Webinar Agenda

Topic:

• Demystifying Deep Learning

• NVIDIA Tools & SDKs

• Deploying with Jetson

• Deep Vision Primitives

• 2 Days To A Demo

• Reinforcement Learning

• Simulation

• Conclusion / Q&A

“ Billions of intelligent devices will take advantage of DNNs to provide personalization and localization as GPUs become faster and faster over the next several years.”

— Tractica

INTELLIGENT MACHINES

Train Model

Robust Validation

Configure Network

Turing-Complete

Runtime Inference

Onboard Intelligence

A NEW COMPUTING MODEL

ERA of LEARNING

Deep Learning

Power Efficiency

Krizhevsky et al., NIPS 2012. ImageNet Classification with Deep Convolutional Neural Networks.

Image Classification Rate

Microsoft “Super Deep Network”

Berkeley’s Brett End-to-End

Reinforcement Learning

Deep Speech 2 One network, 2 languages

DeepMind AlphaGo Rivals a World Champion TU Delft Deep-Learning

Amazon Picking Champion

MILESTONES IN AI

Microsoft & Google “Superhuman” Image Recognition (ResNet)

WEpod Autonomous People Shuttle On the Road Starship Delivery

Robot Enters Service

2014-2016

PC GAMING

ONE ARCHITECTURE — END-TO-END AI

CUDA + Linux throughout the stack.

ONE ARCHITECTURE — END-TO-END AI

8

POWERING THE DEEP LEARNING ECOSYSTEM NVIDIA SDK accelerates every major framework

COMPUTER VISION

OBJECT DETECTION IMAGE CLASSIFICATION

SPEECH & AUDIO

VOICE RECOGNITION LANGUAGE TRANSLATION

NATURAL LANGUAGE PROCESSING

RECOMMENDATION ENGINES SENTIMENT ANALYSIS

DEEP LEARNING FRAMEWORKS

Mocha.jl

NVIDIA DEEP LEARNING SDK

developer.nvidia.com/deep-learning-software

DIGITS

Test Image

Monitor Progress Configure DNN Process Data Visualize Layers

Interactive Training with DIGITS

github.com/NVIDIA/DIGITS

github.com/NVIDIA/DIGITS

Classified Object!

Trained Deep Neural Net Model

Camera Inputs

X

Training: NVIDIA DIGITS Inference: NVIDIA Jetson TX1

TRAINING

DATA MANAGEMENT

MODEL VALIDATION

DGX-1

TESLA

TITAN-X

cuDNN TensorRT

DIGITS Workflow

JETSON TX1 EMBEDDED AI SUPERCOMPUTER

10W | 1 TF FP16 | >20 images/sec/W

JetPack 2.3

Software Components

Linux4Tegra R24.2 Ubuntu 16.04 LTS aarch64

Multimedia API SDK Low-level camera/codec

Runtime Inference TensorRT RC1 + FP16

CUDA Toolkit 8 cuDNN v5.1, cuGRAPH

VisionWorks 1.5.2.14 OpenCV4Tegra 2.4.13.17

OpenGL 4.5.0 EGL 1.4 / OpenGL ES 3.1

Tegra System Profiler Tegra Graphics Debugger

Double Performance, 20x Efficiency

developer.nvidia.com/jetpack

DIGITS OPTIMIZATION ENGINE

EXECUTION ENGINE

PLAN NEURAL NETWORK

TensorRT

• Fuse network layers

• Eliminate concatenation layers

• Kernel specialization

• Auto-tuning for target platform

• Select optimal tensor layout

• Batch size tuning TRAINED

NEURAL NETWORK

OPTIMIZED INFERENCE RUNTIME

developer.nvidia.com/TensorRT

Object Detection Localization

Image Recognition Classification

Segmentation Free Space

DEEP VISION

Registration SLAM

3D ShapeNet

GETTING STARTED JETSON COMMUNITY

Developer Forums devtalk.nvidia.com eLinux Wiki eLinux.org/Jetson_TX1

16

developer.nvidia.com/embedded

COMPREHENSIVE DEVELOPER S ITE

• JetPack SDK

• Libraries

• Developer tools

• Design collateral

• Developer Forum

• Training and Tutorials

• Ecosystem Partners

Hands-On Labs

nvidia.com/DLI

nvidia.com/DLI

Self-Paced Courses Nanodegree Programs

PARTNERSHIP Latest Technology GPU/DL Experts Global Sales & Marketing Network Training from Deep Learning Institute

INCEPTION PROGRAM

CONNECTING WITH AI STARTUPS

www.nvidia.com/inception

2 DAYS TO A DEMO

Ten Steps to Deep Learning

1. Install DIGITS on PC

2. Get JetPack 2.3 & TensorRT

3. Build the Vision Source

4. Dig Into the Example Code

5. Classify Images with ImageNet

6. Run the Camera Recognition Demo

7. Re-train the Network with DIGITS

8. Locate Object Coordinates - DetectNet

9. Run the Live Camera Detection Demo

10. Re-train DetectNet with DIGITS

github.com/dusty-nv/jetson-inference makezine.com/projects/jetson

Project includes: 1000 types of objects

pedestrian DetectNet

Robot OS (ROS) nodes

HORUS SOCIAL INNOVATION

AWARD WINNER

BLIND

ASSISTANCE

DEVICE

AERIALTRONICS

CNN

CNN

Sensory Inputs

CNN

Online L

earn

ing

MOTION CONTROL | BEHAVIOR IMITATION

PATH PLANNING | AUTONOMOUS NAVIGATION

Actions

feature maps

confidence values

Pixels to Actions

backpro

pagati

on

Experience Actuator

CNN RNN/LSTM

Loss/Reward

Solver

REINFORCEMENT LEARNING

OpenAI Gym

Gazebo

GTA-V

Unreal4Torch

OpenRAVE

TORCS

Physical Intuition

SIMULATION

LSTM ACCELERATION

cuDNN v5 - GPU-accelerated RNNs/LSTM

Forms internal working memory cells

Partially-observable inputs/environments

Launch a 2D grid of RNN cells

Supports:

Uni/Bidirectional RNNs

Non-uniform length minibatches

Dropout between layers

Jetson TX1 Developer Kit

Jetson EDU Program available

www.nvidia.com/embedded

THANK YOU! Q&A: WHAT CAN I HELP YOU BUILD?

dustinf@nvidia.com

2 Days To a Demo github.com/dusty-nv/jetson-inference

Reinforcement Learning github.com/dusty-nv/jetson-reinforcement

Access these Slides github.com/dusty-nv/jetson-presentations

Post Discussion devtalk.nvidia.com/default/topic/969035

eLinux TX1 Wiki eLinux.org/Jetson_TX1