically evaluate the effectiveness of attacks and defenses from (full-precision) ANNs on (constrained) QNNs.
Resistive Memory-based Neural Differential Equation Solver for Score-based Diffusion Model
Demonstrating equivalent generative quality to the software baseline, our system achieved remarkable enhancements in generative speed for both unconditional and conditional generation tasks, by factors of 64. 8 and 156. 5, respectively.
SDPose: Tokenized Pose Estimation via Circulation-Guide Self-Distillation
To mitigate the problem of under-fitting, we design a transformer module named Multi-Cycled Transformer(MCT) based on multiple-cycled forwards to more fully exploit the potential of small model parameters.
FOOL: Addressing the Downlink Bottleneck in Satellite Computing with Neural Feature Compression
Further, it embeds context and leverages inter-tile dependencies to lower transfer costs with negligible overhead.
NeuFlow: Real-time, High-accuracy Optical Flow Estimation on Robots Using Edge Devices
Given the features of the input images extracted at different spatial resolutions, global matching is employed to estimate an initial optical flow on the 1/16 resolution, capturing large displacement, which is then refined on the 1/8 resolution with lightweight CNN layers for better accuracy.
Real-Time Multimodal Cognitive Assistant for Emergency Medical Services
Emergency Medical Services (EMS) responders often operate under time-sensitive conditions, facing cognitive overload and inherent risks, requiring essential skills in critical thinking and rapid decision-making.
Combinatorial Client-Master Multiagent Deep Reinforcement Learning for Task Offloading in Mobile Edge Computing
Task offloading in MEC is a strategy that meets the demands of UDs by distributing tasks between UDs and MEC servers.
FedLPS: Heterogeneous Federated Learning for Multiple Tasks with Local Parameter Sharing
jyzgh/fedlps
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FedLPS leverages principles from transfer learning to facilitate the deployment of multiple tasks on a single device by dividing the local model into a shareable encoder and task-specific encoders.
Edge-Computing-Enabled Deep Learning Approach for Low-Light Satellite Image Enhancement
Edge computing enables rapid data processing and decision-making on satellite payloads.
