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Dell NVIDIA AI Roadshow - South Western Ontario

- Artificial intelligence (AI) is mimicking human intelligence through machine algorithms like those used for chess and facial recognition. Machine learning (ML) is a subset of AI that uses algorithms to parse data, learn from data, and make predictions. Deep learning (DL) uses artificial neural networks to develop relationships in data and is used for applications like driverless cars and cybersecurity. - AI technologies are enabling digital transformation and require infrastructure like edge computing, GPUs, FPGAs, deep learning accelerators, and specialized hardware to power applications of AI, ML, and DL. Dell Technologies provides platforms and solutions to accelerate AI workloads and support digital transformation.

Data & Analytics◦
Related topics:
Digital Transformation•Enterprise Architecture• Deep Learning•
Digital Transformation Through Artificial Intelligence Machine Learning, Deep Learning, AI on the Edge, AI Infrastructure Bill Wong Artificial Intelligence and Data Analytics Practice Leader Dell Technologies Dell / NVIDIA AI Roadshow
1950 1960 1970 1980 1990 2000 2010 2020 Cost of Compute Amount of Data Artificial Intelligence Machine Learning Deep Learning - Artificial Intelligence (AI) is human intelligence mimicked by machine algorithms, examples: Chess, Go, Facial Recognition - Machine Learning (ML) is a subset of AI algorithms to parse data, learn from data, and then make a determination or prediction, example: Spam Detection, Preventative Maintenance - Deep Learning (DL) a subset of machine learning algorithms that leverage artificial neural networks to develop relationships among the data, examples: Driverless Cars, Cyber-Security Accelerators Algorithms Big Data Traditional Programming Machine Learning AI TECHNOLOGY ENABLERS
WHY AI AT THE EDGE MATTERS
4 AI AT THE EDGE TODAY
5
6 CORTI.AI
High volume, continuous "data in motion" from multiple sensors Store, blend and manage more time-series data Use of multiple analytics techniques Integration with operational systems More data More complexity Bidirectional communication and control of endpoints More automation Distributed analytics (edge) IOT ANALYTICS CHALLENGES
Consumption Tier * * Key components enabled for end-to-end self-service Sources ERP1 ERP2 CRM Chat Logs Machine Logs/IoT Social Media Services Tier * Execution Tier * Complex Event Processing Rules Based Decisions Data Quality Processing Microservices APIs Streaming Analytics Data Warehouse OLAP In-Memory R/T Cache NoSQL Metadata, Security & Governance Pub-Sub Data Movement* Shared Data Platform * IT Managed/ Enterprise Business Managed Workspaces MDMStreaming Batch Micro-Batch Mobile Notifications 5 Advanced Analytics • Predictive • Prescriptive • Machine Learning Applications • Data Driven • Mobile • Analytical Business Intelligence • Reporting • Ad Hoc • Discovery 8 © Copyright 2017 Dell Inc. Supporting the Digital Transformation at Dell Technologies Raw Processed Integrated Business Challenges • Business acquisition/merger introduces: • Multiple systems of record • Fragmented view of the customer • Lack of timely information • Non-optimized infrastructure Results • One data platform that delivers the right data, to the right place at the right time to address business problems • 360 degree view of the customer • Enabled to deliver real-time customer segmentation and predictive analytics • Dell IT Proven Program – IT Infrastructure built using Dell Servers and EMC Storage solutions Data Warehouse DATA LAKE FOR ADVANCED ANALYTICS
NVIDIA EGX AI PLATFORM Higher Performance Edge Computing Platform
Surveillance validation lab locations • Durham, NC • Limerick, Ireland • Bangalore, India Lab advantages: • Global, dedicated Surveillance labs • Dedicated Surveillance Engineering team • More than 50 years combined test experience • Test-to-fail philosophy reduces risk End-to-end validated solutions built on proven technology that will speed up your time to adoption THE DELL EMC DIFFERENCE
DELL TECHNOLOGIES IOT SOLUTION | SURVEILLANCE Simplified surveillance for a complex world Integrated from camera to cloud Users Field Sensors
