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Neurosynaptic chips Slide 1 Neurosynaptic chips Slide 2 Neurosynaptic chips Slide 3 Neurosynaptic chips Slide 4 Neurosynaptic chips Slide 5 Neurosynaptic chips Slide 6 Neurosynaptic chips Slide 7 Neurosynaptic chips Slide 8 Neurosynaptic chips Slide 9 Neurosynaptic chips Slide 10 Neurosynaptic chips Slide 11 Neurosynaptic chips Slide 12 Neurosynaptic chips Slide 13 Neurosynaptic chips Slide 14 Neurosynaptic chips Slide 15 Neurosynaptic chips Slide 16 Neurosynaptic chips Slide 17 Neurosynaptic chips Slide 18 Neurosynaptic chips Slide 19 Neurosynaptic chips Slide 20 Neurosynaptic chips Slide 21 Neurosynaptic chips Slide 22 Neurosynaptic chips Slide 23 Neurosynaptic chips Slide 24 Neurosynaptic chips Slide 25 Neurosynaptic chips Slide 26 Neurosynaptic chips Slide 27 Neurosynaptic chips Slide 28 Neurosynaptic chips Slide 29 Neurosynaptic chips Slide 30 Neurosynaptic chips Slide 31 Neurosynaptic chips Slide 32 Neurosynaptic chips Slide 33 Neurosynaptic chips Slide 34 Neurosynaptic chips Slide 35 Neurosynaptic chips Slide 36 Neurosynaptic chips Slide 37 Neurosynaptic chips Slide 38 Neurosynaptic chips Slide 39 Neurosynaptic chips Slide 40 Neurosynaptic chips Slide 41 Neurosynaptic chips Slide 42 Neurosynaptic chips Slide 43 Neurosynaptic chips Slide 44 Neurosynaptic chips Slide 45 Neurosynaptic chips Slide 46 Neurosynaptic chips Slide 47 Neurosynaptic chips Slide 48 Neurosynaptic chips Slide 49 Neurosynaptic chips Slide 50 Neurosynaptic chips Slide 51 Neurosynaptic chips Slide 52 Neurosynaptic chips Slide 53 Neurosynaptic chips Slide 54 Neurosynaptic chips Slide 55 Neurosynaptic chips Slide 56 Neurosynaptic chips Slide 57 Neurosynaptic chips Slide 58 Neurosynaptic chips Slide 59 Neurosynaptic chips Slide 60 Neurosynaptic chips Slide 61 Neurosynaptic chips Slide 62 Neurosynaptic chips Slide 63 Neurosynaptic chips Slide 64 Neurosynaptic chips Slide 65
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These slides use concepts from my (Jeff Funk) course entitled analyzing hi-tech opportunities to analyze how Neurosynaptic chips are becoming economic feasible for supercomputing applications. Neurosynaptic chips use a different architecture, one that mimics the brain with neurons and synapses. These neurons and synapses are built with conventional architecture. This presentation describes the advantages and disadvantages of synaptic chips when compared to conventional chips and how rapid rates of progress in speed, density, and power efficiency are making synaptic chips economically feasible for supercomputing applications. The biggest disadvantage for synaptic chips is in software; a new operating system and application software are needed.

