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How can you construct IT Infrastructures for Enterprise AI?

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AI is already playing a critical role in the automation and augmentation of business processes across many different industries. The technologies accelerating the adoption of AI mostly associated with Machine/Deep Learning workloads include the use of GPUs, High Performance Computing infrastructures and Big Data analytics. In this session, we will discuss different ways that Fujitsu is helping fast adopters to construct AI infrastructure solutions on an enterprise scale and its latest innovation enabling AI acceleration.

Ian Godfrey
Manju Oommen
Alexander Kaffenberger

Publié dans : Technologie
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How can you construct IT Infrastructures for Enterprise AI?

  1. 1. Human Centric Innovation Co-creation for Success © 2018 FUJITSU Fujitsu Forum 2018 #fujitsuforum
  2. 2. © 2018 FUJITSU Infrastructures for Enterprise AI Alex Kaffenberger Sr Business Development Manager Manju Annie Oommen Sr Manager, Global Product Marketing Ian Godfrey Director Solution Business, FSE
  3. 3. 3 © 2018 FUJITSU Artificial Intelligence - reaching new heights!! Christie's sells painting created by Artificial Intelligence for $432,500 Oct 25, 2018 How are enterprises taking advantage of this in the Industrial segment?
  4. 4. 4 © 2018 FUJITSU Agenda Changing demands of the Industry Artificial Intelligence 1 New Technology Innovation 4 Q & A 5 Fujitsu’s Artificial Intelligence Solutions 3 Fujitsu’s approach to AI What is required to choose the right architecture? 2
  5. 5. 5 © 2018 FUJITSU Industry Demands are changing Artificial Intelligence is a game changer. Extract Trans- form Analyze Decide Collect Trans- form Analyze Navigate Decide Today‘s demand Internal and external Un-/Semi-/Poly-/structured Various sources TB and PB Prediction Detect opportunities Ad-hoc & real time Massive number of users Everywhere on all devices Artificial Intelligence Internal Structured Few sources GB and TB Historical Risk reduction Periodical & batch Limited number of users On-premise Human Intelligence BI in the past
  6. 6. 6 © 2018 FUJITSU Industries investing in AI Total AI market is expected to be $56.2 Bn by 2021 Source: derived from information available in public research
  7. 7. 7 © 2018 FUJITSU At what stage of the AI journey are you? Vote Now 1 No Budget allocated yet 33,3% 2 Evaluating or investigating 46,7% 3 Initial trial done, but further on-hold 16,7% 4. Trials done and ready to scale 3,3% 15
  8. 8. 8 © 2018 FUJITSU Artificial Intelligence | Enterprise ARTIFICIAL INTELLIGENCE A program that can sense, reasons, act and adapt MACHINE LEARNING Algorithms whose performance improve when exposed to more data over time DEEP LEARNING Multi-layered neural networks learn from vast amounts of data Source: derived from information available in public research
  9. 9. 9 © 2018 FUJITSU What led to the rise of AI adoption? c. Faster, more accurate predictions b. Reduce human interaction a. Improve existing processes and increase automation New Business requirements AI Big Data Compute/ HPC Algorithms d. More complete understanding of customer needs e. Fraud prevention, cyber security Improved and proven Algorithms, Big Data Analytics and High Performance Computing
  10. 10. 10 © 2018 FUJITSU Deep Learning: Flow of training to inference PRIMEFLEX Reference Architecture Data: Millions of images Create training data Create neural network Result Extract learned neural networks InputOutput Target for recognition Probability of dog: 0.0001 Probability of dog: 0.9999 Training Improve training data Inference Trained Model In Out
  11. 11. 11 © 2018 FUJITSU Dimensions in choosing the right architecture Compute-intensity Data-intensity Machine Learning Deep Learning Many-coreMulti-core Parallel filesystem Network attached storage Log scale Latency, the time to train or infer, determines final architecture and connectivity 10 TFlops
  12. 12. 12 © 2018 FUJITSU How Parallelism Is Evolving in Deep Learning Many-core accelerated processors have become dominant for training, and are often also needed for rapid inference Tightly-coupled parallel models are growing, with fast interconnects increasingly used for communication and data movement Source: Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis, TAL BEN-NUN and TORSTEN HOEFLER, ETH Zurich, 26 Feb 2018
