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End of Moore's Law?

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Moore’s Law is slowing, but more importantly the world is changing from PCs to smart phones and cloud computing where improvements continue to occur. Improvements are still occurring in other types of ICs such as wireless, GPUs, and 3D camera chips because they lag microprocessors and parallel processing is easier on them than on microprocessors. Data centers are also experiencing rapid improvements as changes in architecture are made, particularly for analyzing unstructured data, i.e., Big Data. These slides discuss the implications for new services in areas such as smart phones, software, and Big Data. The last one-third of the slides summarize alternatives to silicon and von Neumann.

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End of Moore's Law?

  1. 1. A/Prof Jeffrey Funk Division of Engineering and Technology Management National University of Singapore For information on other technologies, see http://www.slideshare.net/Funk98/presentations Implications for Smart Phones, Big Data and Software. What is the future of work?
  2. 2.  Improvements in microprocessors (Moore’s Law) are probably slowing  But not improvements in other components, partly because they lag microprocessors • Graphic processors and 3D camera chips • Wireless chips and Data Centers  What does this mean for future of Information Technology….. and Businesses? • Smart phone becomes dominant device • Internet of Things provides new data • Most IT processing (i.e., big data) and storage moves to cloud
  3. 3.  More types of data will be collected and analyzed  New sources of data • “Things,” thus Internet of Things in Session 5 • Bio-sensors for health care data in Session 6  New forms of smart phones and wearable computing can manage data • Better touch displays, voice recognition, Session 7 • Virtual and augmented reality, wearable, Session 8  What opportunities will be created? • What types of data will become important to your business?
  4. 4. Session Technology 1 Objectives and overview of course 2 How/when do new technologies become economically feasible? 3 Two types of improvements: 1) Creating materials that better exploit physical phenomena; 2) Geometrical scaling 4 Semiconductors, ICs, electronic systems 5 Sensors, MEMS and the Internet of Things 6 Bio-electronics, Health Care, DNA Sequencers 7 Displays, including touch displays 8 Voice and gesture interfaces, AR, VR, wearables, neural 9 Information Technology and Land Transportation 10 Smart Cities: lighting, food, water, 3D printing This is Fourth Session of MT5009
  5. 5.  What do numbers say about Moore’s Law? • Microprocessors and Flash memory • Graphic processors and 3D camera chips • Wireless chips, Data Centers  What does this mean for: • Smart phones? • Software and Big Data?  Alternatives to Silicon and von Neumann • Organic transistors • Carbon nanotubes, Graphene • Atomic transistors • Synapse, AHaH • Quantum computers
  6. 6. Is Moore’s Law Ending? Economist, March 12-18, 2016
  7. 7. People Have Been Talking About These Problems for Many Years; Is this a Real End for Moore’s Law?
  8. 8.  Becoming increasingly expensive  Without further reductions, hard to • increase the number of transistors per chip • reduce cost per transistor  Main problem is photolithography • See below If photolithography and other problems can’t be solved, • New solutions are needed (see below)
  9. 9. Source: Chuck Moore, Data Processing in Exascale-Class Systems, April 27, 2011. Salishan Conference on High Speed Computing Power and Heat Problems Led to Multiple Cores and Prevent Further Improvements in Speed
  10. 10.  Can the number continue to be increased?  Many say NO  Hard to break up problems into still smaller ones for general purpose processors  Additional problem is whether costs have stopped falling • Related to problems with reducing feature sizes and increasing number of cores?
  11. 11. http://www.economist.com/blogs/economist-explains/2015/04/economist-explains-17 Intel Says Differently (1 Feb 2016) Intel reiterated its claim it has reduced cost per transistor at its 22 and 14nm nodes at a rate slightly better than the industry’s 30% historical trend. That’s despite the fact the cost of developing each new process has risen to 30% in the last few nodes up from a historical trend of 10%. http://www.eetimes.com/document.asp?doc _id=1328835 If Costs are no Longer Falling, There is Big Problem
  12. 12. Intel Says Costs Continue to Fall
  13. 13. http://www.alixpartners.com/en/Publications/AllArticles/tabid/635/articleType/ArticleView/articleId/941/Cashing-in-with-Chips.aspx#sthash.QDcy503U.dpbs Some Costs Rise as Feature Sizes Become Smaller
  14. 14. http://www.chipdesignmag.com/bursky/ Cost of Photolithography (Litho) is Rising Faster than other Process Equipment
  15. 15. Photolithography Used to Form Patterns in Layers Width of this “line” is one type of feature size. Another is thickness Note: Masks are made with Electron Beam, which is even more expensive
  16. 16. Bottleneck in photolithographic process is wavelength of light. Feature sizes are now smaller than wavelength of visible light Source: http://www.soccentral.com /results.asp?CatID=488&EntryID=30894
  17. 17.  Must compensate with strong optical lenses and error correction software  Basically putting a supercomputer in a photolithographic equipment  This is one reason for rising cost of fabrication facilities
  18. 18. Light is emitted by a plasma Need 1) Vacuum since air absorbs small wave- length light 2) stronger light source to speed up processing Raises costs Need fast processing to justify high costs Recent Development of Extreme Ultraviolet
  19. 19. Latest EUV lithography system achieves 28 wafers per hour but needs 200 wafers per hour to be economical Intel says it will use EUV for 7nm Source: http://nextbigfuture.com /2014/06/extreme- ultraviolet-lithography -hopes.html#more
  20. 20.  What do numbers say about Moore’s Law? • Microprocessors and Flash memory • Graphic processors and 3D camera chips • Wireless chips, Data Centers  What does this mean for: • Smart phones? • Software and Big Data?  Alternatives to Silicon and von Neumann • Organic transistors • Carbon nanotubes, Graphene • Atomic transistors • Synapse, AHaH • Quantum computers