• Agent Video Intelligence: automates video analysis to detect and alert for events of interest, expedite search in recorded video and extract statistical data from the footage captured by surveillance cameras. • BriefCam: provider of Video Synopsis® and Deep Learning solutions for rapid video review and search, real-time alerting and quantitative video insights. Transforms raw video into actionable intelligence. • Deep Vision AI: highly customizable “deep learning” platform that provides, facial and object recognition, as well as behavioral analysis. Used in more specialized situations where customer has unique models such as for uniform detection, sanitary processes, foreign objects in food processing as a few examples. DELL VISION ANALYTICS PARTNERS - SAMPLE
- The Toronto-Waterloo Corridor is the second largest start-up and technology hub in North America, second only to Silicon Valley. - The Corridor is home to 5,200 start-ups, 15,000 tech companies and 200,000 tech workers. - The region’s strongest subsectors were FinTech, AI, manufacturing and robotics, and life sciences and health. - Most Patent Intensive region in Canada – 11x the national average - The Technology Triangle, which consists of the cities of Cambridge, Kitchener, and Waterloo, has become a preferred destination for technology start-ups, incubators, accelerators, and young inventors. - High technology in the “Triangle” is supported by three acclaimed universities, a college, and more than 150 research organizations. - World’s largest concentration of mathematical and computer science students - More than 22% of all spin-off Canadian information technology companies have gotten their start in University of Waterloo incubator programs. AI IN WATERLOO
Opportunity Material handing automation (which can account for up to 70% of the final cost of good) offers the potential for significant performance improvements and allows facility operators to focus more resources on core, value-added operations. Solution OTTO, the self-driving vehicle, is designed exclusively for material transport in industrial environments such as manufacturing facilities and warehouses. Benefits OTTO Motors is democratizing robotics technology for their customers, and making it possible for companies of all sizes to adopt self-driving vehicles in their work environments. “We have, over the last few years, evolved significantly into an autonomous systems and software company, and with that, really taken the promise of data collection and data analysis to heart. I’d say that it’s something we’ve rapidly seen the benefit from, and are rapidly encouraging the adoption of.” - OTTO CTO - DRIVEN TO BE DISRUPTIVE
Expectations Plateau of Productivity Peak of Slope of EnlightenmentInnovationTrigger Trough of Disillusionment Inflated Expectations “Narrow" AI is becoming better than humans at defined tasks. "General" AI is still a long way off.” Time Plateau will be reached less than 2 years 2 to 5 years 5 to 10 years more than 10 years Deep Learning Infrastructure Transformation Autonomous Vehicles “AI, one of the most disruptive classes of technologies, will become more widely available due to cloud computing, open source and the “maker” (developers, data scientists and AI architects) community. While early adopters will benefit from continued evolution of the technology, the notable change will be its availability to the masses. As of July 2019 AI PaaS Artificial General Intelligence Machine Learning NLP FPGA Accelerators GPU Accelerators DNN ASICs Quantum Computing Neuromorphic Hardware Computer Vision Speech Recognition HYPE CYCLE FOR ARTIFICIAL INTELLIGENCE
TOP 10 TYPES OF HARDWARE FOR AI DELIVERY* 1. Processors (CPU, GPU, FPGA, ASIC) 2. HPC / Supercomputer Infrastructure 3. Communication Network 4. Personal Devices 5. Connected Home Devices 6. AR / VR Head-Mounted Displays (HMD) 7. Drones 8. Robotics 9. Automotive 10.Sensors and Application Components (audio, camera, LiDAR, etc.) *The Business Impact and Use Cases for Artificial Intelligence, Gartner, 2017 Accelerate computational performance AI-enabled endpoints AI-enabled autonomous endpoints
Flexibility Efficiency AI ACCELERATORS Nvidia and Microsoft launch Azure supercomputing instance – Nov. 18, 2019 https://venturebeat.com/2019/11/18/nvidia-and-microsoft-launch-azure- supercomputing-instance/
Deep Learning Analytics – GPU, Graphcore Performance $ Inference Data Analytics Multi-App HPC / ML / DL C6420pC6420p R840 DS8440 8+ 4 2 - 3 1 Solution price $ C4140C4140 GPU DB Acceleration, AI/ML R940xa SDS/VDI R740XDR740XD 1:1 CPU/GPU ratio Highest density of CPU and memory with 2 GPUs GRAPHCORE IPU XILINUX FPGA INTEL FPGA NVIDIA GPU INTEL CPU AMD (Future) GRAPHCORE IPU XILINUX FPGA INTEL FPGA NVIDIA GPU INTEL CPU AMD (Future) Cost DELL TECHNOLOGIES – AI COMPUTE PLATFORMS