  13. 13. 13 © 2018 FUJITSU Compute dimension Machine learning Works on small datasets Not as computationally intensive, can work with CPU alone Algorithms are easier to interpret due to direct feature engineering Deep learning Accuracy improves with more data Compute intensive requiring fast processors (GPU’s or specialized processors) Best with high performance I/O Inference Transfer Learning Neural Network Training PRIMERGY CX PRIMERGY RX Increasing compute power
  14. 14. 14 © 2018 FUJITSU NAS or SAN based storage Ethernet based access Data Dimension Object based Datathroughhput HDFS Big Data oriented Duplicated hardware and data (redundancy) Object interface PFS High speed parallel file system Parallel I/O processing Highly available hardware High performance I/O Single namespace POSIX file interface High speed distributed Data system – Lustre/FEFS File based Ceph storage (S3) PFS Supporting HDFS interface
  15. 15. 15 © 2018 FUJITSU Metrics for the Node Design DNN Algorithm Metrics Hardware Metrics Accuracy Network Architecture (layers, filter size, filters, channels) Number of Weights (storage capacity) Number of multiply-add operations (throughput capacity) Accuracy Energy Latency Throughput core utilisation External memory bandwidth Required on-chip storage Co-Design Reduce size of operands for storage/compute – float to fixed (linear quantization), Bit-width reduction, Non-linear quantization Reduce number of operations for storage/compute – Exploit Activation Statistics (Compression), Network Pruning, Compact Network Architectures Mainly applied to Inference, but some benefits for Training
  16. 16. 16 © 2018 FUJITSU Metrics for System Architecture and Scale Continuous Development Data collection Model training Model deployment & Inference Accuracy monitoring Transfer Learning Production Only
  17. 17. 17 © 2018 FUJITSU Elements in the PRIMEFLEX Solution Compute Server – Training High memory, expandability RX2540 [GPU] Nvidia 1 or 2 Titan*,1 or 2 V100 [CPU] Dual Intel Xeon Gold 6134 8C 3.20 GHz or 6128 6C 3.40 GHz Density, fastest (NVlink) CX2570 [GPU] Nvidia 1 or 2 V100, 4 GPU water-cooled NVLink [CPU] Dual Intel Xeon Gold 6134 8C 3.20 GHz or 6128 6C 3.40 GHz Compute Server – Inference No GPU RX2530 or CX2550: [CPU] Dual Intel Xeon Gold 6148 20C 2.40 GHz With GPU RX2540 or CX2570: [GPU] Nvidia 1-2 Titan*, Tesla T4, (1-2 V100) [CPU] Dual Intel Xeon Gold 6134 8C 3.20 GHz or 6128 6C 3.40 GHz Interconnect PFS, large volume/throughput, high node count InfiniBand Other filesystem or lower throughput, lower node count GBE Filesystem PFS (BeeGFS/Lustre) CD10K + CEPH HDFS or PFS + HDFS interface NFS I/O Server RX2540 NAS Memory 96-192GB 96GB 96GB-192GB 96GB AI Software stack Cloudera (Data Science Workbench, Hadoop), F|AIR, … Tensorflow, Caffe, MXNET, PyTorch, … MKL-DDN, cuDNN, ...
  18. 18. 18 © 2018 FUJITSU What infrastructure do you need to copy PICASSO? Picture taken using Fujitsu’s AI Demo ‘Picasso’ Oct 11, 2018
  19. 19. 19 © 2018 FUJITSU Fujitsu EMEIA AI Systems – Line-up High Performance Computing Appliance  Optimal Reference Architectures  Performance boost  2-4 GPU System PRIMEFLEX for HPC Artificial Intelligence Appliance  8 GPU AI system for training and inference  Advanced Fujitsu API stack for comprehensive use cases scenarios ZINRAI Deep Learning System Big Data Appliance  Efficient Enterprise Data Store  Extreme & easy Scalability  2 GPU System PRIMEFLEX for Hadoop Storage-intense workloads Compute-intense workloads Designed for AI workloads (e.g. with FUJITSU Advanced Image Recognition) HPC to be released in 1H/2019
  20. 20. 20 © 2018 FUJITSU What are the main challenges you foresee or currently are facing in your organization? Vote Now 1 Skillsets for building an AI solution? 42% 2 Find relevant AI Use Cases to solve business problems? 38% 3 Understanding the relevance and return of investment of your existing AI solution? 8% 4. Can I afford this and can AI really help my organization? 12% 15
  21. 21. 21 © 2018 FUJITSU PRIMEFLEX for HPC for Artificial Intelligence HPC optimized Server x86 Servers & performance boost with GPUs Pre-configured PRIMERGY server cluster tuned for customer workload and latency Deep Learning Frameworks with Multi-Node support for Artificial Intelligence use cases Fujitsu DL Services + Solution Development to enable true digital co-creation Extreme & easy Scalability Start small and grow on demand Zinrai Deep Learning System InfrastructureSoftwareServices AI Services Integration Service AI & HPC Software Components Cluster PRIMEFLEX for HPC Fast Interconnect FAIR Math Libraries, MPI with Fast Interconnect Intel MKL-DNN, Nvidia cuDNN, & other 3rd party Processors Company, product and service names or images may be trademarks or registered trademarks of their respective owners.