  21. 21. http://ieeexplore.ieee.org/ieee_pilot/articles/96jproc11/jproc-MSanvido-2004319/article.html Memory Also Depends on Smaller Feature Sizes And thus Faces Same Problems as Microprocessors do nm
  22. 22. http://www.theregister.co.uk/2012/10/12/nand_shrink_trap/ Additional Problem for Flash Memory: Number of electrons available to hold the binary data in each cell decreases
  23. 23.  It can be cheaper to add more layers than to reduce feature sizes This is particularly true for memory chips, which are architecturally simple  Samsung is moving the fastest in memory  Other firms and chips are expected to follow
  24. 24. Continued Increases in Flash Memory Size
  25. 25. Continued Reductions in Flash Memory Cost Reductions in Feature Size Continue to Proceed Over time
  26. 26. TSV: through silicon via TSV: Through Silicon Via Can Combine Different Types of Designs on a Single 3D Chip
  27. 27. In addition to problem of electron numbers, Flash Memory has Slow Read Write Speeds http://isscc.org/doc/2013/2016_Trends.pdf (Non- Volatile
  28. 28. http://isscc.org/doc/2014/2016_Trends.pdf But Flash Continues to Win
  29. 29.  New form of non-volatile memory  1,000 times faster than NAND flash memory  10 times more data stored than in DRAM  Unique way to store data, using vertical columns of circuitry linked by crisscross grid of microscopic wires  Companies will initially offer two-layer chips that have 128 Gb, matching some NAND chips http://www.micron.com/about/innovations/3d-xpoint-technology
  30. 30.  They can replace flash memory, SRAM, and DRAM and thus enable new and better architectures  Most electronic products use all three • SRAM (fastest and most expensive volatile memory) and DRAM (slower and cheaper) store data for microprocessors • Flash memory: non-volatile memory  Combining them on single chip • can reduce overall access and processing times • can eliminate bottleneck that currently exists between memory and processor chips (see below)
  31. 31.  What do numbers say about Moore’s Law? • Microprocessors and flash Memory • Graphic processors and 3D camera chips • Wireless chips, Data Centers  What does this mean for: • Smart phones and Biometrics? • Big Data, Internet of Things?  Alternatives to Silicon and von Neumann • Organic transistors • Carbon nanotubes, Graphene • Atomic transistors • Synapse, AHaH • Quantum computers
  32. 32. May 7, 2016 http://www.wsj.com/articles/nvidia-pushes-chip-speed-higher-price-lower-1462594938
  33. 33. Launch Year 2010 (GTX 580) 2014 (GTX Titan Black) 2016 (GTX Titan X) Pascal 2017 GPU Process 40nm 28nm 28nm 16nm (TSMC FinFET) Flagship Chip GF110 GK210 GM200 GP100 GPU Design SM (Streaming Multiprocessor) SMX (Streaming Multiprocessor) SMM (Streaming Multiprocessor Maxwell) SMP (Streaming Multiprocessor Pascal) Maximum Transistors 3.00 Billion 7.08 Billion 8.00 Billion 15.3 Billion Maximum Die Size 520mm2 561mm2 601mm2 610mm2 Stream Processors Per Compute Unit 32 SPs 192 SPs 128 SPs 64 SPs Maximum CUDA Cores 512 CCs (16 CUs) 2880 CCs (15 CUs) 3072 CCs (24 CUs) 3840 CCs (60 CUs) FP32 Compute 1.33 TFLOPs(Tesla) 5.10 TFLOPs (Tesla) 6.10 TFLOPs (Tesla) ~12 TFLOPs (Tesla) FP64 Compute 0.66 TFLOPs (Tesla) 1.43 TFLOPs (Tesla) 0.20 TFLOPs (Tesla) 5.5 TFLOPs(Tesla) Maximum VRAM 1.5 GB GDDR5 6 GB GDDR5 12 GB GDDR5 16 / 32 GB HBM2 Maximum Bandwidth 192 GB/s 336 GB/s 336 GB/s 1 TB/s Maximum TDP 244W 250W 250W 300W
  34. 34.  Feature sizes lag those on micro-processors by about 5 years  Easier to break down graphics processing into smaller problems and thus use multiple cores • >3,500 cores on GPU • About 20 on general purpose microprocessor  Future of GPUs is machine learning  Nvidia, AMD, and new entrants are pursuing this market • New entrants such as Movidius and Nervana offer special-purpose processors for machine learning
  35. 35.  Use GPUs to • analyze medical images, spot anomalies on CT scans • study photos, audio files social media posts  Blue River Technology, Nervana customer • analyzes crop and weed photos to determine where to spray (5,000 decisions a minute)  Market growth between 2015 and 2024 • For GPUs: from $43.6 million to $4.1 billion • Software spending by enterprises: from $109 million to $10.4 billion http://www.wsj.com/articles/new-chips-propel-machine-learning-1463957238
  36. 36.  What do numbers say about Moore’s Law? • Microprocessors and flash Memory • Graphic processors and 3D camera chips • Wireless chips, Data Centers  What does this mean for: • Smart phones and Biometrics? • Big Data, Internet of Things?  Alternatives to Silicon and von Neumann • Organic transistors • Carbon nanotubes, Graphene • Atomic transistors • Synapse, AHaH • Quantum computers
  37. 37. PixelSize(µm2) Resolution of Camera Chips Continues to Increase, Far from Limits International Solid State Circuit State Conference, Technology Trends, 2016 http://www.future-fab.com/documents.asp?d_ID=4926 Microprocessor feature sizes are less than 20 nm
  38. 38.  Cost of 3D sensors falling quickly  Intel’s Real Sense in more than 25 models of laptops and will be in Android phones by 2017  RealSense gives 3D vision via a four- millimeter-thick strip that includes two cameras and one processor • By comparison, Kinect required a foot-long box that relied on Xbox’s processors  Other sources claim that cost of 3D vision has dropped from $200 to $20 http://www.wsj.com/articles/more-devices-gain-3-d-vision-1444859629
  39. 39.  Intel released • F200 in 2015 • SR300 in 2016  Both create high quality 3D depth video stream  SR300 adds IR laser projector, fast VGA, shorter exposure time, dynamic motion up to 2m/second  Applications • Gesture interfaces, augmented reality • Robotics, 3D scanning • Driverless vehicles, drones https://software.intel.com/en-us/articles/a-comparison-of-intel-realsensetm-front-facing-camera-sr300-and-f200
  40. 40.  What do numbers say about Moore’s Law? • Microprocessors and flash Memory • Graphic processors and 3D camera chips • Wireless chips, Data Centers  What does this mean for: • Smart phones and Biometrics? • Big Data, Internet of Things?  Alternatives to Silicon and von Neumann • Organic transistors • Carbon nanotubes, Graphene • Atomic transistors • Synapse, AHaH • Quantum computers
