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Fuelling the AI Revolution with Gaming
This document discusses NVIDIA's work in accelerating AI through its GPUs and AI platforms. It highlights several key projects including Project SOL which showcases real-time ray tracing in Minecraft using RTX GPUs, Omniverse which is a 3D design collaboration platform, and NASA Mars lander visualizations. It also discusses NVIDIA's 25 years of accelerated computing, enabling enterprise AI through frameworks and solutions, and its Selene supercomputer built using DGX A100 systems. The document provides information on the powerful NVIDIA A100 GPU and its CUDA ecosystem. It outlines how NVIDIA is breaking AI performance records and developing tools like Jarvis for conversational AI. In summary, the document show
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Fuelling the AI Revolution with Gaming
- 1. ALISON B LOWNDES AIDevRel | EMEA @alisonblowndes August 2020
- 2. 2 NVIDIA FUELLING THE AI REVOLUTION WITHGAMING PROJECT SOL: A Showcase for the Power of NVIDIA RTX MINECRAFT RTX: Real-time Ray Tracing in the World’s Most Popular Game OMNIVERSE: A Powerful Collaboration Platform for 3D Design NASA MARS LANDER: Visualizing NASA’s Supercomputer Simulations
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- 6. 66 25 YEARS OFACCELERATED COMPUTING X-FACTOR SPEED UP FULL STACK ONE ARCHITECTURESYSTEMS GPU CPU
- 7. 7 ENABLING ENTERPRISE TRANSFORMATIONWITH AI End to End Application Frameworks Desktop Development Data Center Solutions Accelerated Edge Supercomputers GPU-Accelerated Cloud Jarvis Merlin Metropolis Clara Isaac Drive Aerial Conversational AI Recommender Systems Smart Cities Healthcare Robotics Autonomous Vehicles Telecom
- 8. 8 Original NVIDIA Campus NVIDIAEndeavor (2017) New Selene Datacenter (2020) NVIDIA Voyager (2020)
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- 10. 10 NVIDIA’s DGX SuperPODDeployment THE SELENE SUPERCOMPUTER 27.6 PetaFLOPS HPL 20.5 GigaFLOPS/watt 1+ ExaFLOPS AI Built from the NVIDIA DGX SuperPOD Reference Architecture 2nd generation SuperPOD Reference Architecture NVIDIA DGX A100 and NVIDIA Mellanox InfiniBand network The blueprint for AI power and scale, infused with the knowhow from NVIDIA’s decade plus of AI experience Configuration: 2,240 NVIDIA A100 Tensor Core GPUs 280 NVIDIA DGX A100 systems 494 Mellanox 200G HDR InfiniBand switches 7 PB of all-flash storage One of the fastest and most efficient supercomputers on the planet — built in under one month nvidia.com/en-us/data-center/dgx-a100/
- 11. 11 NVIDIA A100 Greatest GenerationalLeap – 20X Volta 54B XTOR | 826mm2 | TSMC 7N | 40GB Samsung HBM2 | 600 GB/s NVLink Peak Vs Volta FP32 Training 312 TFLOPS 20X INT8 Inference 1,248 TOPS 20X FP64 HPC 19.5 TFLOPS 2.5X Multi-instance GPU (MIG) 7X GPUs
- 12. 12 NVIDIA CUDA-X AIECOSYSTEM
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- 14. 14 NVIDIA BREAKS ALL16 RECORDS IN AI PERFORMANCE Among Commercially Available Systems Benchmark Max Scale Records (DGX SuperPOD) Per Accelerator Records (NVIDIA A100) Recommendation (DLRM) 3.33 Min 0.44 Hrs NLP (BERT) 0.81 Min 6.53 Hrs Reinforcement Learning (MiniGo) 17.07 Min 39.96 Hrs Translation (Non-recurrent) Transformer 0.62 Min 1.05 Hrs Translation (Recurrent) GNMT 0.71 Min 1.04 Hrs Object Detection (Heavy Weight) Mask R-CNN 10.46 Min 10.95 Hrs Object Detection (Light Weight) SSD 0.82 Min 1.36 Hrs Image Classification (ResNet-50 v1.5) 0.76 Min 5.30 Hrs Per Chip Performance arrived at by comparing performance at the same scale when possible. Per Accelerator comparison using reported performance for MLPerf 0.7 NVIDIA A100 (8 A100s). MLPerf ID DLRM: 0.7-17, ResNet50 v1.5: 0.7- 18, 0.7-15 BERT, GNMT, Mask R-CNN, SSD, Transformer: 07-19, MiniGo: 0.7-20. Max Scale: All results from MLPerf v0.7 using NVIDIA DGX A100 (8xA100s). MLPerf ID Max Scale: ResNet50 v1.5: 0.7-37, Mask R-CNN: 0.7-28, SSD: 0.7-33, GNMT: 0.7-34, Transformer: 0.7-30, MiniGo: 0.7-36, BERT: 0.7-38, DLRM: 0.7-17. MLPerf name and logo are trademarks. See www.mlperf.org for more information.
