Edge-computing
224 papers with code • 0 benchmarks • 0 datasets
Deep Learning on EDGE devices
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Libraries
Use these libraries to find Edge-computing models and implementations
Most implemented papers
TransVisDrone: Spatio-Temporal Transformer for Vision-based Drone-to-Drone Detection in Aerial Videos
tusharsangam/transvisdrone
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Drone-to-drone detection using visual feed has crucial applications, such as detecting drone collisions, detecting drone attacks, or coordinating flight with other drones.
Grounding DINO 1.5: Advance the "Edge" of Open-Set Object Detection
Empirical results demonstrate the effectiveness of Grounding DINO 1. 5, with the Grounding DINO 1. 5 Pro model attaining a 54. 3 AP on the COCO detection benchmark and a 55. 7 AP on the LVIS-minival zero-shot transfer benchmark, setting new records for open-set object detection.
Residual-INR: Communication Efficient On-Device Learning Using Implicit Neural Representation
However, as the scale of the edge computing system is getting larger, communication among devices is becoming the bottleneck because of the limited bandwidth of wireless communication leads to large data transfer latency.
Decentralized Computation Offloading for Multi-User Mobile Edge Computing: A Deep Reinforcement Learning Approach
swordest/mec_drl
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Numerical results are illustrated to demonstrate that efficient policies can be learned at each user, and performance of the proposed DDPG based decentralized strategy outperforms the conventional deep Q-network (DQN) based discrete power control strategy and some other greedy strategies with reduced computation cost.
On the Convergence of FedAvg on Non-IID Data
lx10077/fedavgpy
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ICLR 2020
In this paper, we analyze the convergence of \texttt{FedAvg} on non-iid data and establish a convergence rate of $\mathcal{O}(\frac{1}{T})$ for strongly convex and smooth problems, where $T$ is the number of SGDs.
Distilled Split Deep Neural Networks for Edge-Assisted Real-Time Systems
yoshitomo-matsubara/head-network-distillation
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Offloading the execution of complex Deep Neural Networks (DNNs) models to compute-capable devices at the network edge, that is, edge servers, can significantly reduce capture-to-output delay.
Scientific Image Restoration Anywhere
We explore this question by evaluating the performance and accuracy of a scientific image restoration model, for which both model input and output are images, on edge computing devices.
Graph Markov Network for Traffic Forecasting with Missing Data
zhiyongc/Graph-Markov-Network
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Although missing values can be imputed, existing data imputation methods normally need long-term historical traffic state data.
Split Computing for Complex Object Detectors: Challenges and Preliminary Results
yoshitomo-matsubara/hnd-ghnd-object-detectors
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Following the trends of mobile and edge computing for DNN models, an intermediate option, split computing, has been attracting attentions from the research community.
Neural Compression and Filtering for Edge-assisted Real-time Object Detection in Challenged Networks
yoshitomo-matsubara/hnd-ghnd-object-detectors
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However, poor conditions of the wireless channel connecting the mobile devices to the edge servers may degrade the overall capture-to-output delay achieved by edge offloading.
