Harnessing the virtual realm for successful real world artificial intelligence

1. Harnessing the virtual realm for successful real world artificial intelligence Alison B. Lowndes AI DevRel | NVIDIA

2. ANNOUNCING NVIDIA MAXINE Early Access at developer.nvidia.com/maxine

3. 4 https://arxiv.org/pdf/1912.04958.pdf

4. 5 5 25 YEARS OF ACCELERATED COMPUTING X-FACTOR SPEED UP FULL STACK ONE ARCHITECTURE SYSTEMS GPU CPU

5. 6 6 25 YEARS OF ACCELERATED COMPUTING X-FACTOR SPEED UP FULL STACK DATA-CENTER SCALE GPU CPU DPU ONE ARCHITECTURE

7. 8 NVIDIA SELENE Featuring NVIDIA DGX A100 640GB 4,480 A100 GPUs 560 DGX A100 system 850 Mellanox 200G HDR switches 14 PB of high-performance storage 2.8 EFLOPS of AI peak performance 63 PFLOPS HPL @ 24GF/W https://blogs.nvidia.com/blog/2020/12/18/nvidia-selene-busy/

8. SINGLE A100 WITH MIG RUNS ALL MLPERF TESTS… AT THE SAME TIME Delivers 98% of Performance of a Single MIG Instance Running Alone MLPerf v1.0 Inference Closed; Per-accelerator performance derived from the best MLPerf results for respective submissions using reported accelerator count in Data Center Offline and Server. 3D U- Net 99%, ResNet-50, SSD-Large, DLRM 99%, RNN-T, BERT 99%: 1.0-26. MLPerf name and logo are trademarks. See www.mlperf.org for more information. ResNet-50 v1.5 3D-UNet 99% RNN-T BERT-Large SSD-Large DLRM ResNet-50 v1.5 Single A100 with 7 MIG Instances Enabled 98% Performance vs. MIG instance running alone

9. TODAY’S AI DATA CENTER 50 DGX-1 systems for AI training 600 CPU systems for AI inference $11M 25 racks 630 kW

10. 5 DGX A100 systems for AI training and inference $1M 1 rack 28 kW 1/10th COST 1/20th POWER $1M 28 kW DGX A100 DATA CENTER

11. 12 NVIDIA CUDA-X AI ECOSYSTEM

12. 13 13 EXPANDING NGC NEW CONTAINERS FOR A100 & ARM Now NGC-READY SYSTEMS FOR A100 Starting Q3 NGC Private Registry NGC Container Environment Modules Higher HPC app performance w/ NVTAGS NEW FEATURES Now Multi-arch support for x86, Arm and Power Learn More – ngc.nvidia.com | NGC Private Registry | NVTAGS | NGC Container Environment Modules HPC Simulation & Visualization AI Frameworks (A100) Chroma AutoDock 4 VMD ** * Available week of June 22 ** Available starting with v20.06 * * *

13. 14 ENABLING ENTERPRISE TRANSFORMATION WITH 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

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18. 22 Treeswift.com

19. 23 BUILDING AN AI PRODUCT SENSORS PERCEIVE REASON PLAN DATA DATA ANALYTICS MACHINE LEARNING AI MODEL VALIDATION ACTUATORS AI MODEL

20. INGESTION STORAGE PROCESSING SERVING BIG DATA PIPELINE Ingredients: • Lots of data • Lots of compute • Software tools • Time and patience Method: 1. Collect raw, massive sets of data. 2. Put the data in a Data Lake. 3. Grab the data that you need and sort through. 4. Find patterns in the data. 5. Solve the problem. 1. Obtaining and importing data 2. Organizing & storing data for future use 3. Manipulating and analyzing the data 4. Operationalizing the solution

21. 25 HARNESSING AI Step I: Build data fabric for your organization Step II: Define your objective Step III: Hire the right talent Step IV: Identify key processes to augment with AI Step V: Create a sandbox lab environment Step VI: Operationalize successful pilots Step VII: Scale up for enterprise-wide adoption Step VIII: Drive cultural change