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Neurosynaptic chips

  1. 1. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES Neurosynaptic Chip Shen Xiaotong A0098666H Zhang Chunyan A0128996H Jiang Yaohong A0128997E Wang Junfang A0129148B Li Bing A0119251M 1 For other technologies see: http://www.slideshare.net/Funk98/presentations
  2. 2. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES Overview • Introduction • Comparison with Conventional Chips • Performance Analysis • Cost Analysis • Application • Conclusion 2
  3. 3. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES Overview • Introduction • Comparison with Conventional Chips • Performance Analysis • Cost Analysis • Application • Conclusion 3
  4. 4. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES Introduction Biological Neural System • Neurons and Synapses Signals: Electrical -> Chemical -> Electrical 4
  5. 5. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES Introduction Artificial Neural Network • Each neuron can perform non-linear operations. • The algorithm is designed to mimic the behavior of the biological neural network. • Currently, the algorithm is running on a normal PC. • New parallel computing chips can help to improve the learning process. – Neurosynaptic chips/brain-inspired chips – The hardware design also mimics the biology brain – Deep learning is also based on neural network 5
  6. 6. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES Introduction Artificial Neural Network • “We require exquisite numerical precision over many logical steps to achieve what brains accomplish in very few short steps.” - John von Neumann • A neural network is a massively parallel distributed processor made up of simple processing unit, which has a natural propensity for storing experiential knowledge and making it available for use. 6
  7. 7. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES Introduction Neurosynaptic System 7
  8. 8. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES Overview • Introduction • Comparison with Conventional Chips • Performance Analysis • Cost Analysis • Application • Conclusion 8
  9. 9. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES Comparison with Conventional Chips • Architecture • Complexity • Performance – Lower power – Denser package – Fast speed • Commercial Availability 9
  10. 10. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES Comparison with Conventional Chips Architecture Conventional Computer Brain Inspired Computer Architecture Von Neumann Neural Network Computing unit CPU Synaptic Chip (e.g. TrueNorth) Storing unit Memory Synaptic Chip (e.g. TrueNorth) Computing Serial (multiple cores) Massively Parallel Communication CPU <-> Memory Neurons <-> Neurons Advantage Processing (Logical, Analytical) Learning (Pattern Recognition) 10
  11. 11. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES Comparison with Conventional Chips Architecture • Processing and Storage are separated in CPU. • CPU is built for a linear process to handle linear sequence of events. • Synaptic chip integrates processing with storage. • Synaptic chip process the information in a massively parallel fashion. • Each neuron is able to process a piece of information and store it locally. • Synapses helps with data communication between neurons. • Synapses decides the connectivity between neurons and thus be able to rewire them. http://www.forbes.com/sites/alexknapp/2011/08/26/how-ibms-cognitive-computer-works/ 11
  12. 12. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES Comparison with Conventional Chips Architecture • Example: Converting the color image (480x360x3) to gray image – Gray value = (red + green + blue)/3; – CPU: 480x360 =172800 iterations of linear processing. – Synaptic chips, with 480x360x3 input neurons. 480x360 output neurons, For each output neurons, compute the gray value. One iteration of parallel processing • CPU is good at linear processing to make sure the logic sequence is correct. • Synaptic chip is good at massively parallel processing the image data. – Image processing – Pattern recognition – Network simulation http://www.forbes.com/sites/alexknapp/2011/08/26/how-ibms-cognitive-computer-works/ 12 ……
  13. 13. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES Comparison with Conventional Chips Complexity 13
  14. 14. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES Comparison with Conventional Chips Power Efficiency • > 1000 times as efficient as chips made with the conventional architecture. • In 2012, Sequoia IBM conventional supercomputer simulating brain using 500 billion neurons and 100 trillion synapses, running at 1/1500 of brain speed, requires 12 GW of power. • Each TrueNorth consumes 0.07w (Average 63mw, Maximum 72mw). • The same simulation requires 27.3~35 kw. Conventional Chips TrueNorth 50~100 w/(cm*cm) 0.02 w/(cm*cm) Source: The brain chip Robert F. Service Science 8 August 2014: 345 (6197), 614-616. 14
