GTC 2017: Powering the AI Revolution
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Jensen Huang, founder and CEO of NVIDIA, discusses the rise of GPU computing and artificial intelligence. He outlines how GPUs have enabled massive performance increases for deep learning workloads. NVIDIA is introducing new products like the Tesla V100 GPU and DGX-1 server to further accelerate AI research and commercial applications. These announcements position NVIDIA to power continued growth in AI and deep learning.
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GTC 2017: Powering the AI Revolution
- 1. JENSEN HUANG, FOUNDER & CEO | GTC 2017 POWERING THE AI REVOLUTION
- 2. 2 LIFE AFTER MOORE’S LAW 1980 1990 2000 2010 2020 102 103 104 105 106 107 40 Years of Microprocessor Trend Data Original data up to the year 2010 collected and plotted by M. Horowitz, F. Labonte, O. Shacham, K. Olukotun, L. Hammond, and C. Batten New plot and data collected for 2010-2015 by K. Rupp Single-threaded perf 1.5X per year 1.1X per year Transistors (thousands)
- 3. 3 1980 1990 2000 2010 2020 GPU-Computing perf 1.5X per year 1000X by 2025 RISE OF GPU COMPUTING Original data up to the year 2010 collected and plotted by M. Horowitz, F. Labonte, O. Shacham, K. Olukotun, L. Hammond, and C. Batten New plot and data collected for 2010-2015 by K. Rupp 102 103 104 105 106 107 Single-threaded perf 1.5X per year 1.1X per year APPLICATIONS SYSTEMS ALGORITHMS CUDA ARCHITECTURE
- 4. 4 RISE OF GPU COMPUTING GPU Developers 11X in 5 Years GTC Attendees 3X in 5 Years 2017 2017 511,0007,000 20122012 CUDA Downloads in 2016 1M+
- 5. 5 ANNOUNCING PROJECT HOLODECK Photorealistic models Interactive physics Collaboration Early access in September
- 6. 6 ERA OF MACHINE LEARNING “A Quest for Intelligence” — Fei-Fei Li “The Master Algorithm” — Pedro Domingos
- 7. 7 BIG BANG OF MODERN AI Auto Encoders GANLSTM IDSIA CNN on GPU Stanford & NVIDIA Large-scale DNN on GPU U Toronto AlexNet on GPU CaptioningNVIDIA BB8 Style TransferBRETTImageNet Google Photo Arterys FDA Approved AlphaGo Super Resolution Deep Voice Baidu DuLight NMT Superhuman ASR Reinforcement Learning Transfer Learning
- 8. 8 DEEP LEARNING FOR RAY TRACING
- 9. 9 IRAY WITH DEEP LEARNING
- 10. 10 $5B BIG BANG OF MODERN AI Udacity Students in AI Programs 100X in 2 Years NIPS, ICML, CVPR, ICLR Attendees 2X in 2 Years 2016 2017 20,00013,000 20152014 AI Startups Investments 9X in 4 Years $5B 20162012
- 11. 11 NVIDIA DEEP LEARNING SDK GPU AAS NVAIL INCEPTION INTERNET SERVICES ENTERPRISE HEALTHCARE GPU SYSTEMSFRAMEWORKS TESLA HGX-1 DGX-1 NVIDIA RESEARCH AUTOMOTIVE PLACES ROBOTS NVIDIA DEEP LEARNING SDK DRIVE PX JETSON TX POWERING THE AI REVOLUTION
- 12. 12 NVIDIA INCEPTION — 1,300 DEEP LEARNING STARTUPS HEALTHCARE BUSINESS INTELLIGENCE & VISUALIZATION DEVELOPMENT PLATFORMS RETAIL, ETAIL IOT & MANUFACTURING PLATFORMs & APIs DATA MANAGEMENT AEC FINANCIAL SECURITY, IVA CYBERAUTONOMOUS MACHINES
- 13. 13 SAP AI FOR THE ENTERPRISE First commercial AI offerings from SAP Brand Impact, Service Ticketing, Invoice-to-Record applications Powered by NVIDIA GPUs on DGX-1 and AWS
- 14. 14 MODEL COMPLEXITY IS EXPLODING 2016 — Baidu Deep Speech 22015 — Microsoft ResNet 2017 — Google NMT 105 ExaFLOPS 8.7 Billion Parameters 20 ExaFLOPS 300 Million Parameters 7 ExaFLOPS 60 Million Parameters