- 15. 15 NVIDIA JARVIS Fully AcceleratedFramework for Multimodal Conversational AI Services End-to-End Multimodal Conversational AI Services Pre-trained SOTA models-100,000 Hours of DGX Retrain with NeMo Interactive Response – 215ms on V100 versus 25sec on CPU Deploy Services with One Line of Code RETRAIN video audio Multi-Speaker TranscriptionNVIDIA GPU CLOUD NVIDIA AI TOOLKIT Transfer Learning NeMo Service Maker TRITON INFERENCE SERVER Dialog Manager Chatbot Multi- Speaker Transcription Look to Talk Gesture Recognition Speech Vision NLU Jarvis Sign up for Early Access https://developer.nvidia.com/nvidia-jarvis
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- 17. 17 MEGATRON (August 2019) https://devblogs.nvidia.com/training- bert-with-gpus/ https://github.com/nvidia/megatron-lm BERT-Large trainedin 53 minutes on 92x DGX-2H systems with model parallelism 2 millisecond inference with TRT/T4
- 18. 18 ResNet-50 GPT BERT-Base BERT-Large GPT-2 MegatronLM T-NLG GPT-3 0 12 3 4 5 6 7 8 9 10 Model Size Comparison HOW BIG? Perspective https://github.com/openai/gpt-3
- 19. 19 TODAY’S AI DATA CENTRE 50DGX-1 systems for AI training 600 CPU systems for AI inference $11M 25 racks 630 kW
- 20. 20 5 DGX A100systems for AI training and inference $1M 1 rack 28 kW 1/10th COST 1/20th POWER $1M 28 kW DGX A100 DATA CENTER
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- 24. Currently get 1day extreme rain forecast which makes her crop vulnerable. What if we could improve forecasting to a 5 day forecast? 25% destroyed 95% harvested
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- 26. 26 AI REVOLUTION AI inSpace is More Critical Than Ever Earth Observations Urban Planning Solar PredictionsPlanetary Defense Planetary Exploration Manufacturing LunarMission Control $400B INDUSTRY — Increase operational efficiency and safety across every challenge using AI
- 27. 27 BUILDING AN AIMODEL AI MODELFEATURES DEPLOYMENTDATA DATA ANALYTICS MACHINE LEARNING MODEL VALIDATION NEW DATA
- 28. 28 BUILDING AN AIPRODUCT SENSORS PERCEIVE REASON PLAN DATA DATA ANALYTICS MACHINE LEARNING AI MODEL VALIDATION ACTUATORSAI MODEL
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- 30. 30 GoodAI talk byKenneth Stanley (OpenAI) https://youtu.be/AwlLrugFcOc “it is biblical in its power” https://arxiv.org/pdf/2003. 08536.pdf
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- 32. 32 NVIDIA ROBOTICS RESEARCH LABSEATTLE Drive breakthrough robotics research to enable the next-generation of robots that safely work alongside humans, transforming industries such as manufacturing, logistics, healthcare, and more
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- 36. 36 ISAAC PLATFORM FORROBOTICS Nvidia's multi-tool for robotics DESIGN JETSON XAVIER SIMULATE TRAIN DEPLOY
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- 39. 39 Virtual Production AECIsaacSimDriveSim OMNIVERSE DGX EGX RTX EXPERIENCES BUILT ON OMNIVERSE
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- 42. 42 JETSON NANO $99 NVIDIACUDA-X AI Computer CUDA-X acceleration stack | High-resolution sensor support | Runs all CUDA-X AI models
- 43. 43 NVIDIA JETSON Software-Defined AIPlatform Sensor Fusion & Compute Performance Expertise, Time to Market JETSON COMPUTER ECOSYSTEMSOFTWARE DEFINED Jetpack SDK ∙ CUDA ∙ TensorRT ∙ TensorFlow ∙ ONMX ∙ ROS Artificial Intelligence Computer Vision Accelerated Computing Multimedia Gesture rec Obj detectPath planningDepth est Pose est Speech rec SDK, Design Tools, Libs, GEMs Act Sense Reason AI at the Edge
- 44. 44 DEEP LEARNING INSTITUTE Training Labs Nanodegrees nvidia.com/DLI TWO DAYS TO A DEMO Create your first demo today developer.nvidia.com/ embedded/twodaystoademo JETSON DEVELOPER KIT AGX Xavier Developer Kit $699 Xavier NX software patch developer.nvidia.com/ buy-jetson GTC Largest event for GPU developers gputechconf.com JETSON - START NOW