  22. 22. 22 © 2018 FUJITSU PRIMEFLEX for Hadoop = Big Data Analytics + Artificial Intelligence Efficient Enterprise Data Store Standard x86 Servers & performance boost with GPUs Pre-configured PRIMERGY server cluster running open source software Hadoop distribution and Self-service Analytics from Cloudera®, Hortonworks®, MapR®, Datameer® Data Science Workbench & Deep Learning Frameworks for AI use cases with native GPU support Fujitsu Analytics Services to enable true digital co-creation Extreme & easy Scalability Start small: 4 servers (48 TB). Scale: 48 racks (100 PB) InfrastructureSoftwareServices Analytic Services Sizing and Integra- tion Service Analytics & Big Data Management Analytics Servers PRIMEFLEX for Hadoop AI Node + NVIDIA GPU Company, product and service names or images may be trademarks or registered trademarks of their respective owners.
  23. 23. 23 © 2018 FUJITSU Zinrai Deep Learning System Fully integrated solution (HW, SW) Deep Learning/ inference optimized configuration Staged and factory installed Rapid deployment and predefined learning models Hybrid On premise (ZDLS), cloud (ZDL) and hybrid (ZDLS + ZDL) Performance 8x GPU NVIDIA® Tesla® V100 Services Comprehensive services through to the API level APIs Advanced interfaces for multiple use cases InfrastructureSoftwareServices AI Services Integration Service AI Software Components AI Servers Zinrai Deep Learning System NVIDIA GPUs
  24. 24. 24 © 2018 FUJITSU AI for Quality Control Software Stack for Fujitsu’s Image Recognition DL platform Quality Control operator interface Neural Network progress monitor F|AIR business functional interface Production-ready solution for non-destructive testing Fujitsu Gateway AI Platform Workload management and scalable hybrid IT support Modular DL Frameworks Multiple pre-trained networks and object detectors Co-Creation APIs Adaptable interface, integration with operational systems Performance-optimising libraries Tuned AI frameworks, Intel and Nvidia maths support Reference IT designs Consultation on optimal architectures for customer needs
  25. 25. 25 © 2018 FUJITSU PRIMEFLEX design inputs for Quality Control BROKEN: 94.82% Purpose Classification Object detection** Processor 1x Nvidia P100 1x Nvidia TitanX Base network VGG16 VGG16 Batch size 8 8 Input resolution 224 x 224 300 x 300 Inference rate (frames per second) 324 59 Actual image size 4000 x 2672 1000000 x 2000 Training time is highly dependent on the target accuracy and is subject to randomness in compute trajectory ** Detection with Single Shot MultiBox Detector (SDD)reported 31x109 compute operations per inference
  26. 26. 26 © 2018 FUJITSU Real world use cases implemented Underground cavity inspection Quality control on supply aircraft components Anomaly Detection in Manufacturing Rail maintenance Non-destructive material testing
  27. 27. 27 © 2018 FUJITSU Use Case: Machine Data Analysis Challenges Sensor logs from CNC machine. Using multiple tools on a work piece. Many sensors files for import. Find anomalies pointing to production failure. Automation of complex failure detection. Solution Detect anomalies with ML clustering algorithm Benefits Fast wizard driven import of many file formats from many sources Data Sensor data from CNC machines 100 files/second/tool application Rapid data visualization with flip sheet and drag&drop Infographics Easy cleansing, transformation and analysis by familiar Excel like Workbooks and visualization and/or export of results Machine learning: Clustering (K-means) for automatic grouping Anomalies & Patterns detected
