Presentation for NVIDIA at ISC 2016 session: "Connected & Autonomous Vehicles: Driving with Data & Analytics"

1. SUPERCOMPUTING IN A CAR
Carlo Nardone, Senior Solution Architect EMEA Enterprise
ISC 2016

2. 2
ENTERPRISE AUTOGAMING DATA CENTERPRO VISUALIZATION
THE WORLD LEADER IN VISUAL COMPUTING

3. 3
IN THE BEGINNING

5. 5
SIMULATION MEANS BETTER PRODUCTS, FASTER
ACTUAL CRASH SIMULATED CRASH

6. 6
THE SELF DRIVING REVOLUTION
Safer Driving New Mobility Services Urban Redesign

7. 7
AUTONOMOUS DRIVING IS HARD

8. 8
Uber Enters the Race
Toyota Invests $1B
in AI Lab
Volvo Drive Me on
Public Roads in 2017
NHTSA: Computer
Counts as Driver
Tesla Model 3:
300K pre-orders
2016: AN AMAZING YEAR
FOR SELF-DRIVING CARS
Audi, BMW, Daimler
Buy HERE
Tesla Model S Auto-pilot
Baidu Enters the Race
Honda, Nissan, Toyota
Team Up
GM Buys Cruise

9. 9
DEEP LEARNING FOR SELF-DRIVING CARS

10. 10
NVIDIA PILOTNET VIDEO Paper on http://arxiv.org/abs/1604.07316

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THE BIG BANG IN MACHINE LEARNING
DNN GPUBIG DATA
“The GPU is the workhorse of modern A.I.”

12. 12
Image “Volvo XC90”
Image source: “Unsupervised Learning of Hierarchical Representations with Convolutional Deep Belief Networks” ICML 2009 & Comm. ACM 2011.
Honglak Lee, Roger Grosse, Rajesh Ranganath, and Andrew Ng.
WHAT IS DEEP LEARNING?

13. 13
TRAINING VS INFERENCE

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

15. 15NVIDIA CONFIDENTIAL. DO NOT DISTRIBUTE.
NVIDIA DGX-1
WORLD’S FIRST DEEP LEARNING SUPERCOMPUTER
170 TFLOPS FP16
8x Tesla P100 16GB
NVLink Hybrid Cube Mesh
Accelerates Major AI Frameworks
Dual Xeon
7 TB SSD Deep Learning Cache
Dual 10GbE, Quad IB 100Gb
3RU – 3200W

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NVIDIA END-TO-END
AUTONOMOUS DRIVING PLATFORM
NVIDIA DRIVE PX 2NVIDIA DGX-1
NVIDIA DRIVENET
Localization
Planning
Visualization
Perception
DRIVEWORKS

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

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SELF DRIVING COMPUTER I/O
NVIDIA Drive PX 2: 70 Gbps aggregate I/O
DISPLAY
DATA LOGGING
DRIVE TRAIN
POWER TRAIN
Ethernet
GMSL
Ethernet
FlexRay/Ethernet
TEGRA TEGRA
SMART
CAMERAS
CAMERAS
LIDAR
RADAR
CANBus
LVDS
USB/PCIE

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DRIVE™ PX 2
COMPUTATION ENGINES
24 DL TOPS, 8 TFLOPS, high performance
CPU/GPU complex

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NVIDIA DRIVE PX SW STACK
A full stack of rich software components

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GPU INFERENCE ENGINE
High-performance framework makes it easy to
develop GPU-accelerated inference
Production deployment solution for deep learning
inference
Optimized inference for a given trained neural
network and target GPU
Solutions for Hyperscale, ADAS, Embedded
Supports deployment of 32-bit or 16-bit inference
Maximum Performance for Deep Learning Inference
developer.nvidia.com/gpu-inference-engine
GPU Inference Engine for Automotive
Pedestrian
Detection
Lane
Tracking
Traffic Sign
Recognition
---
NVIDIA DRIVE PX 2

23. ACTIVE LEARNING
Data Scientist Vehicle
Drive PX - Deploy
Model Classification
Detection
Segmentation
DIGITS / Tesla - Train
Network
Solver
Dashboard

24. 24
A COMPLETE DEEP LEARNING PLATFORM
MANAGE TRAIN DEPLOY
DIGITS
DATA CENTER AUTOMOTIVE
TRAINTEST
MANAGE / AUGMENT
EMBEDDED
GPU INFERENCE ENGINE

25. NVIDIA DRIVE™ PX 2
Selected by Volvo on
Journey Towards a
Crash-Free Future

26. 26
WORLD’S FIRST AUTONOMOUS CAR RACE
10 teams, 20 identical cars | DRIVE PX 2 as “brain” in every car | 2016/17 Formula E season

27. THANK YOU!
cnardone@nvidia.com
+39 335 5828197
www.nvidia.com/drive

28. 28
DEEP LEARNING &
ARTIFICIAL INTELLIGENCE
Sep 28-29, 2016 | Amsterdam
www.gputechconf.eu #GTC16
SELF-DRIVING CARS VIRTUAL REALITY &
AUGMENTED REALITY
SUPERCOMPUTING & HPC
GTC Europe is a two-day conference designed to expose the innovative ways developers, businesses and academics
are using parallel computing to transform our world.
GTC EUROPE
2 Days | 800 Attendees | 50+ Exhibitors | 50+ Speakers | 15+ Tracks | 15+ Workshops | 1-to-1 Meetings

29. BACKUP SLIDES

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MANY THINGS TO LEARN

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THE BASIC SELF-DRIVING LOOP
LOCALIZE
MAP
CONTROLSENSE
PLAN
PERCEIVE

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INTERFACES
70 Gigabits per second of I/O
Sensor Fusion Interfaces:
GMSL Camera, CAN, GbE, BroadR-Reach, FlexRay,
LIN, GPIO
Displays and Cockpit Computer Interfaces
HDMI, FPDLink III and GMSL
Development and Debug Interfaces
HDMI, GbE, 10GbE, USB3, USB 2 (UART/debug),
JTAG
Auto Grade connectors Debug/Lab interfaces

33. 33
GPU INFERENCE ENGINE
Optimizations
• Fuse network layers
• Eliminate concatenation layers
• Kernel specialization
• Auto-tuning for target platform
• Select optimal tensor layout
• Batch size tuningTRAINED
NEURAL NETWORK
OPTIMIZED
INFERENCE
RUNTIME
developer.nvidia.com/gpu-inference-engine

34. OPEN PLATFORM FOR ALL DEVELOPERS

35. 37
AUTOMOTIVE PARTNERS
Self Driving Vehicles

36. “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

37. “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

38. DGX-1 DEEP LEARNING
SUPERCOMPUTER

39. 41

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DGX-1 GPU CLUSTER
Two fully connected quads,
connected at corners
160GB/s per GPU bidirectional to Peers
Load/store access to Peer Memory
Full atomics to Peer GPUs
High speed copy engines for bulk data copy
PCIe to/from CPU