- 45. 45 CLOUD NATIVE ONJETSON Easier and Faster Deployment - Eliminates complex, time-consuming builds and installs Agile and Easier Development - Update specific modules not the whole system Scalability - Push the right container to the right platform Portable - Deploy across various environments, from test to production with minimal changes Consistency Across Portfolio - Update once and push broadly Containers & Micro Services
- 46. 46 GET STARTED WITHNGC Deploy containers: ngc.nvidia.com Learn more about NGC offering: nvidia.com/ngc Technical information: developer.nvidia.com Explore the NGC Registry for DL, ML & HPC
- 47. 47 PURPOSE BUILT PRE-TRAINEDNETWORKS Number of classes: 3 Dataset: 750k frames Accuracy: 84% Number of classes: 4 Dataset: 150k frames Accuracy: 84% Number of classes: 12 Dataset: 56k frames Accuracy: 88% Number of classes: 20 Dataset: 60k Frames Accuracy: 92% Number of Classes: 4 Dataset: 160k frames Accuracy: 84% Number of classes: 1 Dataset: 600k images Accuracy: 95% PeopleNet TrafficCamNet VehicleTypeNet DashCamNet FaceDetect-IR VehicleMakeNet Highly Accurate | Re-Trainable | Out of Box Deployment
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- 49. 49 A massive DeepLearning challenge
- 50. 50 BRIDGING THE REALITYGAP https://arxiv.org/pdf/1804.06516.pdf NVR+University of Toronto “leveraging the power of synthetic data improves upon results obtained using real data alone”
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- 52. 52 Oxford Nanopore Sequence Viral Genomein 7Hrs Plotly, NVIDIA Real-Time Infection Rate Analysis ORNL, Scripps Screen 2B Drug Compounds in 1 Day vs 1 Year Structura, NIH, UT Austin CryoSPARC 1st 3D Structure of Virus Spike Protein NIH, NVIDIA AI COVID-19 Classification Kiwibot Robot Medical Supply Delivery Whiteboard Coordinator AI Elevated Body Temp Screening System NVIDIA SCIENTIFIC COMPUTING FIGHTS COVID-19 Data Analytics Simulation & Visualization AI Edge
- 53. 53 REVOLUTIONIZING GENOMIC TESTING Genome sequencingis the process of identifying the DNA sequence of anything biological―animal, food, water, air, plants. This ‘decoding’ is an important step toward better scientific understanding. Oxford Nanopore Technologies is democratizing genome sequencing with MinIT, a portable rapid analysis and device control accessory to run the MinION DNA/RNA sequencer. Powered by the NVIDIA AGX System for high-throughput, real-time analysis, MinIT will enable DNA/RNA sequencing by anyone, anywhere.
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- 55. 55 RICH CONTENT PORTFOLIO Fundamentals and advanced hands-ontraining in key technologies and application domains AI for Digital Content Creation Deep Learning Fundamentals AI for HealthcareAI for Autonomous Vehicles AI for Intelligent Video Analytics Accelerated Computing Fundamentals AI for Robotics AI for Predictive Maintenance Accelerated Data Science Fundamentals Intro to AI in the Data Center AI for Anomaly Detection AI for Industrial Inspection nvidia.com/DLI
- 56. 56 NVIDIA INCEPTION PROGRAM Accelerates AI startupswith a boost of GPU tools, tech and deep learning expertise Startup Qualifications Driving advances in the field of AI Business plan Incorporated Web presence Technology DL startup kit* Pascal Titan X Deep Learning Institute (DLI) credit Connect with a DL tech expert DGX-1 ISV discount* Software release notification Live webinar and office hours *By application Marketing Inclusion in NVIDIA marketing efforts GPU Technology Conference (GTC) discount Emerging Company Summit (ECS) participation+ Marketing kit One-page story template eBook template Inception web badge and banners Social promotion request form Event opportunities list Promotion at industry events GPU ventures+ +By invitation www.nvidia.com/inception
- 57. INNOVATION NEVER SLEEPS October5 - 9, 2020 Register at www.nvidia.com/GTC EXPERIENCE WHAT’S NEXT AT NVIDIA GTC ONLINE This autumn’s GTC will run continuously for five days, across seven time zones, showcasing the latest breakthroughs in AI, HPC, graphics, networking, and more. Attend live events in the time zone that works best for you or browse an extensive catalog of on-demand content showcasing innovative uses of GPU technology. Use code: NVALOWNDES
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