- 15. 15 ANNOUNCING TESLA V100 GIANT LEAP FOR AI & HPC VOLTA WITH NEW TENSOR CORE 21B xtors | TSMC 12nm FFN | 815mm2 5,120 CUDA cores 7.5 FP64 TFLOPS | 15 FP32 TFLOPS NEW 120 Tensor TFLOPS 20MB SM RF | 16MB Cache 16GB HBM2 @ 900 GB/s 300 GB/s NVLink
- 16. 16 NEW TENSOR CORE New CUDA TensorOp instructions & data formats 4x4 matrix processing array D[FP32] = A[FP16] * B[FP16] + C[FP32] Optimized for deep learning Activation Inputs Weights Inputs Output Results
- 17. 17 ANNOUNCING TESLA V100 GIANT LEAP FOR AI & HPC VOLTA WITH NEW TENSOR CORE Compared to Pascal 1.5X General-purpose FLOPS for HPC 12X Tensor FLOPS for DL training 6X Tensor FLOPS for DL inferencing
- 18. 18
- 19. 19 GALAXY
- 20. 20 DEEP LEARNING FOR STYLE TRANSFER
- 21. 21 ANNOUNCING NEW FRAMEWORK RELEASES FOR VOLTA Hours CNN Training (ResNet-50) Hours Multi-Node Training with NCCL 2.0 (ResNet-50) 0 5 10 15 20 25 64x V100 8x V100 8x P100 0 10 20 30 40 50 V100 P100 K80 Hours LSTM Training (Neural Machine Translation) 0 10 20 30 40 50 8x V100 8x P100 8x K80
- 22. 22 ANNOUNCING NVIDIA DGX-1 WITH TESLA V100 ESSENTIAL INSTRUMENT OF AI RESEARCH 960 Tensor TFLOPS | 8x Tesla V100 | NVLink Hybrid Cube From 8 days on TITAN X to 8 hours 400 servers in a box
- 23. 23 ANNOUNCING NVIDIA DGX-1 WITH TESLA V100 ESSENTIAL INSTRUMENT OF AI RESEARCH 960 Tensor TFLOPS | 8x Tesla V100 | NVLink Hybrid Cube From 8 days on TITAN X to 8 hours 400 servers in a box $149,000 Order today: nvidia.com/DGX-1
- 24. 24 ANNOUNCING NVIDIA DGX STATION PERSONAL DGX 480 Tensor TFLOPS | 4x Tesla V100 16GB NVLink Fully Connected | 3x DisplayPort 1500W | Water Cooled
- 25. 25 ANNOUNCING NVIDIA DGX STATION PERSONAL DGX 480 Tensor TFLOPS | 4x Tesla V100 16GB NVLink Fully Connected | 3x DisplayPort 1500W | Water Cooled $69,000 Order today: nvidia.com/DGX-Station
- 26. 26 ANNOUNCING HGX-1 WITH TESLA V100 VERSATILE GPU CLOUD COMPUTING 8x Tesla V100 with NVLINK Hybrid Cube 2C:8G | 2C:4G | 1C:2G Configurable NVIDIA Deep Learning, GRID graphics, CUDA HPC stacks
- 27. 27 ANNOUNCING TENSORRT FOR TENSORFLOW Graph optimizations for vertical and horizontal layer fusion | GPU-specific optimizations Import models from Caffe and TensorFlow Compiler for Deep Learning inferencing next layer Relu bias 1x1 conv 3x3 conv bias Relu 5x5 conv bias Relu 1x1 conv bias Relu Relu bias 1x1 conv Relu bias 1x1 conv max pool previous layer
- 28. 28 next layer 1x1 CBR 3x3 CBR 5x5 CBR 1x1 CBR 1x1 CBR 1x1 CBR max pool previous layer ANNOUNCING TENSORRT FOR TENSORFLOW Graph optimizations for vertical and horizontal layer fusion | GPU-specific optimizations Import models from Caffe and TensorFlow Compiler for Deep Learning Inferencing next layer bias bias bias bias max pool previous layer Relu Relu Relu Relu 1x1 conv 3x3 conv 5x5 conv 1x1 conv Relu bias 1x1 conv Relu bias 1x1 conv
- 29. 29 next layer 3x3 CBR 5x5 CBR 1x1 CBR 1x1 CBR max pool previous layer concat 1x1 CBR 3x3 CBR 5x5 CBR 1x1 CBR max pool previous layer 1x1 CBR 1x1 CBR ANNOUNCING TENSORRT FOR TENSORFLOW Graph optimizations for vertical and horizontal layer fusion | GPU-specific optimizations Import models from Caffe and TensorFlow Compiler for Deep Learning Inferencing
- 30. 30 next layer 3x3 CBR 5x5 CBR 1x1 CBR max pool previous layer 1x1 CBR ANNOUNCING TENSORRT FOR TENSORFLOW Graph optimizations for vertical and horizontal layer fusion | GPU-specific optimizations Import models from Caffe and TensorFlow Compiler for Deep Learning Inferencing