  41. 41.  Reductions in feature sizes lag microprocessors by many years  Thus are slower, and have fewer cores, partly because lower power consumption is required  Parallel data transfers being implemented for cellular and WiFi, just like multi-cores of GPUs  In future,WiFi will dominate smart phones, with most processing in the cloud • More bio- and environmental sensors • More image sensors for 3D reconstruction • More data mining of user behavior
  42. 42. Speed of PC Micro- processors W
  43. 43. Improvements are Still Occurring Along Many Dimensions Source: International Technology Roadmap for Semiconductors
  44. 44. Other Sources are also Optimistic (International Solid State Circuits Conference)
  45. 45.  LiFi = Light Fidelity  LiFi will become economical in near future as LEDs become cheaper (see Session 10)  LiFi enables much faster speeds • Twenty times!  Much lower power consumption • 100 times!  Previously required line of sight  But new forms of LiFi can handle reflections
  46. 46.  What do the numbers say about Moore’s Law? • Microprocessors and Flash memory • Graphic processors and 3D camera chips • Wireless chips, Data Centers  What does this mean for: • Smart phones? • Software and Big Data?  Alternatives to Silicon and von Neumann • Organic transistors • Carbon nanotubes, Graphene • Atomic transistors • Synapse, AHaH • Quantum computers
  47. 47.  Everything is going to cloud  Data centers are more efficient than stand-alone computers  Computers operate 24/7 in data centers  New architectures and new software are making them even more efficient
  48. 48.  Virtualization • split computers into virtual machines, each with OS and programs • VM ware has been largest success • Enables higher work loads Other methods such as • Containerization • Orchestration http://www.cisco.com/c/en/us/solutions/collateral/service- provider/global-cloud-index-gci/Cloud_Index_White_Paper.html progress without profits, economist, sept 19, 2015. P. 61
  49. 49.  New architectures and software enable faster analysis of unstructured data  GFLOPS (giga-floating operations per unit)/Watt • 0.4 in 2010 to 67.8 in 2021  Bandwidth • 1 Gb/second in 2010 to 100 Gb/sec in 2021  In combination with smart phones and WiFi, these rapid improvements will enable • many new types of applications • automation of many functions • Machine Learning Source: International Technology Roadmap for Semiconductors
  50. 50.  IBM has made quantum computing available on the cloud (2016) http://flip.it/chi2D  This has much higher speeds, for some applications  Speeds continue to rise and costs continue to fall Will quantum computing dominate cloud computing in 10 years?  See http://www.slideshare.net/Funk98/superconductivity-15131282 and a few slides at the end of tonight
  51. 51.  What do numbers say about Moore’s Law? • Microprocessors and Flash memory • Graphic processors and 3D camera chips • Wireless chips, Data Centers  What does this mean for: • Smart phones? • Software and Big Data?  Alternatives to Silicon and von Neumann • Organic transistors • Carbon nanotubes, Graphene • Atomic transistors • Synapse, AHaH • Quantum computers
  52. 52. The Smart Phone Will be Used Much More than PCs
  53. 53. Internet Advertising has Moved to the Internet
  54. 54. And Will Now Move to the Smart Phone
  55. 55. to Smart Phones
  56. 56. Though Data is Still Expensive for Poor Countries it will get cheap, let’s look at how this occurred and will continue to occur
  57. 57.  How did they become economically feasible? • What are main costs for smart phones? • What determines their performance? • What levels of performance and cost were needed in the microprocessors and memory for smart phones to become feasible? • When did this happen?  What happens as the components become better and cheaper? • What types of high performance phones? • What types of low-cost phones?
  58. 58. Type of Product Final Assembly Standard Components1 Number of Data Points Average (%) Number of Data Points Lower Estimate for Average2 (%) Smart Phones 28 4.2% 26, 28 76%, 79% Tablet Computers 33 3.1% 33, 33 81%, 84% eBook Readers 6 4.9% 6, 9 88%, 88% Game Consoles 2 2.4% 2, 2 64%, 70% MP3 Players 2 3.4% 2, 9 74%, 75% Large Screen Televisions 2 2.4% 2, 2 82%, 84% Internet TVs 2 5.7% 2, 2 57%, 61% Google Glass 1 2.7% 1, 1 62%, 64% Cost Breakdown for Electronic Products 1 Values as a percent of total and material costs 2 Excludes mechanical components, printed circuit boards, and passive components
  59. 59. Type of Product # of Data Point Mem ory Micro- Proc- essor Displ ay Camer a Connecti vity, Sensors Bat- tery Power Mgmt Phones 23 15% 22% 22% 8.2% 7.9% 2.3% 3.8% Tablets 33 17% 6.6% 38% 2.9% 6.3% 7.3% 2.5% eBook Readers 9 10% 8.1% 42% .30% 8.3% 8.3% Not available Game Console 2 38% 39% none none Not available none 5.8% MP3 Players 9 53% 9% 6% none Not available 4% 3.5% TVs 2 7% 4.0% 76% none Not avail. none 3.0% Internet TVs 2 16% 31% none none 10.5% none 3.5% Google Glass 1 17% 18% 3.8% 7.2% 14% 1.5% 4.5% Contribution of “Standard Components” to Costs of Selected Electronic Products
  60. 60. Measure iPhone iPhone 3G iPhone 4 iPhone 5 iPhone 6 Operating System 1.0 2.0 4.0 6.0 8.0 Flash Memory 4, 8, 16GB 8 or 16GB 8, 16, 64GB 16, 32, 64GB 16, 64, or 128GB DRAM 128MB 128MB 512MB 1GB 1GB Application Processor 620MHz Samsung 32-bit RISC 1 GHz dual- core Apple A5 1.3 GHz dual-core Apple A6 1.4 GHz dual-core Apple A8 Graphics Processor PowerVR MBX Lite 38 (103 MHz) PowerVR SGX535 (200 MHz) PowerVR SGX543MP3 (tri- core, 266 MHz) PowerVR GX6450 (quad-core) Cellular Processor GSM/GPRS/ EDGE Previous plus UMTS/HSDPA 3.6Mbps Previous plus HSUPA 5.76Mbps Previous plus LTE, HSPA+, DC-HSDPA, 4.4Mbps Previous plus LTE- Advanced, 14.4Mbps Display resolution 163 ppi (pixels per inch) 326 ppi 401 ppi Camera resolution Video speed 2 MP (mega-pixels) 5 MP 30 fps, 480p 8 MP 30 fps at 1080p 8 MP 60 fps at 1080p WiFi 802.11 b/g 802.11 b/g/n 802.11 a/b/g/n 802.11 a/b/g/n/ac Other Bluetooth 2.0 GPS, compass, Bluetooth 2.1, gyroscope GPS, compass, Blue- tooth 4.0, gyroscope, voice recognition Previous plus finger- print scanner, near- field communication Evolution of iPhone in Terms of Measures of Performance Fps: frames per second 480p: progressive scan of 480 vertical lines
  61. 61.  What Levels of Performance and cost were needed in each Component? • Memory • Microprocessors • displays
  62. 62.  760 songs, 4000 pictures (4 megapixel JPEG), four hours of video, or 100 apps/games, or some combination  Equal usage • 190 songs • 1000 pictures • one hour of video • 25 apps/games  Was this necessary, would 1GB have been sufficient?