Differentiated Features Benefits Up to 10 Accelerator cards including Industry leading NVIDIA Telsa V100 GPUs and Graphcore Colossus C2 IPU cards (Post RTS) Exceptional horsepower in a 4U chassis Supports up to 205W CPUs with accelerators in 35C environments Thermally unconstrained provides flexibility for a variety of configurations/ environments Up to 10 drives of local storage (NVMe and SAS/SATA) Accelerated access to training data 8 x PCIe Gen3 Extensive I/O options for network/IO traffic DSS 8440 with NVIDIA GPUs DSS 8440 with Graphcore IPUs A DYNAMIC MACHINE LEARNING AND HPC PLATFORM
Oppenheimer Equity Research, Server Market Oct-1, 2018 Tractica, Deep Learning Chipset, 2018 $60B AI Server TAM A new AI Server Infrastructure Deployment GPU Virtualization Market TAM $3B ARR Virtualization $4.5B ARR Orchestration/Analytics 2016 2025 $0.16B $0.35B $2.3B $9.8B 28x CPU GPU CPU GPU FPGA ASIC AI Semiconductors massive growth is Revolutionizing the AI infrastructure AI INFRASTRUCTURE ECONOMICS
vComputeServer Hypervisor 50% vGPU 30% vGPU 20% vGPU GPU Maximize Efficiency Use Case Creative & Technical Professional Knowledge Worker AI, Deep Learning, & Data Science Compute Type Client Computing Client Computing Server Workloads Software Quadro Virtual Data Center Workstation GRID Virtual PC GRID Virtual Apps vComputeServer NVIDIA GPU VIRTUALIZATION
Ethernet GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPUGPU GPUGPU GPUGPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPUGPU GPU GPU GPU GPU GPUGPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU GPU Public Cloud VMWare VSphere GPUAAS
ARTIFICIAL INTELLIGENCE SHARE SNAPSHOT Dell Technologies' 2018 AI revenue grew 72.6% to $1.89 billion, from $1.10 billion in 2017. Dell revenue in the AI market is primarily from infrastructure (server and storage). *IBM's AI revenue grew 19.0% to $2.58 billion, from $2.17 billion in 2017. Revenue is divided across software, hardware, and services, with services and hardware significantly larger than software ($349.6M).
• Eliminate inefficient islands of storage – Infrastructure consolidation for both clinical and non-clinical workloads • Scales as data growth and number of instruments, modalities, and digital clinical applications increases • Enable better information sharing • Accelerate data analytics to gain new insight • Extends into the cloud • Prepared for next generation analytics Dell EMC Data Lake Caffe2 DATA LAKE STORAGE PLATFORM
The Marketplace Continues To Evolve Data science and machine-learning platforms are defined as: • A cohesive software application that offers a mixture of basic building blocks essential both for creating many kinds of data science solution and incorporating such solutions into business processes, surrounding infrastructure and products. Firms assessed: • Leaders: KNIME, Alteryx, SAS, RapidMiner, H2O.ai • Challengers: MathWorks, TIBCO Software • Visionaries: IBM, Microsoft, Domino Data Lab, Dataiku, Databricks • Niche: SAP, Angoss, Anaconda, Teradata “By 2019, startups will overtake Amazon, Google, IBM and Microsoft in driving the artificial intelligence economy with disruptive business solutions.” Gartner DATA SCIENCE AND ML PLATFORMS
Data Environment Challenge Best Practices Data Quality Inconsistently formatted across the organization, often contains errors and could create biases. Minimize duplication. Implement Data Governance strategy and tools to cleanse, validate and enforce data quality. Data Silos Inability to leverage data across the enterprise for analytics Develop/engineer a common data repository to access for advanced analytics New Sources of Data Machine/Deep Learning often requires large amounts of data, from various sources, and formats (unstructured). Develop a data integration strategy and use tools to process unstructured data that requires additional processing, cleansing, and/or normalization. Supporting Near Real- time Requests Data repositories are challenged scaling transactional and query- based workloads together Consider translytical databases to address near real-time data response times for advanced analytical applications. Privacy, Security, Ethics, Auditability Often not considered at design time and leads to governance exposures. Adoption of design principles to provide data transparency for key stakeholders and accountability for governance/regulation organizations. AI DATA DEVELOPMENT BEST PRACTICES
IDC 2018 DATA SCIENTIST CRITERIA FOR AI INFRASTRUCTURE
