Edge-computing
89 papers with code • 0 benchmarks • 0 datasets
Deep Learning on EDGE devices
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Most implemented papers
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.
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.
Head Network Distillation: Splitting Distilled Deep Neural Networks for Resource-Constrained Edge Computing Systems
yoshitomo-matsubara/head-network-distillation
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In this paper, we propose to modify the structure and training process of DNN models for complex image classification tasks to achieve in-network compression in the early network layers.
Systolic-CNN: An OpenCL-defined Scalable Run-time-flexible FPGA Accelerator Architecture for Accelerating Convolutional Neural Network Inference in Cloud/Edge Computing
Systolic-CNN is also run-time-flexible in the context of multi-tenancy cloud/edge computing, which can be time-shared to accelerate a variety of CNN models at run time without the need of recompiling the FPGA kernel hardware nor reprogramming the FPGA.
DONE: Distributed Approximate Newton-type Method for Federated Edge Learning
dual-grp/DONE
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In this work, we propose DONE, a distributed approximate Newton-type algorithm with fast convergence rate for communication-efficient federated edge learning.