  63. 63. The Average User Downloaded 58 Apps or a Significant Fraction of Memory Available in 4GB Phone
  64. 64.  Cost of iPhone 5 varies from $207 to $238 depending on flash memory capacity • 16GB, 32GB, or 64GB  For iPhone 4s, costs range from $196 to $254 for same range in flash memory  For iPhone 3GS, 16GB of flash memory are $24 thus suggesting costs for same change in capacity would range from $179 to $251  In percentage terms, same changes in flash memory capacity led to increase of 40% in iPhone 3GS and increase of only 15% in iPhone 5
  65. 65.  Needed sufficient processor to have 3G network capability  Needed sufficiently inexpensive processor  What about camera,WiFi, gyroscope, other sensors?
  66. 66.  What components are experiencing rapid improvements?  Can they tell us something about “next big thing”  Improvements will probably continue in • Microprocessor, memory and other ICs • MEMS, bio-electronic ICs • Displays including flexible ones • Lasers, LEDs, photo-sensors, and other sensors • Speeds of cellular networks and WiFi • New forms of user interfaces (gesture, touch)  Open source software is becoming more available
  67. 67.  New features, perhaps for high-end phones • Health care: phones monitor health (heart rate, brain wave, blood pressure) using sensors • Home automation: use phones to control homes • Voice-activated assistant for unfree hands (e.g., drivers) • Engineering assistant: environmental data (temperature, pressure, air and water quality)  Different phones for different applications? • Or one phone does everything? • Specific phones must be defined for specific users
  68. 68.  What are entrepreneurial opportunities?
  69. 69. Will Apple be Disrupted? Apple has highest prices Does it Deserve High Prices? Why might Apple be disrupted?
  70. 70. Type of Product # of Data Point Mem ory Micro- Proc- essor Dis play Cam- era Connect- ivity, Sensors Bat- tery Power Mgmt Phones 23 15% 22% 22% 8.2% 7.9% 2.3% 3.8% Includes WiFi Contribution of “Standard Components” to Phone Costs Can any of these components be eliminated to create a much cheaper phone (and perhaps a much cheaper phone service? Are there improvements in components and/or technological trends that can help us think about components to eliminate? How about a Low-End Phone: what might emerge?
  71. 71.  Can microprocessors and memory be eliminated to create low-end phones that bypass network providers (SingTel, StarHub) • Lower cost phones • Lower cost services  If WiFi is main connection and it works good enough • Can we reduce memory capacity? • Can we reduced performance of application processor?  Lower resolution cameras, displays, and other components will also reduce costs  How might open source software enable lower costs?
  72. 72.  What are the entrepreneurial opportunities?
  73. 73.  Concept of service • Combine WiFi routers into integrated services • Access cellular network when WiFi isn’t available • How long will cellular service providers continue selling network space to new entrants?  In U.S., Republic Wireless, Scratch Wireless, FreedomPop, Google, soon cable companies. In France, service called “Free”  But Korea may be leader – large use of WiFi, great phones from Samsung, and great mobile content and services  In India, Uber will Offer Free Wi-Fi in taxis, Google also (http://blogs.wsj.com/digits/2016/01/22/google-brings-wi-fi-to-mumbais-railway-station/?mod=ST1) http://www.wsj.com/articles/google-unveils-wireless-service-called-project-fi-1429725928; http://nyti.ms/1AFMiFW; http://nyti.ms/1HI2BkW; http://www.economist.com/news/business/21654602-wi-fi-first-technology-will-be-great-consumers-disruptive-mobile-firms-change http://www.wsj.com/articles/uber-to-offer-india-passengers-free-wi-fi-1440136803
  74. 74.  Messaging Apps are most widely used function on most smart phones  Some used for more than messaging • Particularly in China • For payments, hotel, rides sharing, food delivery, restaurant  Voice recognition is future (Session 7) http://www.wsj.com/articles/the-future-of-texting-e-commerce-1451951064 http://www.economist.com/news/business-and-finance/21696477-market-apps-maturing-now-one-text-based-services-or-chatbots-looks-poised
  75. 75. http://www.wsj.com/articles/global-telecoms-struggle-to-answer-challenge-from- messaging-apps-1464038370
  76. 76.  $40 billion in apps downloaded in 2015 (100 billion apps available)  20 most successful developers grab nearly half of revenues  For using apps to order things, • services must understand chat, thus AI is required  Interact with bank accounts, get news, do bookings, find sports shoes, respond to health care questions, interact with doctors
  77. 77.  Hard to switch between messaging apps • Inconvenient for many users • Hard to move data between them  Similar to incompatible word processing, spreadsheet and power point software in 1980s  Microsoft Windows integrated them in late 1980s  Whose apps will become the ‘hub’ for many services? FB, Uber, or somebody else?  What will be roles for AI and bots?