- 31. 31 next layer 3x3 CBR 5x5 CBR 1x1 CBR max pool previous layer 1x1 CBR ANNOUNCING TENSORRT FOR TENSORFLOW Graph optimizations for vertical and horizontal layer fusion | GPU-specific optimizations Import models from Caffe and TensorFlow Compiler for Deep Learning Inferencing
- 32. 32 ANNOUNCING TESLA V100 FOR HYPERSCALE INFERENCE 15-25X INFERENCE SPEED-UP VS SKYLAKE 150W | FHHL PCIE
- 33. 33 THE CASE FOR GPU ACCELERATED DATACENTERS Tesla V100 Reduce 15X500 Nodes CPU Servers 33 Nodes GPU Accelerated Server 300K inf/s Datacenter @300 inf/s > 1K CPUs 1K CPUs > 500 Nodes @$3K @500W = $1.5M = 250KW
- 34. 34 NVIDIA DEEP LEARNING STACK DEEP LEARNING FRAMEWORKS DEEP LEARNING LIBRARIES NVIDIA cuDNN, NCCL, cuBLAS, TensorRT CUDA DRIVER OPERATING SYSTEM GPU SYSTEM
- 35. 35 Registry of Containers, Datasets, and Pre-trained models NVIDIA GPU CLOUD CSPs ANNOUNCING NVIDIA GPU CLOUD Containerized in NVDocker | Optimization across the full stack Always up-to-date | Fully tested and maintained by NVIDIA | Beta in July GPU-accelerated Cloud Platform Optimized for Deep Learning
- 36. 36 POWER OF GPU COMPUTING 0 8 16 24 32 40 AMBER Performance (ns/day) P100 2016 K80 2015 K40 2014 K20 2013 AMBER 12 CUDA 4 AMBER 14 CUDA 5 AMBER 14 CUDA 6 AMBER 16 CUDA 8 0 2400 4800 7200 9600 12000 GoogleNet Performance (i/s) cuDNN 2 CUDA 6 cuDNN 4 CUDA 7 cuDNN 6 CUDA 8 NCCL 1.6 cuDNN 7 CUDA 9 NCCL 2 8x K80 2014 8x Maxwell 2015 DGX-1 2016 DGX-1V 2017
- 37. 37 AI REVOLUTIONIZING TRANSPORTATION Domino’s: 1M Pizzas Delivered per Day 800M Parking Spots for 250M Cars in U.S. 280B Miles per Year
- 38. 38 NVIDIA DRIVE — AI CAR PLATFORM Computer Vision Libraries OS Perception AI CUDA, cuDNN, TensorRT Localization Path Planning 1 TOPS 10 TOPS 100 TOPS DRIVE PX 2 Parker Level 2/3 DRIVE PX Xavier Level 4/5
- 40. 40 ANNOUNCING TOYOTA SELECTS NVIDIA DRIVE PX FOR AUTONOMOUS VEHICLES
- 41. 41 AI PROCESSOR FOR AUTONOMOUS MACHINES XAVIER 30 TOPS DL 30W Custom ARM64 CPU 512 Core Volta GPU 10 TOPS DL Accelerator General Purpose Architectures Domain Specific Accelerators Energy Efficiency CPU FPGA CUDA GPU DLA Pascal Volta
- 42. 42 AI PROCESSOR FOR AUTONOMOUS MACHINES XAVIER 30 TOPS DL 30W Custom ARM64 CPU 512 Core Volta GPU 10 TOPS DL Accelerator General Purpose Architectures Domain Specific Accelerators Energy Efficiency CPU CUDA GPU DLA Volta +
- 43. 43 ANNOUNCING XAVIER DLA NOW OPEN SOURCE Early Access July | General Release September Command Interface Tensor Execution Micro-controller Memory Interface Input DMA (Activations and Weights) Unified 512KB Input Buffer Activations and Weights Sparse Weight Decompression Native Winograd Input Transform MAC Array 2048 Int8 or 1024 Int16 or 1024 FP16 Output Accumulators Output Postprocess or (Activation Function, Pooling etc.) Output DMA
- 45. 45 ROBOTS Credit: Yevgen Chebotar, Karol Hausman, Marvin Zhang, Sergey Levine
- 46. 46 ANNOUNCING ISAAC ROBOT SIMULATOR NVIDIA GPU Computer ISAAC Robot Simulator OpenAI GYM Robot & Environment Definition Virtual Jetson
- 47. 47 ANNOUNCING ISAAC ROBOT SIMULATOR NVIDIA GPU Computer ISAAC Robot Simulator OpenAI GYM Robot & Environment Definition Virtual Jetson LEARN & PLAN SEE, HEAR, TOUCH ACT
- 48. 48 NVIDIA POWERING THE AI REVOLUTION NVIDIA GPU Cloud ISAAC NVIDIA GPU in Every Cloud Xavier DLA Open Source DGX-1 and DGX StationTesla V100 TensorRT Tensor Core NVIDIA GPU CLOUD CSPs