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November 10, 2016
Adrian Bowles, PhD
Founder, STORM Insights, Inc.
info@storminsights.com
Emerging Hardware Choices for #M...
Copyright (c) 2016 by STORM Insights Inc. All Rights reserved.
Hardware - The Final Frontier for Workload Optimization
Per...
Copyright (c) 2016 by STORM Insights Inc. All Rights reserved.
Value Migrates to Hardware
Optimize
Commoditize
Standardize
Conventional
AI
Machine
Learning
Big
Data
#ModernAI Scope
Copyright (c) 2016 by STORM Insights Inc. All Rights reserved.
Emerging AI Hardware Trends and Options
A Role for Hardware...
Human
Machine
Input Output
Narrative Generation
Copyright (c) 2016 by STORM Insights Inc. All Rights reserved.
Data Mgmt
L...
Copyright (c) 2016 by STORM Insights Inc. All Rights reserved.
Hearing (audioception)
~12,000 outer hair cells/ear
~3,500 ...
Copyright (c) 2016 by STORM Insights Inc. All Rights reserved.
Hearing (audioception)
~12,000 outer hair cells/ear
~3,500 ...
Copyright (c) 2015 by STORM Insights Inc. All Rights reserved.
deep
learning
Deep learning refers to a biologically-inspir...
Copyright (c) 2016 by STORM Insights Inc. All Rights reserved.
Memory
(Instructions & Data)
Central Processing Unit
(CPU)
...
Copyright (c) 2016 by STORM Insights Inc. All Rights reserved.
Memory
(Instructions & Data)
Central Processing Unit
(CPU)
...
Copyright (c) 2016 by STORM Insights Inc. All Rights reserved.
Memory
(Instructions & Data)
Central Processing Unit
(CPU)
...
Copyright (c) 2016 by STORM Insights Inc. All Rights Reserved. 9/28/2011
IBM Power 750
90 servers, 32 cores/server,
2880 C...
Copyright (c) 2016 by STORM Insights Inc. All Rights reserved.Source: https://www.top500.org/system/177999
Amdahl’s Law: T...
Copyright (c) 2015 by STORM Insights Inc. All Rights reserved.
Research Examples:
The European Commission FACETS (Fast Ana...
Copyright (c) 2016 by STORM Insights Inc. All Rights reserved.
Synapse 16 chip board
Neuromorphic Architectures
IBM - SyNA...
Source: Qualcomm
Copyright (c) 2015 by STORM Insights Inc. All Rights reserved.
Neuromorphic Architectures
MAY 2, 2016: Qu...
Copyright (c) 2016 by STORM Insights Inc. All Rights reserved.
The Nvidia M40 processor for training neural networks.
Nvid...
Copyright (c) 2016 by STORM Insights Inc. All Rights reserved.
GPU/Advanced Memory Architectures
Copyright (c) 2016 by STORM Insights Inc. All Rights reserved.
Server racks with TPUs used in the
AlphaGo matches with Lee...
Copyright (c) 2016 by STORM Insights Inc. All Rights reserved.
At Facebook, we've made great progress thus far with off-th...
Copyright (c) 2016 by STORM Insights Inc. All Rights reserved.
Source: https://www.micron.com/about/emerging-technologies/...
Copyright (c) 2016 by STORM Insights Inc. All Rights reserved.
GPU/Advanced Memory Architectures
Copyright (c) 2016 by STORM Insights Inc. All Rights reserved.
http://www.research.ibm.com/quantum/
Quantum Architectures
Copyright (c) 2016 by STORM Insights Inc. All Rights reserved.
Source: https://arxiv.org/abs/1608.00263
Quantum Architectu...
Copyright (c) 2016 by STORM Insights Inc. All Rights reserved.
Probabalistic Architecture?
Copyright (c) 2016 by STORM Insights Inc. All Rights reserved.
Neuromorphic
GPU/
Memory Acceleration
Quantum
Market/Techno...
Copyright (c) 2016 by STORM Insights Inc. All Rights reserved.
IBM
Qualcomm
Brain Corporation
(hosted by Qualcomm)
Knupath...
Copyright (c) 2016 by STORM Insights Inc. All Rights reserved.
adrian@storminsights.com
Twitter @ajbowles
Skype ajbowles
U...
Copyright (c) 2016 by STORM Insights Inc. All Rights reserved.
Basilar membrane. (2016, October 28). In Wikipedia, The Fre...
Copyright (c) 2016 by STORM Insights Inc. All Rights reserved.
Hardware - The Final Frontier for Workload Optimization
#Mo...
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Copyright (c) 2015 by STORM Insights Inc. All Rights reserved.
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Smart Data Slides: Emerging Hardware Choices for Modern AI Data Management

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Leading edge AI applications have always been resource-intensive and known for stretching the limits of conventional (von Neumann architecture) computer performance. Specialized hardware, purpose built to optimize AI applications, is not new. In fact, it should be no surprise that the very first .com internet domain was registered to Symbolics - a company that built the Lisp Machine, a dedicated AI workstation - in 1985. In the last three decades, of course, the performance of conventional computers has improved dramatically with advances in chip density (Moore’s Law) leading to faster processor speeds, memory speeds, and massively parallel architectures. And yet, some applications - like machine vision for real time video analysis and deep machine learning - always need more power.

