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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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ACCELERATING THE RACE TO SELF-DRIVING CARS Jen-Hsun Huang, Co-Founder & CEO, NVIDIA | Jan. 4, 2016
SELF-DRIVING IS A MAJOR COMPUTER SCIENCE CHALLENGE SOFTWARE SUPERCOMPUTER DEEP LEARNING 2
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 3
THE SELF-DRIVING REVOLUTION Safer Driving New Mobility Services Urban Redesign 4
THE BASIC SELF-DRIVING LOOP MAP SENSE CONTROL LOCALIZE PLAN PERCEIVE 6
SELF-DRIVING IS HARD The world is complex The world is unpredictable The world is hazardous 7
DEEP LEARNING TO THE RESCUE DNN BIG DATA GPU “The GPU is the workhorse of modern A.I.” 8
THE AI RACE IS ON IMAGENET Accuracy Rate 100% Traditional CV Deep Learning 90% 80% 70% Baidu Deep Speech 2 Beats Humans IBM Watson Achieves Breakthrough in Natural Language Processing Facebook Launches Big Sur 60% 50% 40% 30% 20% 10% Microsoft & U. Science & Tech, China Beat Humans on IQ Google Toyota Invests $1B in AI Labs Launches TensorFlow 0% 2009 2010 2011 2012 2013 2014 2015 2016 9
THE ENGINE OF MODERN AI EDUCATION BIG SUR TENSORFLOW WATSON CNTK TORCH CAFFE THEANO MATCONVNET START-UPS DL4J MOCHA.JL PURINE CHAINER KERAS OPENDEEP MINERVA MXNET* SCHULTS LABORATORIES VITRUVIAN NVIDIA GPU PLATFORM 10 *U. Washington, CMU, Stanford, TuSimple, NYU, Microsoft, U. Alberta, MIT, NYU Shanghai
DEEP LEARNING EVERYWHERE NVIDIA DRIVE PX NVIDIA Tesla NVIDIA Jetson NVIDIA Titan X 11
END-TO-END DEEP LEARNING PLATFORM FOR SELF-DRIVING CARS DNN TRAINING PLATFORM IN-CAR AI SUPERCOMPUTER DEEP NEURAL NETWORK 12
KITTI Dataset: Object Detection NVIDIA DRIVENet 100% 90% Top Score 88% 80% 72% 9 inception layers 3 convolutional layers 37M neurons 40B operations Single and multi-class detection Segmentation 70% 60% 55% 50% 40% 39% 30% 7/2015 8/2015 9/2015 10/2015 11/2015 12/2015 13
“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 18
“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 19
“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.” Image from ZMP [Sahin] Hisashi Taniguchi CEO, ZMP Inc. 20
“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) 21
“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 22
“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 23
END-TO-END DEEP LEARNING PLATFORM FOR SELF-DRIVING CARS DRIVEWORKS Perception Localization Planning Visualization NVIDIA DRIVE PX 2 NVIDIA DIGITS NVIDIA DRIVENET 25
TITAN X DRIVE PX 2 NVIDIA DRIVE PX 2 Process 28nm 16nm FinFET 12 CPU cores CPU — 8-core A57 + 4-core Denver GPU Maxwell Pascal TFLOPS 7 8 DL TOPS 7 24 AlexNet 450 images / sec 2,800 images / sec 28
150 MACBOOK PROS IN YOUR TRUNK 6 TITAN X = 42 TFLOPS, Core i7 = 280 GFLOPS, 42 / 0.28 = 150 MacBook Pros 29
LIQUID COOLING 250W | Operation up to 80C (175F) ambient | 256 cubic inches 32
NVIDIA’s Deep Learning Car Computer Selected by Volvo on Journey Towards a Crash-Free Future 34
NVIDIA DRIVE PX SELF-DRIVING CAR PLATFORM DRIVEWORKS Perception Localization Planning Visualization NVIDIA DRIVE PX 2 NVIDIA DIGITS NVIDIA DRIVENET 35