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NVIDIA CES 2016 Press Conference

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NVIDIA
NVIDIA

At a press event kicking off CES 2016, we unveiled artificial intelligence technology that will let cars sense the world around them and pilot a safe route forward. Dressed in his trademark black leather jacket, speaking to a crowd of some 400 automakers, media and analysts, NVIDIA CEO Jen-Hsun Huang revealed DRIVE PX 2, an automotive supercomputing platform that processes 24 trillion deep learning operations a second. That’s 10 times the performance of the first-generation DRIVE PX, now being used by more than 50 companies in the automotive world. The new DRIVE PX 2 delivers 8 teraflops of processing power. It has the processing power of 150 MacBook Pros. And it’s the size of a lunchbox in contrast to other autonomous-driving technology being used today, which takes up the entire trunk of a mid-sized sedan. “Self-driving cars will revolutionize society,” Huang said at the beginning of his talk. “And NVIDIA’s vision is to enable them.”

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Jen-Hsun Huang, Co-Founder & CEO, NVIDIA | Jan. 4, 2016 ACCELERATING THE RACE TO SELF-DRIVING CARS
2 SELF-DRIVING IS A MAJOR COMPUTER SCIENCE CHALLENGE SOFTWARE SUPERCOMPUTER DEEP LEARNING
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 THE SELF-DRIVING REVOLUTION Safer Driving New Mobility Services Urban Redesign
5 TWO VISIONS
6 LOCALIZE MAP THE BASIC SELF-DRIVING LOOP CONTROLSENSE PLAN PERCEIVE
7 SELF-DRIVING IS HARD The world is complex The world is unpredictable The world is hazardous
8 DEEP LEARNING TO THE RESCUE “The GPU is the workhorse of modern A.I.” DNN GPUBIG DATA
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 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 DEEP LEARNING EVERYWHERE NVIDIA Titan X NVIDIA Jetson NVIDIA Tesla NVIDIA DRIVE PX
12 END-TO-END DEEP LEARNING PLATFORM FOR SELF-DRIVING CARS DNN TRAINING PLATFORM IN-CAR AI SUPERCOMPUTER DEEP NEURAL NETWORK
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
14KITTI dataset
15Courtesy of Cityscapes dataset project
16Courtesy of Cityscapes dataset project
17Courtesy of Audi
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 “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 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 “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 “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 “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 MANY THINGS TO LEARN
25 END-TO-END DEEP LEARNING PLATFORM FOR SELF-DRIVING CARS NVIDIA DRIVE PX 2NVIDIA DIGITS NVIDIA DRIVENET Localization Planning Visualization Perception DRIVEWORKS
26
27
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
29 150 MACBOOK PROS IN YOUR TRUNK 6 TITAN X = 42 TFLOPS, Core i7 = 280 GFLOPS, 42 / 0.28 = 150 MacBook Pros
30 2 NEXT-GEN TEGRA PROCESSORS
31 2 NEXT-GEN PASCAL GPUS
32 LIQUID COOLING 250W | Operation up to 80C (175F) ambient | 256 cubic inches
33 NVIDIA DRIVE PX 2
34 NVIDIA’s Deep Learning Car Computer Selected by Volvo on Journey Towards a Crash-Free Future
35 NVIDIA DRIVE PX SELF-DRIVING CAR PLATFORM NVIDIA DRIVE PX 2NVIDIA DIGITS NVIDIA DRIVENET Localization Planning Visualization Perception DRIVEWORKS
NVIDIA CES 2016 Press Conference

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NVIDIA CES 2016 Press Conference

  • 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
  • 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
  • 15. 15Courtesy of Cityscapes dataset project
  • 16. 16Courtesy of Cityscapes dataset project
  • 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
  • 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
  • 32. 32 LIQUID COOLING 250W | Operation up to 80C (175F) ambient | 256 cubic inches
  • 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
  翻译：