  78. 78. Better Apps Support Growth in Demand Economy
  79. 79. 1. Finger print 2. Palm veins 3. DNA 4. Iris recognition 5. Facial recognition 6. Voice recognition 7. Signature recognition 8. Palm print 9. Hand geometry 10.Retina scan 11.Ordure/Scent
  80. 80.  Google will begin testing an alternative to passwords next month • With a goal to eliminate complicated logins for  Google introduced new feature to developers at company’s I/O conference in May 2016  Called the Trust API  To be initially tested with several very large financial institutions http://flip.it/gPoep
  81. 81. 89 Biometric Technology Accuracy Cost Device Required Social Acceptability DNA High High Test Equipment Low Iris recognition High High Camera Medium -Low Retina scan High High Camera Low Facial recognition Medium - Low Medium Camera High Voice recognition Medium Medium Microphone, telephone High Hand geometry Medium - Low Low Scanner High Finger print High Medium Scanner Medium Signature recognition Low Medium Optic pen, touch panel High http://kaitleencrowe.com/2015/01/22/bi • Compared with other Bio Technologies, Finger Print is the best choice
  82. 82. How Fingerprint are acquired? Source: http://360biometrics.com/faq/fingerprint_scanners.php Optical Sensor Ultrasonics with RF Sensor Optical Sensor module Capacitive Sensor Thermal Sensor
  83. 83. 91 Fingerprint Sensors Comparison Optical Capacitive Ultrasound Size Relatively big and require camera Can embed into small devices Can embed into small devices Method Image capture RF Field RF Field Cost Middle Low High Accuracy May be affected by dirt or water May be affected by dirt or water Will not be affected by dirt or water Working Current 120 mA 200 mA 6 µA Source: http://yourbusiness.azcentral.com/comparison-fingerprint-scanners-27754.html http://artofcircuits.com/product/optical-fingerprint-sensor-module-fpm10a http://www.techshinobiometrics.com/products/fingerprint-identification-products/fingerprint-oem-modules/ http://www.sonavation.com/ultrasound-biometric-sensor
  84. 84.  The most commonly used are: 1. Minutiae matching (commonly used) 2. Pattern matching http://www.biometric-solutions.com/solutions/index.php?story=fingerprint_recognition http://biometrics.mainguet.org/types/fingerprint/fingerprint_algo.htm
  85. 85. 93http://biometrics.mainguet.org/types/fingerprint/fingerprint_sensors_manufacture.htm Average Selling Price of Fingerprint Sensors are Dropping
  86. 86. 94 https://www.tractica.com/newsroom/press-releases/fingerprint-readers-in-mobile-devices-to-surpass-1-billion-unit-shipments-annually-by-2021/ Demand for Finger Print Sensor is Growing
  87. 87.  What are the entrepreneurial opportunities?
  88. 88.  Mobile phones Mobile phone banking, e-commerce, stock trading Banking Cars Houses  thumb drives Offices
  89. 89. 97 Applications for Biometrics Payment $$$ Public Transport Convenient Stores Vending Machines Computers
  90. 90.  What do numbers say about Moore’s Law? • Microprocessors and Flash memory • Graphic processors and 3D camera chips • Wireless chips, Data Centers  What does this mean for: • Smart phones? • Software and Big Data?  Alternatives to Silicon and von Neumann • Organic transistors • Carbon nanotubes, Graphene • Atomic transistors • Synapse, AHaH • Quantum computers
  91. 91. Routine Non-Routine Manual Assembly line: being automated Nail Salon; won’t be automated Cognitive Many Jobs are being Auto- mated!!!! Hardest to Automate. Where you want to be Easier to make computers exhibit adult (calculations) than child (perception and mobility) behavior Low-level sensorimotor still require much computational resources
  92. 92.  What types of software and other automation should be used?  What jobs should be done by humans?   Not a question just for MIS  A question for top management  Want low costs, high quality, fast response and many other things  Let’s look at what is happening
  93. 93.  Apple has never been a big player in the $2 trillion annual spending on workplace technology  But now Apple is inviting firms to develop such programs (working with Cisco and others)  Encouraging providers of complementary apps to collaborate  20% of tablets used globally in 2018 will be owned by businesses, up from 12% in 2014  High expectations for retailers, restaurants, sales presentations, government offices http://www.wsj.com/articles/with-ipad-sales-cooling-apple-leans-on-partners-1439422814
  94. 94. Slack is more like messaging apps Focus on people or projects, not time More like a conversation! Easy to understand internal vs. external Easier to see most of your conversations in one view
  95. 95.  Slack allows third parties to build apps • Order lunch is one • Other small tasks will be automated • Intelligent personal assistant for groups?  Will it absorb functions from other software?  Will similar things happen with other enterprise software? • Sales software? • Marketing software?