22. 26 World Sense See, Understand Automation AI Program Computer ARTIFICIAL INTELLIGENCE IS DOMAIN SPECIFIC Self-Driving

23. 27 World Sense See, Understand Automation AI Program Computer AI Program Computer ARTIFICIAL INTELLIGENCE IS DOMAIN SPECIFIC Self-Driving Manufacturing

24. 28 World Sense See, Understand Automation AI Program Computer AI Program Computer AI Program Computer ARTIFICIAL INTELLIGENCE IS DOMAIN SPECIFIC Self-Driving Manufacturing Radiology

25. 29 Image “Volvo XC90” Image source: “Unsupervised Learning of Hierarchical Representations with Convolutional Deep Belief Networks” ICML 2009 & Comm. ACM 2011. Honglak Lee, Roger Grosse, Rajesh Ranganath, and Andrew Ng. CONVOLUTIONAL NEURAL NETWORKS

26. 30 FULLY CONVOLUTIONAL NETWORK https://github.com/NVIDIA/MinkowskiEngine

27. RT DENOISING VIDEO TO 3D CHARACTER LOCOMOTION CHARACTER CONCEPTING AUDIO TO FACIAL ANIMATION PHYSICS SIMULATION Clothing models from UC Berkeley Garment Library THE MAGIC OF DEEP LEARNING

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29. 33 ADVANCED TOOLS AND TECHNOLOGIES Foundational Platform Components

30. 34 LEARN MORE ABOUT OMNIVERSE DEVELOPER TOOLS EARLY ACCESS CLOSED BETA TUTORIAL COLLECTION OPEN BETA DOWNLOAD WEBSITE

31. ADD ANYMAL VIDEO (RL TRAINING IN SIM)

32. NVIDIA & ETHZ RSL

33. 37 https://arxiv.org/pdf/1810.05762.pdf

34. 38 END-TO-END GPU RL Grasping Robot Use Case 38

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37. THE IMPORTANCE OF SYNTHETIC DATA https://blogs.nvidia.com/blog/2021/06/08/what-is-synthetic-data/

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40. 45 Retail & supply chain GTC talk S31538 with Kinetic Vision

41. 46 PURPOSE BUILT PRE-TRAINED NETWORKS 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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43. ANNOUNCING JARVIS OPEN BETA Integrated AI Skills with Pre-Trained Models Fully Customizable Application Pipeline Human Voice with Neural TTS Superhuman NLU with Megatron-BERT <300 ms Latency | 7X Throughput | 1/3rd Cost Sign Up at developer.nvidia.com/nvidia-jarvis State-of-the-Art Conversational AI

44. 49 LEARN MORE Conversational AI Developer Overview NVIDIA Jarvis Product Page Conversational AI Demo Videos "Misty" | "Mark" | In-car Conversational AI Explainer Videos YouTube Playlist Jarvis Intro Blog Conversational AI Corp Blogs Intro to building Conversational AI Apps for Enterprise (Webinar)

45. RECOMMENDERS — THE PERSONALIZATION ENGINE OF THE INTERNET DIGITAL CONTENT 2.7 Billion Monthly Active Users E-COMMERCE 2 Billion Digital Shoppers SOCIAL MEDIA 3.8 Billion Active Users DIGITAL ADVERTISING 4.7 Billion Internet Users Item Candidate Generation O(102) Ranking User Embedding User Items Recommende d Items Item Embedding O(10) O(109)

46. 51 TRANSFER LEARNING TOOLKIT (TLT) Zero Code Approach| Domain Adaptability Purpose-Built Pretrained Models Quantization Aware Training with TLT Automatic Mixed Precision with TLT 2X Inference Speedup 1.5X Training time Speedup 10X Overall Development Time Speedup SmartCow is building turnkey AIoT solutions to optimize turnaround time at ports and dry docks. “By using TLT, we were able to reduce the training iterations by 9x and reduce the data collection and labeling effort by 5x which significantly reduces our training cost by 2x” “Using NVIDIA’S TLT made training a real time car detector and license plate detector easy. It eliminated our need to build models from the ground up, resulting in faster development of models and ability to explore options” Highly Accurate