Participants in this webinar will learn the fundamentals of the three hardware approaches that are receiving significant investments and demonstrating significant promise for AI applications.

- neuromorphic/neurosynaptic architectures (brain-inspired hardware)
- GPUs (graphics processing units, optimized for AI algorithms), and
- quantum computers (based on principles and properties of quantum-mechanics rather than binary logic).

Note - This webinar requires no previous knowledge of hardware or computer architectures.

Publié dans : Technologie
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Smart Data Slides: Emerging Hardware Choices for Modern AI Data Management

  1. 1. November 10, 2016 Adrian Bowles, PhD Founder, STORM Insights, Inc. info@storminsights.com Emerging Hardware Choices for #ModernAI
  2. 2. Copyright (c) 2016 by STORM Insights Inc. All Rights reserved. Hardware - The Final Frontier for Workload Optimization Performance Challenges for #ModernAI Optimizing Workloads Through Parallel Execution Three Architectural Paths Neuromorphic GPU/Advanced Memory Quantum Market Overview & Recommendations Agenda
  3. 3. Copyright (c) 2016 by STORM Insights Inc. All Rights reserved. Value Migrates to Hardware Optimize Commoditize Standardize
  4. 4. Conventional AI Machine Learning Big Data #ModernAI Scope
  5. 5. Copyright (c) 2016 by STORM Insights Inc. All Rights reserved. Emerging AI Hardware Trends and Options A Role for Hardware Optimization Cognitive Machine Learning Reasoning Understanding Planning Human Input Language Vision Aural Human-Oriented Output Machine Input IOT Machine-Oriented Output Emerging AI Hardware Trends and Options
  6. 6. Human Machine Input Output Narrative Generation Copyright (c) 2016 by STORM Insights Inc. All Rights reserved. Data Mgmt Learn Model Reason Understand Plan Taste Smell Touch Hear See Gestures Emotions Language Visualization Reports Haptics IoT IoT Cognitive Systems: Communication & Control Sensors Systems Controls
  7. 7. Copyright (c) 2016 by STORM Insights Inc. All Rights reserved. Hearing (audioception) ~12,000 outer hair cells/ear ~3,500 inner hair cells Vision (ophthalmoception) Photoreceptors - Per Eye ~120,000,000 rod cells (triggered by single photon) ~6,000,000 cone cells (require more photons to trigger) ~ 60,000 photosensitive ganglion cells Touch (tactioception) Thermoreceptors, mechanoreceptors, chemoreceptors and nociceptors for touch, pressure, pain, temperature, vibration Smell (olfacoception) Chemoreception Taste (gustaoception) Chemoreception Neurosynaptic Problem Solving Scope
  8. 8. Copyright (c) 2016 by STORM Insights Inc. All Rights reserved. Hearing (audioception) ~12,000 outer hair cells/ear ~3,500 inner hair cells Vision (ophthalmoception) Photoreceptors - Per Eye ~120,000,000 rod cells (triggered by single photon) ~6,000,000 cone cells (require more photons to trigger) ~ 60,000 photosensitive ganglion cells Touch (tactioception) Thermoreceptors, mechanoreceptors, chemoreceptors and nociceptors for touch, pressure, pain, temperature, vibration Smell (olfacoception) Chemoreception Taste (gustaoception) Chemoreception Human Cognition ~100,000,000,000 (100B) Neurons ~100-500,000,000,000,000 (100-500T) Synapses Neurosynaptic Problem Solving Scope Learn ModelReason Understand Plan
  9. 9. Copyright (c) 2015 by STORM Insights Inc. All Rights reserved. deep learning Deep learning refers to a biologically-inspired approach to machine learning that leverages a collection of simple processing units - analogous to neurosynaptic elements - that collaborate to solve complex problems at multiple levels of abstraction. These modern neural networks can support supervised, reinforcement, or unsupervised learning systems. In general, deep learning solutions require a high degree of parallelism, which may be implemented in hardware and/or software. Deep Learning is Inherently Parallel
  10. 10. Copyright (c) 2016 by STORM Insights Inc. All Rights reserved. Memory (Instructions & Data) Central Processing Unit (CPU) Control Unit Arithmetic/Logic Unit (ALU) Input Device(s) Output Device(s) Operating System The von Neumann Architecture
  11. 11. Copyright (c) 2016 by STORM Insights Inc. All Rights reserved. Memory (Instructions & Data) Central Processing Unit (CPU) Control Unit Arithmetic/Logic Unit (ALU) Input Device(s) Output Device(s) Operating System “Speed”/Throughput Constraints
