The document discusses the Intel Movidius Neural Compute Stick (NCS), a small deep learning device that can be used to run AI models at the edge. It provides an overview of the NCS, describing its low-power dedicated hardware for neural network inference. It also outlines the steps to install the Movidius SDK and run example models using frameworks like Caffe and TensorFlow on the NCS. Key capabilities of the NCS include object detection, classification and facial recognition using pre-trained models.

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Intel Movidius: Neural Compute Stick
João Guilherme Reiser de Melo
Luana Vieira Martinez Bonatto
M.Sc Students in Electrical Engineering at UFSC
Trilha – Machine Learning

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Intel Machine Learning
● Software
○ Intel® Optimization for TensorFlow
○ Intel® Optimization for Caffe
○ Intel® Math Kernel Library
○ Intel® Movidius™ Neural Compute
SDK
● Hardware
○ Intel® Xeon Phi™ processor
○ Intel® Movidius™ Neural Compute
Stick
● Project
○ Evaluation of Intel Machine Learning Solutions

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What is the Intel Movidius
Neural Compute Stick?
The Intel Movidius Neural Compute Stick (NCS) is a tiny fanless deep learning device that
you can use to learn AI programming at the edge.
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Computer Vision
What kind of AI programming?

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Computer Vision
● Artificial Neural Network
○ Artificial Neuron

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Computer Vision
● Convolutional Neural Network

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● ILSVRC ImageNet
(2010 - 2017)
Computer Vision

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Computer Vision
Typical use cases:
Robot
Security cameras
Smart-home assistant
...
Key capabilities:
Object detection
Classification of objects
Facial recognition
...

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An Overview about Intel
Movidius NCS
Intel Movidius NCS is a low-power device designed to accelerate perform inference of
Deep Neural Network (DNN).
▪ Ultra-low-power tool
▪ Embedded applications
▪ High-performance processor
▪ Programmable architecture
▪ Small-area footprint
▪ And more!
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Dedicated structure of CNN
hardware blocks

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An Overview about Intel
Movidius NCS
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Introducing the Myriad 2
Vision Processor SoC
Myriad 2 can be found in millions of devices on the market today and continues to be
utilized for some of the most ambitious AI, vision and imaging applications where both
performance and low consumption are mandatory.
▪ An ultra-low power design
▪ Unique design for vision and AI workloads
▪ 12 programmable SHAVE cores
▪ A small-area footprint
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Intel Movidius NCS
Quick Start
1. You will need the following:
▪ Ubuntu 16.04 system, RPI3 Model B with Raspian Stretch or
Ubuntu VirtualBox instance
▪ Intel Movidius Neural Compute Stick (NCS)
▪ Internet connection to download and install NC SDK
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2. Install NC SDK
Run these commands on a terminal window
▪ mkdir -p ~/workspace
▪ cd ~/workspace
▪ git clone https://github.com/movidius/ncsdk.git
▪ cd ~/workspace/ncsdk
▪ make install

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Movidius SDK Installation
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Intel Movidius NCS
Quick Start
3. Test installation by running built-in
examples
Plug the Intel Movidius NCS to your system’s USB port and run these commands
on a new terminal window
▪ cd ~/workspace/ncsdk
▪ make examples
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4. Next steps
Review Intel Movidius NCS deep learning technical documentation

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Movidius Examples
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Intel Movidius NCS
Quick Start
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git clone http://github.com/Movidius/ncsdk && cd ncsdk && make install && make examples

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Intel Movidius NCS
Quick Start
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Movidius Neural Compute SDK (NCSDK)
Development Flow:

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Frameworks Optimized by
Intel AI Academy
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Neural Compute Stick currently supports two Deep Learning frameworks:
▪ Caffe:
Is a deep learning framework from
Berkeley Vision Labs.
▪ TensorFlow:
Is a deep learning framework from
Google.

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Frameworks Optimized by
Intel AI Academy
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Caffe:
model.prototxt → defines the network
solver.prototxt → training configuration
deploy.prototxt → model.prototxt with
modifications
model.caffemodel → weights file
model.solverstate
TensorFlow:
model.py → defines the network and
training
model.meta → graph structure
model.data → weights
model.index → index meta - data
checkpoint
freeze_graph → model.pb

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Frameworks Optimized by
Intel AI Academy
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Caffe: TensorFlow:
dense3/dense/MatMul
dense3/dense/BiasAdd
output/Softmax

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TensorFlow Files Generate
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TensorFlow Freeze Model
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Neural Compute SDK Tools
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The SDK comes with a set of tools to assist in development and deployment of
applications that utilize hardware accelerated Deep Neural Networks via the Intel
Movidius Neural Compute Stick. Each tool and its usage is described below:
▪ mvNCCompile: Converts Caffe/TF network and weights to Intel Movidius technology internal compiled format.
▪ mvNCProfile: Provides layer-by-layer statistics to evaluate the performance of Caffe/TF networks on the NCS.
▪ mvNCCheck: Compares the results from an inference by running the network on the NCS and Caffe/TF.

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Neural Compute API
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Applications for inferencing with NCSDK can be developed either in C/C++ or Python.
The API provides a software interface to Open/Close NCS, load graphs into the Intel
Movidius NCS, and run inferences on the stick.
● from mvnc import mvncapi as mvnc
● devices = mvnc.EnumerateDevices()
● device = mvnc.Device(devices[0])
● device.OpenDevice()
● graph = device.AllocateGraph(graphfile)
● graph.LoadTensor(img.astype(np.float16) , 'obj')
● output, userobj = graph.GetResult()
● graph.DeallocateGraph()
● device.CloseDevice()

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Using the SDK
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● TensorFlow:
mvNCCompile model.pb -s 12 -in=in_name -on=out_name -is 299 299 -o model.graph
mvNCProfile model.pb -s 12 -in=in_name -on=out_name
• Into the model folder:
● Caffe:
mvNCCompile deploy.prototxt -w model.caffemodel -s 12 -in in_name -on out_name -is 224
224 -o model.graph
mvNCProfile deploy.prototxt -w model.caffemodel -s 12 -in in_name -on out_name

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Generating Movidius Graph
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Compiler Model Generated
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Compiler Model Generated
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Inference Time

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Neural Compute Examples

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Movidius NCS on the Edge
Raspberry Pi 3 Model B
• ARMv8 Cortex-A53 64bit CPU
• Quad Core 1.2GHz
• 1GB RAM
• 4 USB 2 Ports
• Raspbian Stretch Desktop OS

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Movidius API Installation

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Efficient CNNs for Mobile
Applications
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● Fewer Parameters
● Much Faster Inference
● Good Accuracy
● MobileNet
● SqueezeNet

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Raspberry Pi + Movidius
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Movidius Performance Analysis

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• https://github.com/intel/caffe
• https://software.intel.com/en-us/articles/intel-optimized-tensorflow-installation-guide
• https://developer.movidius.com/
• https://movidius.github.io/ncsdk/
• https://www.raspberrypi.org/downloads/raspbian/

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João Guilherme Reiser de Melo
Luana Vieira Martinez Bonatto
Federal University of Santa Catarina
Postgraduate Program in Electrical Engineering

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