Using and Understanding the Neural Compute SDK: mvNCCheck

Neural Compute SDK Toolkit: mvNCCheck

The Intel® Movidius™ Neural Compute Software Development Kit (NCSDK) comes with three tools that are designed to help users get up and running with their Intel® Movidius™ Neural Compute Stick (Intel® Movidius™ NCS): mvNCCheck, mvNCCompile, and mvNCProfile. In this article, we will aim to provide a better understanding of how the mvNCCheck tool works and how it fits into the overall workflow of the Neural Compute SDK.

Deploying Your Customized Caffe Models on Intel® Movidius™ Neural Compute Stick

Why do I need a custom model?

The Neural Compute Application Zoo (NCAppZoo) downloads and compiles a number of pre-trained deep neural networks such as GoogLeNet, AlexNet, SqueezeNet, MobileNets, and many more. Most of these networks are trained on ImageNet dataset, which has over a thousand classes (also called categories) of images. These example networks and applications make it easy for developers to evaluate the platform, and also build simple projects. If you plan on building a proof of concept (PoC) for an edge product, such as smart digital cameras, gesture controlled drones, or industrial smart cameras, you will probably need to customize your neural network.

Battery-Powered Deep Learning Inference Engine

Improving visual perception of edge devices

LiPo batteries (lithium polymer batteries) and embedded processors are a boon to the Internet of Things (IoT) market. They have enabled IoT device manufacturers to pack more features and functionalities into mobile edge devices, while still providing a long runtime on a single charge. The advancement in sensor technology, especially vision-based sensors, and software algorithms that process large amount of data generated by these sensors has spiked the need for better computational performance without compromising on battery life or real-time performance of these mobile edge devices.

MobileNets on Intel® Movidius™ Neural Compute Stick and Raspberry Pi 3

Introduction

Deep Learning at the edge gives innovative developers across the globe the opportunity to create architecture and devices promising to solve problems and deliver innovative solutions like the Google’s Clips Camera with Intel’s Movidius VPU Inside. An edge device typically should be portable and use low power while delivering scalable architecture for the deep learning neural network. This article will showcase one such deep learning edge solution that pairs the popular Raspberry Pi 3 single board computer with the Intel® Movidius™ Neural Compute Stick.

Rethinking Deep Learning

Slides for Darren S Crews’ Presentation @ QCon San Fransisco

The Movidius™ Neural Compute Stick (NCS) is a tiny fanless deep learning device that you can use to learn AI programming at the edge. NCS is powered by the same low power high-performance Movidius™ Vision Processing Unit (VPU) that can be found in millions of smart security cameras, gesture-controlled drones, industrial machine vision equipment, and more. The Movidius Neural Compute Stick enables rapid prototyping, validation and deployment of Deep Neural Network (DNN) inference applications at the edge. Its low-power VPU architecture enables an entirely new segment of AI applications that aren’t reliant on a connection to the cloud. The NCS combined with Movidius™ Neural Compute SDK allows deep learning developers to profile, tune, and deploy Convolutional Neural Network (CNN) on low-power applications that require real-time inferencing. This talk explores this cutting-edge device an offers a glimpse into what the future holds for software developers diving into the space of deep learning.

Run NCS Applications on Raspberry Pi

Why an Embedded Board?

The Intel® Movidius™ Neural Compute Stick (Intel® Movidius™ NCS) is essentially an Intel® Movidius™ visual processing unit (VPU) on a USB stick. It is the same low-power chip that provides visual intelligence to millions of low-power embedded devices such as smart security cameras, gesture controlled drones, industrial machine vision equipment, and more. Since the Intel Movidius NCS is designed for low-power applications, it makes sense we pair it with a low-power embedded system such as MinnowBoard, UP Board, or Raspberry Pi (RPi).

Build an Image Classifier in 5 steps

What is Image Classification?

Image classification is a computer vision problem that aims to classify a subject or an object present in an image into predefined classes. A typical real-world example of image classification is showing an image flash card to a toddler and asking the child to recognize the object printed on the card. Traditional approaches to providing such visual perception to machines have relied on complex computer algorithms that use feature descriptors, like edges, corners, colors, and so on, to identify or recognize objects in the image.