  28. 28. 28 © 2018 FUJITSU Use Case: non-destructive Material Testing (image recognition) Challenge Production of large wind turbine blades requires quality assurance of the wing body. Ultrasonic scan of the entire shell construction. Manual inspection of diagnostic images. Solution Image recognition with deep learning approach for automated image analysis. (FAIR) Benefits Pre-trained neural network reliably learns to identify defects Data Historical image data with corresponding marking of defects Easy-to-use GUI for validating and further processing of detected weak spots Reduction of 80% processing time for image verification Continuous improvement based on incoming new images Processing reduced by 80% Damage
  29. 29. 29 © 2018 FUJITSU Fujitsu driving latest technology in line with Industry trends Trends driven with slow down of Moore’s law Fujitsu’s latest upcoming technology
  30. 30. 30 © 2018 FUJITSU Fujitsu’s innovation in AI|DLU™ High Performance Architecture optimized for deep learning 10x performance per watt Compatibility Migration capability for learned models and user applications used in current GPU environment. Scalability Scalable up to 1,024 classes Infrastructure utilizing DLU™ mounted server designed for deep learning
  31. 31. 31 © 2018 FUJITSU Multiple Node Multiple node basic configuration toward next node Single Node UP to 4x DLU™ per Node Scalable up-to 1,024x DLU™ InfiniBand Board DLU™ Board DLU™ Board InfiniBand Board T-switch T-switch DLU™ Board DLU™ Board PCIeGen3×16 InfiniBand HDR ×4 T-link cable MiniSAS cable InfiniBand Switch … … High Scalability by multi-node configuration
  32. 32. 32 © 2018 FUJITSU Qualifying for Quantum with Fujitsu’s Digital Annealer A Revolutionizing technology solving real world “combinatory optimization problems” overcoming the challenges of traditional quantum computing today! 17000 times faster than industry standard compute* Performance 14 Moore’s Generations Ahead Avoid the complexity and energy costs of advanced cooling solutions * This figure is based on solving a typical combinatory optimization problem in software using the algorithm implemented in the hardware running on a Xeon family processor. The solution is currently available on cloud services and would be available as an on-premise solution in the future
  33. 33. 33 © 2018 FUJITSU What does it mean from an AI perspective? Quantum computing algorithms facilitate an enhancement in multi-fold to what is already possible with machine learning.
  34. 34. 34 © 2018 FUJITSU Find Artificial Intelligence showcases in the Exhibition Area A (A) Fujitsu Advanced Image Recognition (FAIR)
  35. 35. 35 © 2018 FUJITSU Fujitsu Sans Light – abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789 ¬!”£$%^&*()_+-=[]{};’#:@~,./<>?| ©¨~¡¢¤¥¦§¨ª«»¬- ®¯°±²³µ¶·¸¹º¼½¾¿ÀÁÂÃÄÅÇÈÆÉÊËÌÍÎÏÐÑÒÓÔÕÖ×ØÙÚÛÜÝÞßàáâãäåæçèéêëìíîïðñòóôõö÷øùúûü ýþÿĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝·-‒–—―‘’‚“”„†‡•…‰‹›‾⁄⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉€™Ω→∂∆∏∑−√∞∫≈≠≤≥⋅■◊fifl Fujitsu Sans – abcdefghijklmnopqrstuvwxyz 0123456789 ¬!”£$%^&*()_+-=[]{};’#:@~,./<>?| ©¨~¡¢¤¥¦§¨ª«»¬- ®¯°±²³µ¶·¸¹º¼½¾¿ÀÁÂÃÄÅÇÈÆÉÊËÌÍÎÏÐÑÒÓÔÕÖ×ØÙÚÛÜÝÞßàáâãäåæçèéêëìíîïðñòóôõö÷øùúû üýþÿĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝·-‒–—―‘’‚“”„†‡•…‰‹›‾⁄⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉€™Ω→∂∆∏∑−√∞∫≈≠≤≥⋅■◊fifl Fujitsu Sans Medium – abcdefghijklmnopqrstuvwxyz 0123456789 ¬!”£$%^&*()_+- =[]{};’#:@~,./<>?| ©¨~¡¢¤¥¦§¨ª«»¬- ®¯°±²³µ¶·¸¹º¼½¾¿ÀÁÂÃÄÅÇÈÆÉÊËÌÍÎÏÐÑÒÓÔÕÖ×ØÙÚÛÜÝÞßàáâãäåæçèéêëìíîïðñòóôõö÷øùú ûüýþÿĐıŒœŠšŸŽžƒʼˆˇˉ˙˚˛˜˝·-‒–— ―‘’‚“”„†‡•…‰‹›‾⁄⁰⁴⁵⁶⁷⁸⁹₀₁₂₃₄₅₆₇₈₉€™Ω→∂∆∏∑−√∞∫≈≠≤≥⋅■◊fifl