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The Revolution of Deep Learning
This document discusses the rise of deep learning and its key drivers like big data, GPU acceleration, and better algorithms. It outlines NVIDIA's deep learning SDK and frameworks that provide high performance GPU acceleration for computer vision, speech, language, and recommendation applications. The document notes that industries like cloud computing, healthcare, media, security, and autonomous machines are adopting deep learning. It also highlights NVIDIA's DGX-1 deep learning supercomputer and discusses advances in areas like speech recognition, medical imaging, autonomous driving, robotics, and art through deep learning techniques.
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The Revolution of Deep Learning
- 1. Journée ROMEO, 9juin 2016 Frédéric Parienté, Tesla Accelerated Computing, NVIDIA Corporation LA RÉVOLUTION DU DEEP LEARNING
- 2. Deep Learning Achieves “Superhuman”Results A NEW COMPUTING MODEL Traditional Computer Vision Experts + Time Deep Learning Object Detection DNN + Data + HPC ImageNet
- 3. KEY DRIVERS FORDEEP LEARNING Big Data GPU AccelerationBetter Algorithms 350 millions images uploaded per day 2.5 Petabytes of customer data hourly 300 hours of video uploaded every minute “The Three Breakthroughs that have Finally Unleashed A.I. on the World”
- 4. NVIDIA DEEP LEARNINGSDK High Performance GPU-Acceleration for Deep Learning COMPUTER VISION SPEECH AND AUDIO BEHAVIOR Object Detection Voice Recognition Translation Recommendation Engines Sentiment Analysis DEEP LEARNING cuDNN MATH LIBRARIES cuBLAS cuSPARSE MULTI-GPU NCCL cuFFT Mocha.jl Image Classification DEEP LEARNING SDK FRAMEWORKS APPLICATIONS
- 5. EVERY INDUSTRY WANTSDEEP LEARNING Cloud Service Provider Medicine Media & Entertainment Security & Defense Autonomous Machines Cancer cell detection Diabetic grading Drug discovery Pedestrian detection Lane tracking Recognize traffic sign Face recognition Video surveillance Cyber security Video captioning Content based search Real time translation Image/Video classification Speech recognition Natural language processing
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- 11. BAIDU: DL DEVELOPERSNEED HPC “Investments in computer systems — and I think the bleeding-edge of AI, and deep learning specifically, is shifting to HPC (high performance computing) — can cut down the time to run an experiment, and therefore go around the circle, from a week to a day and sometimes even faster.” “Those of us that grew up doing machine learning often didn’t grow up with an HPC or computer systems background … partnerships between machine learning researchers and computer systems researchers tend to help both teams drive a lot of machine learning progress.” — Andrew Ng
- 12. MICROSOFT: “SUPER DEEPNETWORKS” Microsoft Deep ResNet http://arxiv.org/pdf/1512.03385v1.pdf 18 LAYERS 1.8 GF 152 LAYERS 11.3 GF >6X MORE FLOPSRevolution of Depth
- 13. NVIDIA DGX-1 WORLD’S FIRSTDEEP 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
- 14. 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. EUROPE’S BRIGHTEST MINDS & BEST IDEAS 2 Days | 800 Attendees | 50+ Exhibitors | 50+ Speakers | 15+ Tracks | 15+ Workshops | 1-to-1 Meetings