  12. 12. Copyright (c) 2016 by STORM Insights Inc. All Rights reserved. Memory (Instructions & Data) Central Processing Unit (CPU) Control Unit Arithmetic/Logic Unit (ALU) Input Device(s) Output Device(s) Operating System Control Unit Arithmetic/Logic Unit (ALU) Parallelism With Multi-Cores
  13. 13. Copyright (c) 2016 by STORM Insights Inc. All Rights Reserved. 9/28/2011 IBM Power 750 90 servers, 32 cores/server, 2880 Cores in 10 racks 16Tb RAM ~80TeraFLOPS 80,000,000,000,000FLOPS IBM Watson - Parallelism for Deep QA
  14. 14. Copyright (c) 2016 by STORM Insights Inc. All Rights reserved.Source: https://www.top500.org/system/177999 Amdahl’s Law: The theoretical performance improvement resulting from a resource improvement for a fixed workload is limited by that part of the workload that cannot benefit from the resource improvement. Limits to Parallelism
  15. 15. Copyright (c) 2015 by STORM Insights Inc. All Rights reserved. Research Examples: The European Commission FACETS (Fast Analog Computing with Emergent Transient States) and BrainScaleS (Brain-inspired multi scale computation in neuromorphic hybrid systems) UK SpiNNaker (Spiking Neural Network Architecture) DARPA - SyNAPSE (Systems of Neuromorphic Adaptive Plastic Scalable Electronics) Computer, device/component -level systems modeled after biological systems or components, such as neurons and synapses. These may be implemented in analog, digital or hybrid hardware. Typically designed to learn by experience over time, rather than by programming. Neuromorphic Architectures (“Brain-Inspired”) Massively interconnected networks of very simple processors.
  16. 16. Copyright (c) 2016 by STORM Insights Inc. All Rights reserved. Synapse 16 chip board Neuromorphic Architectures IBM - SyNAPSE board “TrueNorth chips can be seamlessly tiled to create vast, scalable neuromorphic systems.” Already demonstrated 16 million neurons and 4 billion synapses. Goal is to integrate 4,096 chips in a single rack with 4 billion neurons and 1 trillion synapses while consuming ~4kW of power.
  17. 17. Source: Qualcomm Copyright (c) 2015 by STORM Insights Inc. All Rights reserved. Neuromorphic Architectures MAY 2, 2016: Qualcomm Incorporated (NASDAQ: QCOM) today announced at the Embedded Vision Summit in Santa Clara, Calif., that its subsidiary, Qualcomm Technologies, Inc., is offering the first deep learning software development kit (SDK) for devices powered by Qualcomm® Snapdragon™ 820 processors. The SDK, called the Qualcomm Snapdragon Neural Processing Engine, is powered by the Qualcomm® Zeroth™ Machine Intelligence Platform
  18. 18. Copyright (c) 2016 by STORM Insights Inc. All Rights reserved. The Nvidia M40 processor for training neural networks. Nvidia NVIDIA Maxwell™ architecture Up to 7 Teraflops of single-precision performance with NVIDIA GPU Boost™ 3072 NVIDIA CUDA® cores 24 GB of GDDR5 memory 288 GB/sec memory bandwidth Qualified to deliver maximum uptime in the datacenter GPU/Advanced Memory Architectures
  19. 19. Copyright (c) 2016 by STORM Insights Inc. All Rights reserved. GPU/Advanced Memory Architectures
  20. 20. Copyright (c) 2016 by STORM Insights Inc. All Rights reserved. Server racks with TPUs used in the AlphaGo matches with Lee Sedol GPU/Advanced Memory Architectures
  21. 21. Copyright (c) 2016 by STORM Insights Inc. All Rights reserved. At Facebook, we've made great progress thus far with off-the-shelf infrastructure components and design. We've developed software that can read stories, answer questions about scenes, play games and even learn unspecified tasks through observing some examples. But we realized that truly tackling these problems at scale would require us to design our own systems. Today, we're unveiling our next-generation GPU-based systems for training neural networks, which we've code-named “Big Sur.” • FAIR is more than tripling its investment in GPU hardware as we focus even more on research and enable other teams across the company to use neural networks in our products and services. • As part of our ongoing commitment to open source and open standards, we plan to contribute our innovations in GPU hardware to the Open Compute Project so others can benefit from them. Facebook Open-source AI hardware design https://code.facebook.com/posts/1687861518126048/facebook-to-open-source-ai-hardware-design/ GPU/Advanced Memory Architectures