  15. 15. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES Comparison with Conventional Chips Power Efficiency • The efficiency of conventional computers is limited because they store data and program instructions in a block of memory that’s separate from the processor that carries out instructions. As the processor works through its instructions in a linear sequence, it has to constantly shuttle information back and forth from the memory store—a bottleneck that slows things down and wastes energy. • While synaptic chips work parallelly, and the information can be stored in numerous synaptic chips. The integration of processing and storing avoids data shuttling and makes computing more energy efficient. • About 176,000 times more efficient than a modern CPU running the same brain-like workload. http://www.extremetech.com/extreme/187612-ibm-cracks-open-a-new-era-of-computing-with-brain-like-chip-4096-cores-1-million-neurons-5-4-billion-transistors http://www.technologyreview.com/news/529691/ibm-chip-processes-data-similar-to-the-way-your-brain-does/ 15
  16. 16. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES Comparison with Conventional Chips Power Efficiency 16
  17. 17. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES Comparison with Conventional Chips Denser Package • Denser package – TrueNorth size is 4.3 cm^2 – In order to achieve the same computational performance of Sequoia IBM conventional supercomputer, it requires 400K~500K TrueNorth chips. – The size will be about 172~215 m^2. http://www.extremetech.com/extreme/131413-us-retakes-supercomputing-crown-with-16-petaflops-sequoia-china-promises-100-petaflops-by-2015 http://www.artificialbrains.com/darpa-synapse-program Sequoia supercomputer Synaptic chip wall 17
  18. 18. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES Comparison with Conventional Chips Faster Speed • Faster Speed – Multi-object detection and classification with 400-pixel-by-240-pixel three-color video input at 30 frames per second. – more than 160 million spikes per second (5.44 Gbits/sec) – TrueNorth vs Intel Core i7 CPU 950 with 4 cores and 8 threads, clocked at 3.07GHz (45nm process, 2009) www.sciencemag.org/content/345/6197/668/suppl/DC1 18
  19. 19. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES Comparison with Conventional Chips Commercial Availability http://www.cs.utah.edu/asplos14/files/Jeff_Gehlhaar_ASPLOS_Keynote.pdf 19
  20. 20. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES Comparison with Conventional Chips Commercial Availability Software CPU Synaptic Chip Language C,C++,Java, etc IBM Corelet Language Operating System Windows, iOS, Linux New operating system Application Office, Game, etc New application (Corelet Library) Algorithm General (include learning) Learning Algorithm Compiler Available New compiler Debugger Available New debugger Hardware CPU Synaptic Chip Dominant Design Intel, AMD No (TrueNorth and NPU are prototypes) Quantity 1 (Generally) ~50K (for Human-brain scale, but growing) Breakthrough Intel 4004 in 1971 IBM TrueNorth in 2014 (not on market) Manufacturing Transistor process Transistor process (45nm, 28nm) http://www.computerworld.com/article/2484737/computer-processors/ibm-devises-software-for-its-experimental-brain-like-synapse-chips.html http://www.research.ibm.com/software/IBMResearch/multimedia/IJCNN2013.corelet-language.pdf Amir, Arnon, et al. "Cognitive computing programming paradigm: a corelet language for composing networks of neurosynaptic cores." Neural Networks (IJCNN), The 2013 International Joint Conference on. IEEE, 2013. http://darksilicon.ucsd.edu/2012/assets/slides/13 20
  21. 21. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES Comparison with Conventional Chips Commercial Availability http://www.computerworld.com/article/2484737/computer-processors/ibm-devises-software-for-its-experimental-brain-like-synapse-chips.html http://www.research.ibm.com/software/IBMResearch/multimedia/IJCNN2013.corelet-language.pdf Amir, Arnon, et al. "Cognitive computing programming paradigm: a corelet language for composing networks of neurosynaptic cores." Neural Networks (IJCNN), The 2013 International Joint Conference on. IEEE, 2013. http://darksilicon.ucsd.edu/2012/assets/slides/13 Current Status Switching Point 21
  22. 22. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES Comparison with Conventional Chips Commercial Availability • However, the Qualcomm’s zeroth chip (Neural Processing Unit) may be incorporated into the new smartphone in 2015. – “The Zeroth software is being developed to launch with Qualcomm’s Snapdragon 820 processor, which will enter production later this year. The chip and the Zeroth software are also aimed at manufacturers of drones and robots.” http://www.technologyreview.com/news/535631/smartphones-will-soon-learn-to-recognize-faces-and-more/ https://www.youtube.com/watch?v=0D9I0SBGAPY https://www.youtube.com/watch?v=zxHIVWXVYi0 http://www.technologyreview.com/featuredstory/526506/neuromorphic-chips/ April 2014 NPU works together with CPU. 22
  23. 23. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES Comparison with Conventional Chips http://www.technologyreview.com/featuredstory/526506/neuromorphic-chips/ 23
  24. 24. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES Comparison with Conventional Chips http://research.ibm.com/cognitive-computing/neurosynaptic-chips.shtml#fbid=YQv631tFS2L 24
  25. 25. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES Overview • Introduction • Comparison with Conventional Chips • Performance Analysis • Cost Analysis • Application • Conclusion 25
  26. 26. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES 26 Performance Dimension Matrix Neurons Synapses Power Consumption Algorithm Neuromorphic System Artificial Neural Network ‘+’: denotes analysis has been done
