Edge-computing
163 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
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.
Bridging Data Center AI Systems with Edge Computing for Actionable Information Retrieval
Extremely high data rates at modern synchrotron and X-ray free-electron laser light source beamlines motivate the use of machine learning methods for data reduction, feature detection, and other purposes.
Supervised Compression for Resource-Constrained Edge Computing Systems
yoshitomo-matsubara/supervised-compression
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There has been much interest in deploying deep learning algorithms on low-powered devices, including smartphones, drones, and medical sensors.
HRNET: AI on Edge for mask detection and social distancing
code-implementation1/Code4
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The framework further equips government agency, system providers to design and constructs technology-oriented models in community setup to Increase the quality of life using emerging technologies into smart urban environments.
Hardware-Efficient Deconvolution-Based GAN for Edge Computing
Azzam-Alhussain/QDCGAN-PYNQ
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Generative Adversarial Networks (GAN) are cutting-edge algorithms for generating new data samples based on the learned data distribution.
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.
David and Goliath: An Empirical Evaluation of Attacks and Defenses for QNNs at the Deep Edge
esrgv3/qnn-adversarial-robustness
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To fill this gap, we empirically evaluate the effectiveness of attacks and defenses from (full-precision) ANNs on (constrained) QNNs.
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.
A Quantization-Friendly Separable Convolution for MobileNets
As deep learning (DL) is being rapidly pushed to edge computing, researchers invented various ways to make inference computation more efficient on mobile/IoT devices, such as network pruning, parameter compression, and etc.
B-DCGAN:Evaluation of Binarized DCGAN for FPGA
We are trying to implement deep neural networks in the edge computing environment for real-world applications such as the IoT(Internet of Things), the FinTech etc., for the purpose of utilizing the significant achievement of Deep Learning in recent years.
