7 October 2016 | Seoul

NAUTOMOTIVE ROADMAP

2

NVIDIA AUTOMOTIVE

10M CARS ON THE ROAD

20+ BRANDS

100+ MODELS

3

SELF-DRIVING CARS ARE AN AI CHALLENGE

PERCEPTION AI PERCEPTION AI LOCALIZATION DRIVING AI

DEEP LEARNING

4

Training on DGX-1

Driving with DriveWorks

KALDI

LOCALIZATION

MAPPING

DRIVENET

NVIDIA DGX-1 NVIDIA DRIVE PX 2

NVIDIA AI SYSTEM FOR AUTONOMOUS DRIVING

5

Engineered for Deep Learning | 170TF FP16 | 8x Tesla P100

NVLink Hybrid Cube Mesh | Accelerates Major AI Frameworks

NVIDIA DGX-1 WORLD’S FIRST DEEP LEARNING SUPERCOMPUTER

6

DRIVE PLATFORMS

Full Autonomy

AutoChauffeur

AutoCruise

7

NVIDIA DRIVE PX 2 AUTOCRUISE

10W AI Car Computer | Passive Cooling | Automotive IO

Multiple Cameras & Sensors | DriveWorks SW/SDK

AI Highway Driving | Localization & HD Mapping

Tegra Parker SoC — 1.3 TFLOPS, 6 CPU Cores, Integrated ISP

8

Scalable from 1 to 4 Processors to Multiple DRIVE PX 2s

2 Parker + 2 Pascal GPU, 20 TOPS DL, 120 SPECINT, 80W

Up to 12 Cameras; plus LIDAR, Radar, Ultrasonic sensors

DriveWorks SW/SDK | AI Perception | Localization & Mapping

NVIDIA DRIVE PX 2 AUTOCHAUFFEUR & FULLY AUTONOMOUS

9

INTRODUCING XAVIER AI SUPERCOMPUTER SOC

7 Billion Transistors 16nm FF

8 Core Custom ARM64 CPU

512 Core Volta GPU

New Computer Vision Accelerator

Dual 8K HDR Video Processors

Designed for ASIL C Functional Safety

7 October 2016 | Seoul

DEEP LEARNING FOR INTELLIGENT MACHINES

11

APPLICATIONS OF DEEP LEARNING Image Classification, Object

Detection, Localization Face Recognition Speech & Natural Language

Processing

Medical Imaging & Interpretation

Seismic Imaging & Interpretation Recommendation

12

NVIDIA GPU

AMAZING RATE OF IMPROVEMENT

Pedestrian Detection

CALTECH

CV-based DNN-based

Object Detection

KITTI

Image Recognition

IMAGENET

Top Score

NVIDIA DRIVENet

13

AI FOR THE AGRICULTURAL AND INDUSTRIAL SUPPLY CHAIN

Intelligent Factory

Pick and place

Complex/custom tasks

Visual inspection

Task consolidation

Dynamic reconfiguration

Collaborative robotics

Efficiency optimization

Factory simulation

Smart Operations

Infrastructure inspection

Predictive maintenance

Physical security

Logistics

Autopilot/self-driving trucks

Robot/drone delivery and support

Intelligent Warehouse

Inventory management

Bin picking

Pallet movement

Mining and Agriculture

Equipment automation

Operational safety

14

COMMERCIAL/INDUSTRIAL UAVS

Logistics

Warehouse automation

Package delivery

Inspection

Wind turbines

Bridges

Oil rigs

Pipelines

High-voltage power lines

Cell towers

Precision Agriculture

Planting

Spraying

Security

Enterprise security

Ad hoc security systems

Emergency Response

First Responder

Search and Rescue

15

Jetson TX1 AI Computer on a Module

Advanced tech for intelligent machines

Unmatched performance under 10W

Smaller than a credit card

16

JETSON TX1

GPU 1 TFLOP/s 256-core Maxwell

CPU 64-bit ARM A57 CPUs

Memory 4 GB LPDDR4 | 25.6 GB/s

Storage 16 GB eMMC

Wifi/BT 802.11 2x2 ac/BT Ready

Networking 1 Gigabit Ethernet

Size 50mm x 87mm

Interface 400 pin board-to-board connector

17

NVIDIA JETPACK 2.3

• TensorRT: Up to 2x performance for Deep Learning inference

• cuDNN: The most flexible library for Deep Learning

• CUDA 8.0: Improved performance and EGL interoperability

• Multimedia API: Encode/decode, scaling, color space conversion, camera imaging and ISP support

• Linux4Tegra 24.2: Complete systems software

SDK for embedded AI

18

UP TO 2X DEEP LEARNING PERF AND EFFICIENCY

Footnotes

• The efficiency was measured using the methodology outlined

in the whitepaper:

https://www.nvidia.com/content/tegra/embedded-

systems/pdf/jetson_tx1_whitepaper.pdf

• Jetson TX1 efficiency is measured at GPU frequency of 691

MHz.

• Intel Core i7-6700k efficiency was measured for 4 GHz CPU

clock.

• GoogLeNet batch size was limited to 64; that is the maximum

supported by Jetpack 2.0. With Jetpack 2.3 and TensorRT ,

GoogLeNet batch size 128 is also supported for higher perf.

• FP16 results for Jetson TX1 are comparable to FP32 results

for Intel Core i7-6700k as FP16 incurs no classification

accuracy loss over FP32.

• Latest publicly available software versions of IntelCaffe and

MKL2017 beta were used.

• For Jetpack 2.0 and Intel Core i7, non-zero data was used for

both weights and input images. For Jetpack 2.3 (TensorRT)

real images and weights were used.

19

Jetson TX1 Developer Kit

Jetson TX1

Developer Board

5MP Camera

20

Jetpack SDK

Libraries

Developer tools

Design collateral

Developer Forum

Training and Tutorials

Ecosystem

http://developer.nvidia.com/embedded-computing

COMPREHENSIVE DEVELOPER SITE

21

DEEP LEARNING END-TO-END

Train

Step 1: Train

Optimize Deploy

NVIDIA DGX-1: Train your model with NVIDIA DIGITS Software on DGX-1, this highest performance training solution for DNNs

TensorRT

Dramatically speed up/reduce memory usage for your model

Jetson TX1: Deploy your model to your fleet of Jetson TX1- enabled products. Jetson TX1 is the highest performance inference solution under 10W

22

Jetson TX1 Module & Developer Kit

Available now from our

regional partners

MDS Technology

http://www.embedsolution.com/main/solutions/sol02_10_01.asp

23

A COMPLETE DEEP LEARNING PLATFORM MANAGE TRAIN DEPLOY

DIGITS

DATA CENTER AUTOMOTIVE

TRAIN TEST

MANAGE / AUGMENT EMBEDDED

GPU INFERENCE ENGINE

PROTOTXT

24

May 8 - 11, 2017 | Silicon Valley | #GTC17 www.gputechconf.com

CONNECT

Connect with technology experts from NVIDIA and other leading organizations

LEARN

Gain insight and valuable hands-on training through hundreds of sessions and research posters

DISCOVER

See how GPU technologies are creating amazing breakthroughs in important fields such as deep learning

INNOVATE

Hear about disruptive innovations as early-stage companies and startups present their work

Don’t miss the world’s most important event for GPU developers May 8 – 11, 2017 in Silicon Valley

JOIN THE ACTION! PRESENT A TALK, LAB OR POSTER AT GTC 2017. APPLY AT

WWW.GPUTECHCONF.COM