IBM Cognitive Systems Technology for AI

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1 IBM Cognitive Systems Technology for AI Cognitive, AI and Analytics examples, trends and directions Ulrich Walter Cognitive Systems HPC & Cloud Sales Leader Böblingen,

2 Past Processes The world is changing Present Collecting, Assistants Future Intelligent, Autonomous Point to Point Communication Social Media explosion User genereated Predictive Analytics Cognitive AI Collaborative buying Batch oriented Mobile revolution Location based Digital money Palm sized And wearable Manual Processes IT Centralized System Power of analytics Cloud enablement XaaS Entire Integration Autonomous Social feedback loop Boundary less IBM Systems 2

3 It s all about prediction and recognition Cogito, Ergo Sum Rene Descartes, 1637 Γνῶθι σεαυτόν Chilon of Sparta, 555 B.C IBM Systems 3

4 AI MOMENTUM Today By 2020 By 2020 By 2020 > % $47B 20% AI startups of all customer service interactions will be powered by AI bots spend on AI technologies of companies will dedicate workers to monitor and guide neural networks. IBM Systems 4

5 Obama: My Successor Will Govern a Country Being Transformed by AI IBM Systems 5

6 Overall Artificial Intelligence (AI) Space Cognitive / ML/DL Human Intelligence Exhibited by Machines Machine Learning Human Trained using large amounts of data & ability to learn how to perform the task Deep Learning IT Systems break tasks into Artificial Neural Networks New Data Sources: NoSQL, Hadoop & Analytics New class of applications Machine Learing & Training Pattern matching Image Real-time decision support Complex workflows Data Lakes Extend Enterprise applications Finance: Fraud detection / prevention Retail: shopping advisors Healthcare: Diagnostics and treatment Supply chain and logistics Extend Predictive Analytics to Advance Analytics with AI Growing across Compute, Network, Middleware, and Storage IBM Systems 6

7 By 2022,HPC-driven simulations and deep learning will be the core innovation engines driving 10,000x increase in compute requirements IBM Systems 7

8 Onthology AI Hardware Solution areas for AI NLS and text mining systems Image Recognition Autonomous systems Robots and robot collaboration Lerning and inference libraries Subsymbolic pattern regognition Learning Knowledge representation Knowledge processing - Search - Acknowledge - Plan Knowledge representation language Multiple agent systems Intelligent Training Softbots and digital twins Predictive Analytics

9 Industry examples Deep Learning/Big Data Automotive and Transportation Security and Public Safety Consumer Web, Mobile, Retail Medicine and Biology Broadcast, Media and Entertainment Autonomous driving: Pedestrian detection Accident avoidance Maintenance prediction Video Surveillance Image analysis Facial recognition and detection Image tagging Speech recognition Natural language Sentiment analysis Drug discovery Diagnostic assistance Cancer cell detection Captioning Search Recommendations Real time translation IBM Systems 9

10 The idea A Computer with some human attributes How can a computer recognize, understand and interpret human language? How can robots collaborate autonomous in teams and solve problems? How can a computer analyze and create movies? How can computers and robots explore untouched and dangerous areas? Intelligente Software- Systeme des DFKI How can systems learn by experience? How can computers and robots become intelligente assistants? How can computers detect human mood and feelings? How can computer systems conclude on experience and data? IBM Systems 10

11 Deep learning in action Mastering the turing test requires deep learning IBM Systems 11

12 For machine/deep learning you need the following components Input layer Hidden Layer (s) Output layer A large set of tagged data 2. A neuronal network 3. An HPC server with GPUs and extreme high internal bandwidth IBM Systems 12

13 Enterprise data sources, analytics and deep learning Analytics platforms and frameworks People Ecosystems Intelligence DATA Data + Analytics Analytics Things IT Systems Structured & Unstructured data IBM Systems 13