47. 52 www.rapids.ai

48. 53 First and only workstation with 4-way NVIDIA A100 GPUs, NVLink, and MIG Four A100 Tensor Core GPUs, 320 GB total HBM2E Multi-Instance GPU (MIG) for up to 28 GPU instances in a single DGX Station A100 3rd generation NVLink 200 GB/s bi-directional bandwidth between any GPU pair, almost 3x compared to PCIe Gen4 New maintenance-free refrigerant cooling system DGX STATION A100 320G Workgroup Appliance for the Age of AI CPU and Memory 64-core AMD® EPYC® CPU, PCIe Gen4 512 GB system memory Internal Storage 1.92 TB NVME M.2 SSD for OS 7.68TB NVME U.2 SSD for data cache Connectivity 2x 10GbE (RJ45) 4x Mini DisplayPort for display out Remote management 1GbE LAN port (RJ45)

49. 54 NEW DGX A100 640GB SYSTEM Speedups Normalized to Number of GPUs | Comparisons to A100 40GB | Measurements performed DGX A100 servers . AI Training: DLRM (Huge CTR) | DGX A100: 16x A100 40GB vs 8x A100 80GB | speedup = 1.4X. Speedup normalized to number of GPUs = 2.8X. AI Inference: RNN-T (MLPerf 0.7 Single stream latency) | DGX A100: A100 40GB vs A100 80GB on 1MIG@10GB when configured for 7MIGs | Data Analytics: big data benchmark with RAPIDS(0.16), BlazingSQL(0.16), DASK(2.2.0) | 30 analytical retail queries, ETL, ML, NLP | 96x A100 40GB vs 48x A100 80GB | HPC: Quantum Espresso - CNT10POR8 40x A100 40GB vs 24x A100 80GB | Speedup normalized to number of GPUs = 1.8X 640 GB of GPU memory per system to increase model accuracy and reduce-time-to-solution Up to 3X higher throughput for large-scale workloads Double the GPU memory for MIG for more flexible AI development, analytics, and inference Available individually, or part of DGX SuperPOD Solution for Enterprise Upgrade option for current DGX A100 customers For the Largest AI Workloads

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51. 56 NVIDIA GPUs IN THE CLOUD AVAILABLE ON-DEMAND FROM THE TOP CLOUD SERVICE PROVIDERS • Immediate access to NVIDIA GPU infrastructure for data science in the cloud • Wide variety of deployment and management options using containers, Kubernetes, Kubeflow, support for cloud native services, and more

52. 57 RICH CONTENT PORTFOLIO Fundamentals and advanced hands-on training in key technologies and application domains AI for Digital Content Creation Deep Learning Fundamentals AI for Healthcare AI 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

53. 58 PROFESSIONAL SERVICES NVIDIA works with a large network of service delivery partners to provide services on NVIDIA- accelerated platforms. AI Service Delivery Partners Contact us directly to start a dialogue about your specific needs: professionalservices@nvidia.com Jay/Pat: Several proposed claims here we need to vet with Marc H.

54. NVIDIA INCEPTION ACCELERATING 6K STARTUPS WORLDWIDE EXPERTISE NVIDIA Deep Learning Institute Training in AI, accelerated computing, and accelerated data science TECHNOLOGY ASSISTANCE Developer resources, preferred pricing on on-prem GPUs, and cloud credits through our global partners GO-TO-MARKET SUPPORT Networking events and exposure opportunities through NVIDIA VENTURE CAPITAL FUNDING & ECOSYSTEM NVIDIA Inception GPU Ventures Investing in breakthrough startups and facilitating engagements with the VC community www.nvidia.com/inception

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56. THANK YOU alowndes@nvidia.com

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