The document presents an overview of NVIDIA's advancements in self-driving technology, highlighting the capabilities of the NVIDIA Drive PX 2, which features a supercomputer with deep learning capabilities for autonomous vehicles. It emphasizes the importance of deep learning in improving the perception and decision-making processes for self-driving cars, showcasing various partnerships and achievements from companies like Audi, BMW, and Ford. The narrative outlines the challenges of autonomous driving and the role of NVIDIA's technology in driving the self-driving revolution forward.

1. Jen-Hsun Huang, Co-Founder & CEO, NVIDIA | Jan. 4, 2016
ACCELERATING THE RACE
TO SELF-DRIVING CARS

2. 2
SELF-DRIVING IS A
MAJOR COMPUTER SCIENCE CHALLENGE
SOFTWARE SUPERCOMPUTER
DEEP LEARNING

3. 3
NVIDIA DRIVE PX 2
12 CPU cores | Pascal GPU | 8 TFLOPS | 24 DL TOPS | 16nm FF | 250W | Liquid Cooled
World’s First AI Supercomputer for Self-Driving Cars

4. 4
THE SELF-DRIVING REVOLUTION
Safer Driving New Mobility Services Urban Redesign

5. 5
TWO VISIONS

6. 6
LOCALIZE
MAP
THE BASIC SELF-DRIVING LOOP
CONTROLSENSE
PLAN
PERCEIVE

7. 7
SELF-DRIVING IS HARD
The world is complex The world is unpredictable The world is hazardous

8. 8
DEEP LEARNING TO THE RESCUE
“The GPU is the workhorse of modern A.I.”
DNN GPUBIG DATA

9. 9
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2009 2010 2011 2012 2013 2014 2015 2016
THE AI RACE IS ON
IBM Watson Achieves Breakthrough
in Natural Language Processing
Facebook
Launches Big Sur
Baidu Deep Speech 2
Beats Humans
Google
Launches TensorFlow
Microsoft & U. Science & Tech, China
Beat Humans on IQ
Toyota Invests $1B
in AI Labs
IMAGENET
Accuracy Rate
Traditional CV Deep Learning

10. 10
EDUCATION
START-UPS
CNTKTENSORFLOW
DL4J
THE ENGINE OF MODERN AI
NVIDIA GPU PLATFORM
*U. Washington, CMU, Stanford, TuSimple, NYU, Microsoft, U. Alberta, MIT, NYU Shanghai
VITRUVIAN
SCHULTS
LABORATORIES
TORCH
THEANO
CAFFE
MATCONVNET
PURINEMOCHA.JL
MINERVA MXNET*
CHAINER
BIG SUR WATSON
OPENDEEPKERAS

11. 11
DEEP LEARNING EVERYWHERE
NVIDIA Titan X
NVIDIA Jetson
NVIDIA Tesla
NVIDIA DRIVE PX

12. 12
END-TO-END DEEP LEARNING PLATFORM
FOR SELF-DRIVING CARS
DNN TRAINING
PLATFORM
IN-CAR AI
SUPERCOMPUTER
DEEP NEURAL NETWORK

13. 13
39%
55%
72%
88%
30%
40%
50%
60%
70%
80%
90%
100%
7/2015 8/2015 9/2015 10/2015 11/2015 12/2015
Top Score
9 inception layers
3 convolutional layers
37M neurons
40B operations
Single and multi-class detection
Segmentation
KITTI Dataset: Object Detection
NVIDIA DRIVENet

14. 14KITTI dataset

15. 15Courtesy of Cityscapes dataset project

16. 16Courtesy of Cityscapes dataset project

17. 17Courtesy of Audi

18. 18
“Using NVIDIA DIGITS deep
learning platform, in less than
four hours we achieved over 96%
accuracy using Ruhr University
Bochum’s traffic sign database.
While others invested years of
development to achieve similar
levels of perception with
classical computer vision
algorithms, we have been able
to do it at the speed of light.”
Matthias Rudolph, Director of Architecture,
Driver Assistance Systems, Audi

19. 19
“Due to deep learning,
we brought the vehicle’s
environment perception a
significant step closer to human
performance and exceeded the
performance of classic computer
vision.”
Ralf G. Herrtwich
Director of Vehicle Automation, Daimler

20. 20
Image from ZMP
[Sahin]
“Deep learning technology can
dramatically improve accuracy
of detection and decision-
making algorithms for
autonomous driving. ZMP is
achieving remarkable results
using deep neural networks on
NVIDIA GPUs for pedestrian
detection. We will expand our
use of deep learning on NVIDIA
GPUs to realize our driverless
Robot Taxi service.”
Hisashi Taniguchi
CEO, ZMP Inc.

21. 21
“BMW is exploring the use of
deep learning for a wide range
of automotive use cases, from
autonomous driving to quality
inspection in manufacturing.
The ability to rapidly train deep
neural networks on vast amounts
of data is critical. Using an NVIDIA
GPU cluster with NVIDIA DIGITS,
we are achieving excellent results.”
Uwe Higgen
Head of BMW Group Technology Office (USA)

22. 22
“With the NVIDIA deep learning
platform, we have greatly
improved performance on a variety
of image recognition applications
for cars, surveillance cameras and
robotics. The remarkable thing is
that we did it all with a single
NVIDIA GPU-powered deep neural
network, in a very short time.”
Daisuke Okanohara
Founder & SVP, Preferred Networks
Distributed Cooperative Deep Learning

23. 23
“Deep learning on NVIDIA DIGITS
has allowed for a 30x enhancement
in training pedestrian detection
algorithms, which are being
further tested and developed as
we move them onto the NVIDIA
DRIVE PX.”
Dragos Maciuca, Technical Director,
Ford Research and Innovation Center

24. 24
MANY THINGS TO LEARN

25. 25
END-TO-END DEEP LEARNING PLATFORM
FOR SELF-DRIVING CARS
NVIDIA DRIVE PX 2NVIDIA DIGITS
NVIDIA DRIVENET
Localization
Planning
Visualization
Perception
DRIVEWORKS

26. 26

27. 27

28. 28
NVIDIA DRIVE PX 2
TITAN X DRIVE PX 2
Process 28nm 16nm FinFET
CPU —
12 CPU cores
8-core A57 +
4-core Denver
GPU Maxwell Pascal
TFLOPS 7 8
DL TOPS 7 24
AlexNet 450 images / sec 2,800 images / sec

29. 29
150 MACBOOK PROS IN YOUR TRUNK
6 TITAN X = 42 TFLOPS, Core i7 = 280 GFLOPS, 42 / 0.28 = 150 MacBook Pros

30. 30
2 NEXT-GEN TEGRA PROCESSORS

31. 31
2 NEXT-GEN PASCAL GPUS

32. 32
LIQUID COOLING
250W | Operation up to 80C (175F) ambient | 256 cubic inches

33. 33
NVIDIA DRIVE PX 2

34. 34
NVIDIA’s Deep Learning
Car Computer Selected by
Volvo on Journey Towards
a Crash-Free Future

35. 35
NVIDIA DRIVE PX
SELF-DRIVING CAR PLATFORM
NVIDIA DRIVE PX 2NVIDIA DIGITS
NVIDIA DRIVENET
Localization
Planning
Visualization
Perception
DRIVEWORKS