Edge and Distributed Machine Learning - BKJackson/BKJackson_Wiki GitHub Wiki

An Overview of Model Compression Techniques for Deep Learning in Space - Leveraging data science to optimize at the extreme edge, Aug 31, 2020

Machine Learning at the Network Edge: A Survey - This survey describes major research efforts where machine learning has been deployed at the edge of computer networks. July 31, 2019

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications - We present a class of efficient models called MobileNets for mobile and embedded vision applications. MobileNets are based on a streamlined architecture that uses depth- wise separable convolutions to build light weight deep neural networks. Apr 17, 2017

Edge Machine Learning at Microsoft Research India - This repository provides code for machine learning algorithms for edge devices developed at Microsoft Research India.

Distributed Deep Neural Networks over the Cloud, the Edge and End Devices - We propose distributed deep neural networks (DDNNs) over distributed computing hierarchies, consisting of the cloud, the edge (fog) and end devices. While being able to accommodate inference of a deep neural network (DNN) in the cloud, a DDNN also allows fast and localized inference using shallow portions of the neural network at the edge and end devices.

MODI: Mobile Deep Inference Made Efficient by Edge Computing - MODI improves deep learning powered mo- bile applications performance with optimizations in three complementary aspects. First, MODI provides a number of models and dynamically selects the best one during runtime. Second, MODI extends the set of models each mobile application can use by storing high quality mod- els at the edge servers. Third, MODI manages a central- ized model repository and periodically updates models at edge locations, ensuring up-to-date models for mobile applications without incurring high network latency. Our evaluation demonstrates the feasibility of trading off in- ference accuracy for improved inference speed, as well as the acceptable performance of edge-based inference.