14 Some principles of AI 1 Detect and Collect 2 Store 3 Analyze/Learn Distributed Deep Learning Image & Video Text Compress/Map Reduce Comparison and intrepretation Voice & Sound Sensor Tag/Aggregate Knowledge Base Combine Conclude Canned or tinned knowledge Data Collection, Storage and Distribution POWER AI Framework Storage nodes complementing IBM Watson IBM Systems 14

15 Deep learning in multiple layered convolutional neuronal networks (CNN) Raw data Iterated data Tagged data Elephants Chairs IBM Systems 15

16 Attributes as side information Images Attributes Class Long fluffy ears Brown fur Lives in Australia Feeds on Eukalyptus Mammal Koala No ears Black and white feathers Lives in antarctica Feeds on Fish Bird Penguin IBM Systems 16

17 Deep Learning in a Nutshell Shallow (supervised) machine learning pipeline Very difficult to find robust mathematical Representations Feature extraction learning model Coffee Mugs Done by human experts IBM Systems

18 Deep Learning in a Nutshell closed optimization of this problem by Neuronal Networks with many layers 3.Feature Extraction Pixel Analysis, color and channel depths, patterns etc. Semantic label -Coffee Mug -Right handle -white 2. Tagging Or semantic Label 4.Modelling x2 Building the model with a CNN x1 xn f = (x1, x2 xn) 1. Unstructured Data 5.Model IBM Systems 18

19 Problem identification A typical training cycle Is it a machine perception problem No Look at other approach Yes Is there sufficient data to train on? No Gather more data Training and inference Execute Training models Data transformation Align relevant data sets using big data ETL middleware to standard schema Tag/aggregate Select and define training algorithm Evaluate reuslts and fine tune algorithms Deploy for production IBM Systems 19

20 Simple example of classification for monitored learning f( ) = Merkel f( ) = Gabriel f( ) = Merkel f(x)= y; y= Output, x= Input, f=classification function Trainingsphase: Generate function f, which minimizes the classification error in function f Testphase: Execute on Data not contained in training data. Slide credit: L. Lazebnik IBM Systems

21 Deep Learning for picture recognition: networks on multiple layers with feature recognitized Neurons Accident on the highway Scene- and Objectmodel Output layer Objects Scenelements, Object artefacts Image elements, arrays and borders, Pic 3 Pic 2 Pic 1 Input layer IBM Systems

22 Googles Tensorflow as Workbench for machine learning Blog Post: goo.gl/wffeca IBM Systems

23 y = Price of a house TensorFlow as operational graph of operations of Tensor data pack Ouput Input A Scalar is a Tensor A Vector is a Tensor A Matrix ist ein Tensor y = Wx + b Weight Predictive error x = Size of a house Example: Find the housepreis (y) depending on the housesize (x), simplified because multidimensional, e.g. area, age, features Bitte geben Sie hier den Titel Ihrer Präsentation ein Objective: Search the best predictions for W und b IBM Systems

24 TensorFlow program code in Python import tensorflow as tf x = tf.placeholder(shape=[none], dtype=tf.float32, name='x') W = tf.variable(tf.random_normal([1], name= W ) b = tf.variable(tf.random_normal([1], name= b ) y = W * x + b Design of the Graphmodell b + y matmul with tf.session() as sess: sess.run(tf.initialize_all_variables()) Start of learning environment Initializaiton of Variables W print(sess.run(y, feed_dict={x: x_in})) Start of Trainings x IBM Systems

25 Classification of three types of Iris using a TensorFlow-Model Iris setosa (0) Iris versicolor (1) Iris virginica (2) Bitte geben Sie hier den Titel Ihrer Präsentation ein / IBM Systems

26 Development of Convolutional Networks # of Transistors per CPU 10 6 # of Pixels 10 7 # of Transistors per CPU 10 9 # of Pixels IBM Systems 26

27 Sic Transit Gloria Mundi Google Brain Servers ~ 8 MW/h ~ 50 TFLOPS 3 NVIDIA PASCAL GPUs ~ 0,9kW/h ~ 62 TFLOPS IBM Systems 27