  96. 96. Zenefits Changed Human Resource Management, Replacing Benefits Brokers
  97. 97.  Examples: journalism, accounting, engineering, architecture, legal, education  Previously face to face and customized services; • Now mass customization and telepresence with computers  Software automates work, transforms work (enabling people to be more proactive), and enables customers to bypass professionals • Nurses do doctors work • Paralegals do lawyers work • Students learn without teachers See The Future of the Professions, Daniel and Richard Susskind)
  98. 98.  The number of articles written by robots is growing rapidly  Associate Press uses Automated Insights’ Wordsmith platform to create more than 3,000 financial reports per quarter  Kristian Hammond, Narrative Science’s co- founder, estimates that 90 percent of news could be algorithmically generated by the mid-2020s, much of it without human intervention  Just input a few facts and let the algorithm write the paper http://www.nytimes.com/2015/03/08/opinion/sunday/if-an-algorithm-wrote-this-how-would-you-even-know.html?rref=opinion &module=Ribbon&version=origin&region=Header&action=click&contentCollection=Opinion&pgtype=article
  99. 99.  Accounting continues to become more automated, particularly tax compliance • Cash flow done with QuickBook, Xero, Kashflows • Internal accounting work focuses on problem solving, like collecting payments  Tax work changing from compliance to planning • Compliances done with TurboTax, H&R Block, At Home TaxACT • But even planning is threatened; planning and compliance are different sides of same coin • Compliance works forward from rules and regulations while planning works backwards from these rules and regulations
  100. 100.  Continuous auditing is next step • Samples (chosen by heuristics) used in past to minimize calculations • Big Data enables software to analyze 100% of the data, and continuously  Governments use software and big data to assess tax returns, estimate chances of fraud • Many require original electronic records, as opposed to paper • Electronic invoices are harder to fake than are paper ones
  101. 101.  Better software continues to emerge, enabling • more high level design work • more design options can be considered  Lower cost software also becoming available • Enables more design options to be considered by small firms, individuals, emerging economies • For example, water flow analysis for fish farms  Can you think of other examples? • Maybe your job should be automated • Better to provide the solution than to have your job eliminated by someone else’s solution
  102. 102.  Software eliminates wooden models • Use CAD and CAE ( VR and AR discussed in Session 8) • Software creates more design possibilities; input objectives and designs are proposed • Computations carried out to test more radical designs  Cheap forms of software are emerging • Individuals use software to become their own architects • Open source designs becoming widely available: Sketchup3d has one million designs while Grab Cad has 660,000 designs. Designs shared on many sites (even Pinterest)  Different people do different tasks • Less need for vertical hierarchy. Use network model • Can probably have designs checked by city governments
  103. 103.  Most work involves paper work  Filling out forms, asking questions • Much of this can be done with online questionnaires  Large cases involves lots of research, which can now be done with computer searches  Computers and artificial intelligence will continue to eliminate legal jobs
  104. 104. ABA: American Bar Association BLS: Bureau of Labor Statistics http://www.mybudget360.com/law-school-bubble -law-tuition-law-degrees-in-bubble-applications-down/ Graduates
  105. 105.  Big move towards Pre-Fab/Modular Housing • To reduce construction time, http://www.dirtt.net/ • No screws, nails, snap fits • Change dimensions of one part, CAD system automatically changes dimensions on other parts • Uses ICE software, borrowed from video games • Easy to reconfigure designs and rooms  Can augmented reality software help? See session 8
  106. 106.  Pre-fab Housing Method is one reason some Chinese companies can construct large buildings in less than one month  Many such articles and videos but here are two of them  http://www.theguardian.com/world/2015/apr/30/chinese- construction-firm-erects-57-storey-skyscraper-in-19-days  http://www.dailymail.co.uk/news/article-2083883/Ark-Hotel- construction-Chinese-built-30-storey-hotel-scratch-15-days.html
  107. 107.  Many ways to learn without schools and teachers  More than 10 million unique visitors each month in 2014 for Khan Academy  700,000 education related videos on YouTube  750,000 educational apps installed in 2014  70 million unique visitors to slideshare 2014  New philosophy for education: need guide on the side, not sage on the stage See these slides for more details: http://www.slideshare.net/funk97/is-a-new-business-model-for-universities-needed
  108. 108.  What do numbers say about Moore’s Law? • Microprocessors and Flash memory • Graphic processors and 3D camera chips • Wireless chips, Data Centers  What does this mean for: • Smart phones? • Software and Big Data?  Alternatives to Silicon and von Neumann • Organic transistors • Carbon nanotubes, Graphene • Atomic transistors • Synapse, AHaH • Quantum computers
  109. 109. Even Museums are Talking about Big Data
  110. 110. Sales Software They mine sales staff emails, calendars, social- media feeds, as well as news articles and customer databases Proposes potential customers, ranking them in order of likelihood to buy Can tell sales staff when client is reading their email
  111. 111.  Google (and others) use data to determine the ads that are displayed when you • use search engine • visit website  Ad sites compete to provide ads each time you click • They propose ads and prices based on your cookies. • Amazon and other web sites also use this data to adjust prices depending on customer characteristics, time of day and other things Humans Need Not Apply: A Guide to Wealth and Work in the Age of Artificial Intelligence, August 4, 2015, Jerry Kaplan
  112. 112.  Mobile ads work differently • InMobi offers questions based on all your on-phone activity • Can somebody do this better?  What about ad blocking software? • Many startups provide this software • Is there a better way to do this? • One that can satisfy both users and advertisers? • How will this war end? Block Shock, Economist, June 6, 2015, p. 52
  113. 113.  In one study of 140,000 test emails, researchers at Verizon reported that 23% of recipients opened and 11% clicked on attachments  Training doesn’t help  Solution is to automate around them • Better email filtering • Something else? http://www.wsj.com/articles/how-to-improve-cybersecurity-just-eliminate-the-human-factor-1453125602?mod=LS1
  114. 114.  Many billion dollar club startups use Big Data • Peer-to peer lending: Lufax, Prosper Marketplace, Social Finance, Funding Circle, Lending Club • E-commerce payment: Klarna • Other: Zhong an Online (insurance), Hanhua Financial (credit guarantor), Credit Karma (credit scores), Sunrun (solar leasing)  But what is next?  What about smart phone apps?
  115. 115.  Many billion dollar club startups use Big Data • Peer-to peer lending: Lufax, Prosper Marketplace, Social Finance, Funding Circle, Lending Club • E-commerce payment: Klarna • Other: Zhong an Online (insurance), Hanhua Financial (credit guarantor), Credit Karma (credit scores), Sunrun (solar leasing)  But what is next?  What about smart phone apps?
