57 papers with code • 1 benchmarks • 1 datasets

Deep Learning on EDGE devices


Latest papers with code

SECDA: Efficient Hardware/Software Co-Design of FPGA-based DNN Accelerators for Edge Inference

giclab/secda 1 Oct 2021

In this paper we propose SECDA, a new hardware/software co-design methodology to reduce design time of optimized DNN inference accelerators on edge devices with FPGAs.


01 Oct 2021

Supervised Compression for Resource-Constrained Edge Computing Systems

yoshitomo-matsubara/supervised-compression 21 Aug 2021

There has been much interest in deploying deep learning algorithms on low-powered devices, including smartphones, drones, and medical sensors.

Data Compression Edge-computing +2

21 Aug 2021

Tiny Machine Learning for Concept Drift

simdis/Adaptive-TML 30 Jul 2021

For the first time in the literature, this paper introduces a Tiny Machine Learning for Concept Drift (TML-CD) solution based on deep learning feature extractors and a k-nearest neighbors classifier integrating a hybrid adaptation module able to deal with concept drift affecting the data-generating process.


30 Jul 2021

An Energy-Efficient Edge Computing Paradigm for Convolution-based Image Upsampling

icolbert/upsampling 15 Jul 2021

We analyze and compare the inference properties of convolution-based upsampling algorithms using a quantitative model of incurred time and energy costs and show that using deconvolution for inference at the edge improves both system latency and energy efficiency when compared to their sub-pixel or resize convolution counterparts.


15 Jul 2021

CSL-YOLO: A New Lightweight Object Detection System for Edge Computing

D0352276/CSL-YOLO 10 Jul 2021

To reduce the computation cost, how to generate redundant features plays a significant role.

Edge-computing Object Detection

10 Jul 2021

Bridge Data Center AI Systems with Edge Computing for Actionable Information Retrieval

AISDC/CookieNetAE 28 May 2021

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.

Edge-computing Information Retrieval

28 May 2021

Networked Federated Multi-Task Learning

sahelyiyi/FederatedLearning 26 May 2021

These local datasets are often related via an intrinsic network structure that arises from domain-specific notions of similarity between local datasets.

Distributed Computing Edge-computing +2

26 May 2021

Spectral Pruning for Recurrent Neural Networks

zalandoresearch/fashion-mnist 23 May 2021

Recurrent neural networks (RNNs) are a class of neural networks used in sequential tasks.


23 May 2021

A Reinforcement Learning Environment for Multi-Service UAV-enabled Wireless Systems

DamianoBrunori/MultiUAV-OpenAIGym 11 May 2021

We design a multi-purpose environment for autonomous UAVs offering different communication services in a variety of application contexts (e. g., wireless mobile connectivity services, edge computing, data gathering).

Edge-computing OpenAI Gym

11 May 2021

ScissionLite: Accelerating Distributed Deep Neural Networks Using Transfer Layer

qub-blesson/ScissionTL 5 May 2021

For example, applications underpinned by deep neural networks (DNN) models can be sliced and distributed across the IIoT device and the edge of the network for improving the overall performance of inference and for enhancing privacy of the input data, such as industrial product images.


05 May 2021