Deep learning gateway on Raspberry Pi and other edge devices

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

ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation

The first competitive instance segmentation approach that runs on small edge devices at real-time speeds.

vendor independent deep learning library, compiler and inference framework microcomputers and micro-controllers

Helmut Hoffer von Ankershoffen experimenting with arm64 based NVIDIA Jetson (Nano and AGX Xavier) edge devices running Kubernetes (K8s) for machine learning (ML) including Jupyter Notebooks, TensorFlow Training and TensorFlow Serving using CUDA for smart IoT.

Automatic Over the Air Deployment of Improved Machine Learning Models to IoT Devices for Edge Processing

Hub for the Radlet IOT ecosystem

Yocto Project meta layer for EdgeX Foundry Services

This repository is a PyTorch implementation of NIPS 2019 Paper "Shallow RNNs: A Method for Accurate Time-series Classification on Tiny Devices"

Personal blog polarize.ai of Helmut Hoffer von Ankershoffen

TensorRT optimises any Deep Learning model by not only making it lightweight but also by accelerating its inference speed with an idea to extract every ounce of performance from the model, making it perfect to be deployed at the edge. This repository helps you convert any Deep Learning model from TensorFlow to TensorRT!

Edge Computing using Tensorflow Lite

An end-to-end video analytic demonstration performing video analytics on edge devices and centralized system

Home Climate Control ESP32 based edge device firmware

A framework for offloading parts of an Android mobile application to nearby Android mobile devices using Wifi-Direct , edge devices (cloudlets), and remote clouds

Home Climate Control ESP8266 based edge device firmware

Oct 12th @ in5 - Hands-on Internet Of Things workshop with Etisalat Digital & PTC. At this session, we’ll take you step by step over the process of creating a modular IoT solution using the Etisalat Thingworx Platform to monitor weather conditions at various locations. We’ll show you how to sync data from edge devices and sensors onto the cloud in real time, model this data to fit business use cases and present it over the web using the platform’s easy-to-use UI building tools.