28 Leveraging the first CPU designed for accelerated computing Faster Cores than x86 Larger Caches Per Core than x86 5X Faster CPU-GPU Data Communication P8 CAPI NVLink POWER8 PCIe High Performance Cores Fast & Large Memory System Fast Power Interconnects for Accelerators IBM Systems 28

29 IBM Power Systems LC Line for AI, HPC and BigData OpenPOWER servers for cloud and cluster deployments that are different by design High Performance Computing S822LC For Big Data S822LC For High Performance Computing S821LC S812LC S822LC Storage rich single socket system for big data applications Memory Intensive workloads Ideal for storage-centric and high data throughput workloads Brings 2 POWER8 sockets for Big Data workloads Big data acceleration with work CAPI and GPUs Incorporates the new POWER8 processor with NVIDIA NVLink Delivers 2.8X the bandwidth to GPUs accelerators Up to 4 integrated NVIDIA Pascal GPUs 2 POWER8 sockets in a 1U form factor Ideal for environments requiring dense computing 2X memory bandwidth of Intel x86 systems Memory Intensive workloads IBM Systems 29

30 Power S822LC for HPC (aka Minsky) vs x86 with P100 GPU 2.8X the CPU-GPU bandwidth compared to x86 based systems S822LC for HPC with CPU-GPU NVLink capability not available on x86 servers faster than any PCI-E platform with 4 GPUs S822LC for HPC packaging allows for higher power/frequency X86 P100 PCI-E Performance compares Kinetica: 2.7X vs x86 with 4 PCI-E based P100 CPMD: 3X performance of CPU only implementation The first ever GPU accelerated version of CPMD NAMD: 30% increase when combine with visualization code IBM Systems 30

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32 Power AI takes advantage of NVLink between the POWER8 CPU the P100 GPUs to increase system bandwidth NVLink between NVIDIA CPUs GPU and GPUs enables fast memory access to large data sets in system memory Graphics Memory Two NVLink connections GB/s between each GPU and CPU-GPU leads to PCIe faster x16 data exchange System Memory Graphics Memory System Memory GPU with NVLink GB/s Graphics Memory Power Chip with NVLink IBM Systems 33

33 Throughput test with MINSKY and x86 platforms Advantages - Reduced Training times x3 in comparions to PCIe - Rapid deployment of models - GPU efficiency at > 95% - Well balanced system IBM Systems 34

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35 POWER 8 CAPI Coherent Accellerator Processor Interface Virtual Addressing Accelerator can work with same memory addresses that the processors use POWER8 Hardware Managed Cache Coherence Enables the accelerator to participate in Locks as a normal thread Lowers Latency over IO communication model Customizable Hardware Application Accelerator Specific system SW, middleware, or user application Written to durable interface provided by PSL PCIe Gen 3 Transport for encapsulated messages _ PSL FPGA or ASIC Coherence Bus CAPP Processor Service Layer (PSL) Present robust, durable interfaces to applications Offload complexity / content from CAPP IBM Systems 36

36 CAPI vs. I/O Device Driver: Data Prep IBM Systems 37

37 AI and ML do s and dont s AI/ML do s AI/ML don ts Too Complex dependencies Impossible to program Specialized Highly customized solutions required Try to make waste to knowledge No data or limited amount of data available Too much data No scalability by human interaction Customization required Long term autonomous learning Perfection required No differentiator between good and bad IBM Systems 38

38 Development of Hybrid Cloud Metasystems as data sources for AI System of Records & AI Governance & Control Permanent connection Cloud Service a API Ecosystems Cloud Service c Rapid depolyment Time to market Access to external data Temporary connection Cloud Service b Improved flexibility Service 3 9 IBM Systems

