Edge-computing

224 papers with code • 0 benchmarks • 0 datasets

Deep Learning on EDGE devices

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 16 Oct 2022

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

idea-research/grounding-dino-1.5-api 16 May 2024

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

sharc-lab/residual-inr 10 Aug 2024

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 16 Dec 2018

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 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 1 Oct 2019

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

lzhengchun/TomoGAN 12 Nov 2019

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 10 Dec 2019

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 27 Jul 2020

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 31 Jul 2020

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.