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2016 06 nvidia-isc_supercomputing_car_v02
The document discusses advancements in supercomputing for self-driving cars, highlighting the role of deep learning and NVIDIA's Drive PX platform as essential components for autonomous vehicle technology. It outlines key developments by companies like Uber, Tesla, and Volvo in the self-driving sector, alongside a description of NVIDIA's hardware capabilities for AI processing. The text emphasizes the importance of high-performance computing and deep learning frameworks for achieving advancements in vehicle autonomy and safety.
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2016 06 nvidia-isc_supercomputing_car_v02
- 1. SUPERCOMPUTING IN ACAR Carlo Nardone, Senior Solution Architect EMEA Enterprise ISC 2016
- 2. 2 ENTERPRISE AUTOGAMING DATACENTERPRO VISUALIZATION THE WORLD LEADER IN VISUAL COMPUTING
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- 5. 5 SIMULATION MEANS BETTERPRODUCTS, FASTER ACTUAL CRASH SIMULATED CRASH
- 6. 6 THE SELF DRIVINGREVOLUTION Safer Driving New Mobility Services Urban Redesign
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- 8. 8 Uber Enters theRace 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 FORSELF-DRIVING CARS
- 10. 10 NVIDIA PILOTNET VIDEOPaper on http://arxiv.org/abs/1604.07316
- 11. 11 THE BIG BANGIN MACHINE LEARNING DNN GPUBIG DATA “The GPU is the workhorse of modern A.I.”
- 12. 12 Image “Volvo XC90” Imagesource: “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?
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- 14. DEEP LEARNING EVERYWHERE NVIDIATitan X NVIDIA Jetson NVIDIA Tesla NVIDIA DRIVE PX
- 15. 15NVIDIA CONFIDENTIAL. DONOT 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
- 16. 16 NVIDIA END-TO-END AUTONOMOUS DRIVINGPLATFORM NVIDIA DRIVE PX 2NVIDIA DGX-1 NVIDIA DRIVENET Localization Planning Visualization Perception DRIVEWORKS
- 17. 17 NVIDIA DRIVE PX2 World’s First AI Supercomputer for Self-Driving Cars 12 CPU cores | Pascal GPU | 8 TFLOPS | 24 DL TOPS | 16nm FF | 250W | Liquid Cooled
- 19. 19 SELF DRIVING COMPUTERI/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
- 20. 20 DRIVE™ PX 2 COMPUTATIONENGINES 24 DL TOPS, 8 TFLOPS, high performance CPU/GPU complex
- 21. 21 NVIDIA DRIVE PXSW STACK A full stack of rich software components
- 22. 22 GPU INFERENCE ENGINE High-performanceframework 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 ScientistVehicle Drive PX - Deploy Model Classification Detection Segmentation DIGITS / Tesla - Train Network Solver Dashboard
- 24. 24 A COMPLETE DEEPLEARNING PLATFORM MANAGE TRAIN DEPLOY DIGITS DATA CENTER AUTOMOTIVE TRAINTEST MANAGE / AUGMENT EMBEDDED GPU INFERENCE ENGINE
- 25. NVIDIA DRIVE™ PX2 Selected by Volvo on Journey Towards a Crash-Free Future
- 26. 26 WORLD’S FIRST AUTONOMOUSCAR 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 3355828197 www.nvidia.com/drive
- 28. 28 DEEP LEARNING & ARTIFICIALINTELLIGENCE 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
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- 30. 30 MANY THINGS TOLEARN
- 31. 31 THE BASIC SELF-DRIVINGLOOP LOCALIZE MAP CONTROLSENSE PLAN PERCEIVE
- 32. 32 INTERFACES 70 Gigabits persecond 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 FORALL DEVELOPERS
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- 36. “Using NVIDIA DIGITSdeep 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 onNVIDIA 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
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- 40. 42 DGX-1 GPU CLUSTER Twofully 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