39 Security, defence, protection of cyber crime Connecting data islands for a hyperconnected and cognitive digital universe Health & research Weather, climate research & Agriculture API API Connected Home Wearables & mobility Infotainment, industrial & military health and fitness API API IBM Bluemix IBM Hybrid Cloud IBM Watson API Connected, autonomous vehicles and intelligent traffic systems API Industrie 4.0 API API API API Energy, utilities and Smart cities Banking, finance & insurance Retail and Marketing IBM Systems 40

40 Challenges ahead 1. Digital transformation 2. Data and ecosystems as competitive advantage 3. Business value estimation 4. Combine & Conclude on AI methods and data (e.g. picture + voice/sound + sensor = x) 5. Organziational changes 6. Lifecycle Management 7. Service Orchestration 8. Governance and Control 9. Integration of legacy systems 10. Security and Compliance IBM Systems 41

41 Conclusion 1. Deep learning and AI will touch every area of our life 2. Autonomous Systems require AI/deep learning based on big data 3. Autonomous Systems must combine subsymbolic and symbolic AI processes in hybrid architectures 4. Business processes must adopt AI/DL as an important business value and driver for new business models 5. The AI/DL system infrastructure must be as well scalable, reliable and efficient for compute, network and storage 6. Collaboration with a variety of enterprises (x2x) and customers and deep integration of AI/DL processes will become standard 7. Multiple autonomous systems can operate as hybrid teams in order to collaborate as a team 8. Auto-Pilots and autonomous driving will become possible. Humans just need to intercept in exceptional situations. 9. Beside of technical and business oriented questions of autonomous systems there are still multiple ethical, juristic and social areas to be considered.

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45 Thank you! ibm.com/systems/hpc IBM Systems 46

46 Thank you! ibm.com/systems/hpc IBM Systems 47

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52 IBM Investment in Innovation Accelerated and Open Source Data Bases and storage Top R&D Applications Machine Learning/ Deep Learning Accelerated DB: Kinetica, Blazegraph OSDB: EnterpriseDB, MongoDB, Redis, Neo4J, Cassandra GROMACS, Gaussian, NAMD, VMD, WRF, VASP, OpenFOAM, LS Dyna, AMBER, NCBI BLAST, GATK4, NWChem GAMESS, Quantum ESPRESSO LAMMPS, CHARMM CP2K, LQCD, QMCPack MILC, Chroma, QPACE COSMO, Abinit, COMSOL, CPMD, GTC, HOMME HYCOM PowerAI ML/DL Software Distro (link) Built for Deployment Speed & with Real Performance Optimization Caffe, Torch, Theano, DIGITS Python, OpenBLAS and other dependencies Caffe, Torch, Theano, DIGITS, TensorFlow, DL4J, more on POWER Custom Caffe- CPU/GPU NVLink Optimized IBM Systems 53

53 Several Options to Realize Performance Enhancements via GPU Acceleration Easy Ease of Use Best Application Performance Best Libraries ESSL/PESSL NVIDIA Libraries Math library, cublas, NPP, etc Easy to Implement Tested and Supported Limited your needs may not be covered Programing models supporting directives OpenACC Open MP Modification of existing programs with directives Compiler assists with mapping to device Programing language which targets GPU CUDA Most time intensive Requires expertise Achieves best performance results IBM Systems 54

54 IBM Power 822LC - 2 Socket Power 8, 4 GPU System NVidia GPU SXM2 form factor NVLink W Max of 2 per socket PCIe slot (2x) Gen3 PCIe HHHL Adapter POWER 8 with NVLINK (2x) 190W Sort Integrated NVLink 1.0 PCIe slot (1x) Gen3 PCIe HHHL Adapter Service Controller Card BMC Content Memory DIMM s Riser (8x) 4 IS DDR4 DIMMs per riser Single Centaur per riser 32 IS DIMM s total GB memory capacity Cooling Fans 80mm Counter- Rotating Fans Hot swap Power Supplies (2x) 1300W Common Form Factor Supply HDD Option (2x) 0-2, 1TB SATA HDD Tray design for install/removal Hot Swap IBM Systems 55