  116. 116.  As firms manage more money, aren’t they becoming like banks?  Klarna • Offers simple payment system (e-mail identifiers) for smart phone users that is easier than inputting credit cards • 65,000 online merchants, 45 m users • It allows users to pay after receiving products and Klarna assumes the risk • Klarna judges risk based on Big Data • Now it is extending loans (also PayPal is) Getting more ambitious, Economist, Feb 6, 2016
  117. 117.  Evaluates creditworthiness of prospective borrowers  Big Data analysis finds patterns of credit worthiness and apps look for this creditworthiness  Very popular in Africa where mobile banking is popular http://www.wsj.com/articles/lending-startups-look-at-borrowers- phone-usage-to-assess-creditworthiness-1448933308
  118. 118.  In U.S. poor people often paid by checks • but banks charge a lot for checks when bank account balances are small  Governments should • pay workers in another way • require companies to pay in another way  What should the other way be? • Messaging apps? • Other mobile phone apps? • Security may be the biggest issue
  119. 119.  Individuals are coding decision rules at home  More than 170,000 people enrolled in a popular online course,“Computational Investing” taught at Georgia Institute for Technology  What types of decision rules?  For example, if stock volumes hit minimum threshold • and price crosses above 200-day moving average, buy • And price falls below 200-day moving average, sell  http://www.wsj.com/articles/an-algo-and-a-dream-for-day-traders-1439160100
  120. 120.  Two big challenges • High regulation • High capital requirements, which bring risk  Solutions • Lemonade uses peer-to peer approach, like FinTech banks  Get friends to adopt risk, since they understand situation of friends • Metromile tracks mileage  More mileage, higher the risk  Many others (Progressive, Aviva) track braking and speed Against the Odds, Economist, January 30, 2016, P. 59
  121. 121. Satellite Data - utilize image data from orbiting satellites to measure number of cars in Walmart parking lots or farm health based on color of crops. Web/App/Social Media Data –mine social media or use data firehoses from web/ mobile to understand what’s happening in world or how people are interacting with their devices. Weather Data –developing weather models and utilizing more sensors to get better localized data or improve weather forecasting. Location/Foot Traffic Data –use different means to understand where consumers are going by measuring foot traffic via check-ins, video analysis, etc. https://www.cbinsights.com/blog/alternative-data-startups-market-map-company-list/?utm_source=CB+Insights+Newsletter&utm_ campaign=5442a1353d-Top_Research_Briefs_5_21_2016&utm_medium=email&utm_term=0_9dc0513989-5442a1353d-86622821
  122. 122. Alternative Credit - new credit models that utilize sources of alternative data (like mobile usage). Credit Card Transactions – use anonymous aggregate transaction data to understand trends in consumer purchasing habits. Alternative Data Monetizers/Aggregators – companies who pay for access to individual data streams which become more valuable in a bundle, and then sell those packages to investors Local Prices – what’s happening to prices and inflation by aggregating data from ground-level sources. https://www.cbinsights.com/blog/alternative-data-startups-market-map-company-list/?utm_source=CB+Insights+Newsletter&utm_ campaign=5442a1353d-Top_Research_Briefs_5_21_2016&utm_medium=email&utm_term=0_9dc0513989-5442a1353d-86622821
  123. 123.  E-commerce sites vary prices by time, day, and location  Trying to maximize profits through small changes in prices  Some of this is personalized pricing  Some of this is time of day pricing  Can this be applied to physical stores?  What is next? http://www.wsj.com/articles/now-prices-can-change-from-minute-to-minute-1450057990
  124. 124. Uber’s System Uber’s System Not Working Working
  125. 125. • Dynamic pricing for parking • Price changes over day and different quarter • Drivers can check for vacant spots and price on smartphone to make better choices • Real-time physical location data can also help make better decisions about parking garages
  126. 126. Price Display by Smartphone In future, smart phones can replace traditional paper or electronic screen price tag and act as price display tool Customers obtain dynamic price as well as other information of goods by just tapping NFC tag or scanning the QR code
  127. 127.  What are the entrepreneurial opportunities?
  128. 128. 1. Smartphone design and mfg with NFC ID identification function 2. NFC chip manufacturing 3. Mobile Apps to read QR code & NFC tag, combined with price analysis function 4. Data processing platform that deals with mass dynamic pricing data analysis Future Opportunities for Price Display by Smartphone
  129. 129.  Google, IBM, Facebook made their machine-learning software available for free under and open-source license • Google:TensorFlow system • IBM: SystemML  They want their systems to be • tested, tuned, and adapted • built upon, improved, and extended Open source is necessary to attract academics http://blogs.wsj.com/digits/2015/11/09/why-google-is-willing-to-give-away-its-latest-machine-learning-software/?mod=ST1 http://www.wsj.com/articles/ibm-turns-up-heat-under-competition-in-artificial-intelligence- 1448362800?mod=WSJ_TechWSJD_NeedToKnow
  130. 130.  Computers have beaten best chess and Jeopardy players  Computers can help doctors diagnose patients  Computer matches medical knowledge with patient’s symptoms, medical histories with test results • formulates diagnosis and treatment plan • Doctors cant read all journals nor remember everything they read  Discussed more in Session 6 Sources: The Second Machine Age: Work, Progress, and Prosperity in aTime of Brilliant Technologies, Erik Brynjolfsson, Andrew McAfee http://www.research.ibm.com/cognitive-computing/watson/watsonpaths.shtml#fbid=NAFH6hHnYVY
  131. 131.  Learning about my music likes, partly through my friends likes  Searching through my photos • find photos that match “wedding” and “mom”
  132. 132.  What do the numbers say about Moore’s Law? • Microprocessors and flash Memory • Graphic processors and 3D camera chips • Wireless chips, Data Centers  What does this mean for: • Smart phones and Biometrics? • Big Data, Internet of Things?  Alternatives to Silicon and von Neumann • Organic transistors • Carbon nanotubes, Graphene • Atomic transistors • Synapse, AHaH • Quantum computers
  133. 133.  Built from organic molecules rather than silicon  Advantages • greater flexibility • lower manufacturing temperature (60-120° C) • lower-cost processes such as roll-to roll printing  Disadvantages • lower mobility and switching speeds compared to silicon • usually do not operate under inversion mode  Current Market • Circuits for Electronic paper (e.g., e-Books), OLEDs and other displays  Future Market • Greater use of organic transistors in cases where flexible electronics are useful • Replacement of ICs
  134. 134. Huanli Dong , Chengliang Wang and Wenping Hu, High Performance Organic Semiconductors for Field-Effect Transistor, Chemical Commununications, 2010,46, 5211-5222
  135. 135. http://pubs.rsc.org/en/content/articlelanding/2010/cs/b909902f#!divAbstract