  22. 22. Copyright (c) 2016 by STORM Insights Inc. All Rights reserved. Source: https://www.micron.com/about/emerging-technologies/automata-processing GPU/Advanced Memory Architectures
  23. 23. Copyright (c) 2016 by STORM Insights Inc. All Rights reserved. GPU/Advanced Memory Architectures
  24. 24. Copyright (c) 2016 by STORM Insights Inc. All Rights reserved. http://www.research.ibm.com/quantum/ Quantum Architectures
  25. 25. Copyright (c) 2016 by STORM Insights Inc. All Rights reserved. Source: https://arxiv.org/abs/1608.00263 Quantum Architectures
  26. 26. Copyright (c) 2016 by STORM Insights Inc. All Rights reserved. Probabalistic Architecture?
  27. 27. Copyright (c) 2016 by STORM Insights Inc. All Rights reserved. Neuromorphic GPU/ Memory Acceleration Quantum Market/Technology Positions & Maturity Ready Now Much More in the Pipeline Promising - Ready Now At Handset Level Promising - Watch But Don’t Wait Proven approach for ||ism Easy interoperability with conventional systems +Natural behavioral process model +Lower power requirements - Requires new software model & skills +Incredible compute power potential - Requires new software model & skills - Requires interface to conventional system for pre-processing - Requires extremely cold (big, expensive) environment
  28. 28. Copyright (c) 2016 by STORM Insights Inc. All Rights reserved. IBM Qualcomm Brain Corporation (hosted by Qualcomm) Knupath Tenstorrent Cirrascale Neurogrid (Stanford) Tensilica - Cadence 1026 Labs Cerebras Artificial Learning HRL Laboratories Isocline Nvidia Intel AMD Facebook (FAIR) Nervana Systems/Intel Movidius - Intel (Vision processing) Google TPU IBM D-Wave Google Neuromorphic GPU/ Memory Acceleration Quantum Ones to Watch On the Horizon Ready Now Much More in the Pipeline Promising - Ready Now At Handset Level Promising - Watch But Don’t Wait
  29. 29. Copyright (c) 2016 by STORM Insights Inc. All Rights reserved. adrian@storminsights.com Twitter @ajbowles Skype ajbowles Upcoming Webinar Dates & Topics December 8 Leverage the IOT to Build a Smart Data Ecosystem January #Modern AI and Cognitive Computing: Boundaries and Opportunities February Artificial General Intelligence: When I Can I Get It? March Data Science and Business Analysis: A Look at Best Practices for Roles, Skills, and Processes April Machine Learning: Moving Beyond Discovery to Understanding May Streaming Analytics for Agile IoT-Oriented Applications June Machine Learning Case Studies July Advances in Natural Language Processing I: Understanding August Organizing Data and Knowledge: The Role of Taxonomies and Ontologies September Advances in Natural Language Processing II: NL Generation October Choosing the Right Data Management Architecture for Cognitive Computing November See Me, Feel Me, Touch Me, Heal Me: The Rise of the Cognitive Interface December The Road to Autonomous Applications For More Information…
  30. 30. Copyright (c) 2016 by STORM Insights Inc. All Rights reserved. Basilar membrane. (2016, October 28). In Wikipedia, The Free Encyclopedia. Retrieved 01:58, October 28, 2016, from https://en.wikipedia.org/w/index.php?title=Basilar_membrane&oldid=746543229 Somatosensory system. (2016, October 9). In Wikipedia, The Free Encyclopedia. Retrieved 04:59, October 9, 2016, from https://en.wikipedia.org/w/index.php?title=Somatosensory_system&oldid=743336883 Photoreceptor cell. (2016, September 19). In Wikipedia, The Free Encyclopedia. Retrieved 03:07, September 19, 2016, from https://en.wikipedia.org/w/index.php?title=Photoreceptor_cell&oldid=740108113
  31. 31. Copyright (c) 2016 by STORM Insights Inc. All Rights reserved. Hardware - The Final Frontier for Workload Optimization #ModernAI Defined Performance Challenges Optimizing Workloads Through Parallel Execution Three Architecture Paths Neuromorphic GPU/Advanced Memory Quantum Agenda A Role for Hardware Cognitive Machine Learning Reasoning Understanding Planning Human Input Language Vision Aural Human-Oriented Output Machine Input IOT Machine-Oriented Output
  32. 32. Copyright (c) 2016 by STORM Insights Inc. All Rights reserved.
  33. 33. Copyright (c) 2015 by STORM Insights Inc. All Rights reserved.

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