WORLD-CLASS INFRASTRUCTURE IN THE INNOVATION LAB Zenith • TOP500-class system based on Intel Scalable Systems Framework (OPA, KNL, Xeon, OpenHPC) • 424 nodes dual Intel Xeon Gold processors, Omni-Path • +160 Intel Xeon Phi (KNL) servers. • Over 1 PF combined performance! • #396 on Top500, 1.86 PF theoretical peak • Lustre, Isilon H600, Isilon F800 and NSS storage • Liquid cooled and air cooled Rattler • Research/development system with Mellanox, NVIDIA and Bright Computing • 88 nodes with EDR InfiniBand and Intel Xeon Gold processors • 32x PowerEdge C4140 nodes with 4x NVIDIA GPUs Other systems • 32 node AMD cluster, storage solutions, etc. 13K ft.2 lab, 1,300+ servers, ~10PB storage dedicated to HPC in collaboration with the community
Workstations Servers Block Storage Unstructured Data Storage Hyper-converged Ready Solution for ML Ready Solution for DL Video Analytics, Splunk, and Streaming Ready Solutions Ready Bundle for Deep Learning - NVIDIA Ready Bundle for Deep Learning - Intel Ready Bundle for Machine and Deep Learning - Hadoop The fastest platform, exclusively for deep learning model training. A complete solution including GPU aware cluster management & the widest set of supported modeling frameworks in the portfolio Industry first exclusively Intel optimized deep learning platform that will evolve into a fully integrated software environment for optimizing the complete range of Intel based processors & acceleration hardware. Multi-purpose Intel Xeon Hadoop based platform for, deep learning, machine learning, universal analytics & ETL. Choice of value- added modular software SOLUTIONS FOR EVERY AI USE CASE
SickKids / Dell Confidential Compute/ Storage/ Networking Applications/ Use Cases Processor/ Accelerator Frameworks & Middleware Management/ Orchestration Bright ML Xeon PhiFPGA R840 C6420 R540 In-Memory Analytics Inference C4140 R740 C6320p Training DELL EMC OPEN ARCHITECTURE AND ECOSYSTEM Xeon Xeon Xeon FPGA Adapter Consumption Model Math Libraries On-prem Solutions Crest Family Cauldron- POC Bitfusion T640 GPU NvLink-GPUGPU
- Comprehensive and Scalable AI/Analytics Platform Portfolio - Workstations, Servers, Clusters, Storage, Networking - Infrastructure and Data Science and Analytics Expertise - HPC and AI Innovation Lab - IoT / Intelligent Video Analytics Lab - Solution-based Offerings - Pre-configured AI Ready Offerings - IoT / Safety and Security and Thermal Vision Solutions - GPU Virtualization - ML Platforms Infrastructure Scalability Reduce Complexity Address Demand Partner Ecosystem Cost Effective THE VALUE OF DELL AND NVIDIA FOR AI INFRASTRUCTURE
- Appendix - Dell Technologies AI and Data Analytics Solutions
Dell Technologies AI and Data Analytics Solutions AI / Machine Learning / Deep Learning • Domino Data Science Platform Design Document • HPC for AI and Data Analytics Ready Architecture • Retail Loss Prevention Ready Solutions • DataRobot Reference Architecture • H2O AI Reference Architecture • Kubeflow Reference Architecture • OneConvergence Dkube Reference Architecture • Iguazio Reference Architecture • Deep Learning with NVIDIA Ready Solutions • Isilon with NVIDIA DGX-1 Reference Architecture • Isilon with NVIDIA DGX-2 Reference Architecture • Isilon with Dell Precision 7920 Data Science Workstation Reference Architecture • Isilon with Dell EMC DSS8440 Reference Architecture • Noodle.ai (OEM) Solution Bundle IoT / Streaming / Machine Data Analytics • IntelliSite (OEM) Thermal Detection Solution • Retail Loss Prevention Ready Solutions • Dell IoT Safety and Security Portfolio • Real-Time Data Streaming Ready Architecture • Splunk Enterprise on Dell EMC Infrastructure • Streaming Data Platform • ElasticSearch (OEM) Solution Bundle © Copyright 2020 Dell Inc. Augmented Analytics and Data Warehouse • Spark on Kubernetes • Kinetica (OEM) Solution Bundle • ThoughtSpot (OEM) Solution Bundle • Pivotal Greenplum • Dell Boomi Data Lake / Unstructured Data Infrastructure • Microsoft SQL Server 2019: Big Data Cluster Ready Solution • Cloudera Hadoop Ready Architecture • Hortonworks Hadoop Ready Architecture • Kubernetes Containers with Diamanti (OEM) Solution Bundle • Grid Dynamics Reference Architecture • Red Hat OpenShift Reference Architecture HPC Ready Solutions • HPC Digital Manufacturing • HPC Life Sciences • HPC Research • HPC BeeGFS Storage • HPC Lustre Storage • HPC NFS Storage • HPC PixStor Storage *Note, some products can deliver capabilities that address multiple use cases Product Offerings and Technical Collateral for Analytical Use Cases