  136. 136.  Dramatically lower costs  But also lower performance  Other types of materials can also be printed • Conductive inks • Electronic paste  Also other applications for such materials
  137. 137.  What do the numbers say about Moore’s Law? • Microprocessors and flash Memory • Graphic processors and 3D camera chips • Wireless chips, Data Centers  What does this mean for: • Smart phones and Biometrics? • Big Data, Internet of Things?  Alternatives to Silicon and von Neumann • Organic transistors • Carbon nanotubes, Graphene • Atomic transistors • Synapse, AHaH • Quantum computers
  138. 138. Very high conductivities In medium term, can be used in channel area (under gate) in place of silicon for faster transistors In long term, can they be designed with different properties (e.g., conductors, insulators, semiconductors) so that transistors can be built with them
  139. 139. Improvements in Purity of CNTs (and Increases in Density) Source: Electronics: The road to carbon nanotube transistors, Aaron D. Franklin Nature 498, 443–444 (27 June 2013)
  140. 140. IBM Says they are five times faster and will be ready around 2020 when feature lengths reach 5nm (now 14 nm) • Built on top of silicon wafers • Each transistor uses six nanotubes lined up in parallel to make a single transistor • Challenge is to make them self-assemble Nantero has shipped samples of nanotube based memory (NRAM) • Produced in CMOS fabs (20 ns access times) Source: Technology Review, http://nextbigfuture.com/2014/07/ibm-says-nanotube-transistors-chips.html#more http://nantero.com/mission.html; http://blogs.wsj.com/digits/2015/06/02/carbon-nanotube-chips-spark-investment/ http://www.nytimes.com/2015/10/02/science/ibm-scientists-find-new-way-to-shrink-transistors.html?_r=0
  141. 141. Graphene Also very high conductivities In short term replace silicon with graphene in channel area In long term combine graphene with other ultra-thin materials
  142. 142.  As of April 2013, >10 materials found; some can be integrated with Graphene or each other  Boron nitride (insulator) has been fabricated in one-atom sheet as has Molybdenum Sulfide • Molybdenum Sulfide is semiconductor, Boron Nitride is insulator, Graphene is for interconnect • Together one atom thick flash memory devices have been constructed  More recently (April 2015), three-atom thick semiconducting films (transition metal dichalcogenide) with wafer-scale homogeneity have been constructed http://thessdreview.com/daily-news/latest-buzz/flash-memory-to-be-based-on-2d-materials-a-single-atom-thick/
  143. 143.  What do the numbers say about Moore’s Law? • Microprocessors and flash Memory • Graphic processors and 3D camera chips • Wireless chips, Data Centers  What does this mean for: • Smart phones and Biometrics? • Big Data, Internet of Things?  Alternatives to Silicon and von Neumann • Organic transistors • Carbon nanotubes, Graphene • Atomic transistors • Synapse, AHaH • Quantum computers
  144. 144. IBM created an array of 96 iron atoms that contain one byte of magnetic information in “anti-ferromagnetic” states. But making them is still a major challenge…………. Source: John Markoff, New Storage Device Is Very Small, at 12 Atoms NY Times, Jan 13, 2012 http://www.nytimes.com/2012/01/13/science/small er-magnetic-materials-push-boundaries-of- nanotechnology.html
  145. 145.  What do the numbers say about Moore’s Law? • Microprocessors and flash Memory • Graphic processors and 3D camera chips • Wireless chips, Data Centers  What does this mean for: • Smart phones and Biometrics? • Big Data, Internet of Things?  Alternatives to Silicon and von Neumann • Organic transistors • Carbon nanotubes, Graphene • Atomic transistors • Synapse, AHaH • Quantum computers
  146. 146.  This chip uses a million digital neurons and 256 million synapses to process information  Potential replacement for microprocessors  Requires completely new forms of computer architectures and software  For more details, see presentation on synaptic chips: http://www.slideshare.net/Funk98/neurosy naptic-chips
  147. 147. 159 Performance Improvements - IBM Cognitive Chip From MT5009 Group Presentation, Spring 2015 http://research.ibm.com/cognitive-computing/neurosynaptic-chips.shtml#fbid=tVSs3tKj1tw http://www.research.ibm.com/articles/brain-chip.shtml http://research.ibm.com/cognitive-computing/neurosynaptic-chips.shtml#fbid=i9UhV_HagUs http://www-03.ibm.com/press/us/en/pressrelease/44529.wss
  148. 148.  What do the numbers say about Moore’s Law? • Microprocessors and flash Memory • Graphic processors and 3D camera chips • Wireless chips, Data Centers  What does this mean for: • Smart phones and Biometrics? • Big Data, Internet of Things?  Alternatives to Silicon and von Neumann • Organic transistors • Carbon nanotubes, Graphene • Atomic transistors • Synapse, AHaH • Quantum computers
  149. 149. Limitations of von-Neumann Architecture
  150. 150. Memristors Are the Key to AHaH Computing Their Resistance Changes According to their History Widely Used Ones Become Less Resistive (i.e., Learning)
  151. 151.  What do the numbers say about Moore’s Law? • Microprocessors and flash Memory • Graphic processors and 3D camera chips • Wireless chips, Data Centers  What does this mean for: • Smart phones and Biometrics? • Big Data, Internet of Things?  Alternatives to Silicon and von Neumann • Organic transistors • Carbon nanotubes, Graphene • Atomic transistors • Synapse, AHaH • Quantum computers
  152. 152. http://nextbigfuture.com/2013/05/dwave-512-qubit-quantum-computer-faster.html; http://www.dwavesys.com/en/dev-tutorial-hardware.html Quantum Computers are Also Becoming Economically Feasible: See Session 10 on Superconductivity
  153. 153. Bit Energy = power consumed per clock period x number of active devices RSFQ: rapid single flux quantum, relies on quantum effects in superconducting devices Source: superconductivity web21, January 16, 2012. www.istec.or.jp/web21/pdf/12_Winter/E15.pdf Improvements in Power Consumption and Speed of Superconductors
  154. 154.  This is obviously a very difficult question…….  Will all chips have 3D layers of transistors or memory cells by 2020? How many layers of transistors or memory cells by 2025?  Will MRAM, PCM, ReRAM, or FeRAM replace flash memory and which one will win?  Will carbon nanotubes, graphene, or other ultra- thin materials be widely used in ICs by 2025 or 2030?  Will organic materials gain share from inorganic?  When might Synapse chips become widespread?
  155. 155.  Improvements in ICs, Computers, and Electronic Products are not over Improvements in ICs will continue at a rapid rate, but perhaps slower than in the past  New forms of Moore’s Law will Emerge  These improvements will enable better computers and other electronic products
  156. 156.  Rapid improvements in electronic products and the Internet are not over  Microprocessors may be slowing  But other components and Internet are not slowing  Smart phone and cloud computing are future  They will enable many new types of content and services • These new services will change the way work is done • And change the definition of a business • Big Data will become even more important

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