  27. 27. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES 27 Performance Neuromorphic Systems Status 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 http://rt.com/usa/202323-brain-chip-drone-darpa/ http://www.artificialbrains.com/spinnaker#hardware Stanford Univesity Neurogrid (2009) HRL Neuorm orphic chip (2014) SpiNNake r HBP (2012) HiCANN HBP (2012) IBM TrueNort h (2014) Human Brain Neurons / Prototype 1.00E+06 2304 2.00E+07 1.20E+06 1.60E+07 2.00E+10 Synapses / Prototype 8.00E+09 292000 2.00E+10 3.00E+08 4.00E+09 2.00E+14 Power Consumptions (mW/cm^2) 50 120 1000 3000 20 10 mW/cm3 Manufacturing process (nm) 180 90 130 65 28
  28. 28. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES 28 Performance Neuromorphic Systems Status 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 http://rt.com/usa/202323-brain-chip-drone-darpa/ http://www.artificialbrains.com/spinnaker#hardware
  29. 29. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES 29 Performance IBM Cognitive Chip Basical Structure Prototype of IBM cognitive computer http://digi.163.com/14/0811/06/A3BKCVHG001618H9.html Defense Advanced Research Projects Agency (DARPA) SyNAPSE(Systems of Neuromorphic Adaptive Plastic Scalabe Electronics) Brain-inspired computer architecture, event-driven
  30. 30. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES 30 Performance IBM Cognitive Chip 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 Rate of Improvement of IBM TrueNorth Prototype Year 2013 2014 2017 2018 Human Brain Neurons 1.00E+06 1.60E+07 4.00E+09 1.00E+10 2.00E+10 Synapses 4.00E+09 1.00E+12 1.00E+14 2.00E+14 Power Consumptions (W) 5.45E+04 4000 1000 20
  31. 31. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES 31 Performance IBM Cognitive Chip 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 CAGR=1160% CAGR=531% CAGR=-172%
  32. 32. 32 MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES Artificial Neural Network ANN: To simulate the biological brain with nonlinear, dynamic, statistical mathematic model. Blue Brain Project •EPFL •IBM BlueGene L/P supercomputer •Open source simulation software NEURON www.neuron.yale.edu/neuron/ Blue Brain Project Cortical column (2006) Rat cortical column (2007) Equivalent honey bee brain (2012) Rat brain neocortical (2014) Full human brain (2023) Neurons 10000 10000 1.00E+06 2.10E+07 8.60E+10 http://bluebrain.epfl.ch/page-59952-en.html http://www.artificialbrains.com/blue-brain-project
  33. 33. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES 3333 Artificial Neural Network http://bluebrain.epfl.ch/page-59952-en.html http://www.artificialbrains.com/blue-brain-project CAGR=160%
  34. 34. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES 34 Performance Avatar Project Estimated 2045 Singularity!
  35. 35. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES Overview • Introduction • Comparison with Conventional Chips • Performance Analysis • Cost Analysis • Application • Conclusion 35
  36. 36. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES Cost Analysis 36 Per transistor Per Neurosynaptic Chip One artificial brain
  37. 37. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES 37 Cost Analysis neurons 5.4 billion transistors 10 102 14 102 IBM TrueNorth (2014) ×2.7E-08$ per transistor 145.8$ per chip ÷1.60E+07 neurons per chip 1250 chips synapses ÷4.00E+09 synapses per chip 50000 chips One artificial brain: 1250~50000 chips 182250$~7.29E+06$
  38. 38. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES 38 Cost Analysis Trend Prediction According to the cost trend of per transistor, we can predict the lowest cost for one transistor: 2.7E-08$ http://electroiq.com/petes-posts/2015/01/26/exponentially-rising- costs-will-bring-changes/
  39. 39. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES 39 Cost Analysis According to the Moore's Law, we can predicte the number of transistors in one chip in the future. year 2014 2016 2017 2018 2020 2022 2024 number 5.40E+9 1.08E+10 1.53E+10 2.16E+10 4.32E+10 8.64E+10 1.73E+11 number of transistors per chip
  40. 40. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES 40 year 2014 2017 2018 cost 145.8 413.1 583.2 Cost Analysis According to the price we assumed previousely, we can get the following result:
  41. 41. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES Cost Analysis According to the table below,we can calculate the interval number of chips: 41 Rate of Improvement of IBM TrueNorth Prototype Year 2013 2014 2017 2018 Human Brain Neurons 1.00E+061.60E+07 4.00E+09 1.00E+10 2.00E+10 Synapses 4.00E+09 1.00E+12 1.00E+14 2.00E+14 Power Consumptions (W) 5.45E+04 4000 1000 20
  42. 42. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES 42 Cost Analysis year 2014 2017 2018 low 1250 5 2 high 50000 200 4 The number of chips for one artificial brain 2013 2014 2015 2016 2017 2018 2019 10 1 10 2 10 3 10 4 10 5 year thenumberofchips low high
  43. 43. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES 43 Cost Analysis year 2014 2017 2018 low 1.82E+05 2065.5 1166.4 high 7.29E+06 82620 2332.8 Total cost for one artificial brain ($) 2013 2014 2015 2016 2017 2018 2019 10 4 10 5 10 6 10 7 10 8 year totalcost($) low high CAGR=-789% CAGR=-253%
  44. 44. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES Cost Analysis Reasons for cost Reduction of Neuromorphic chips -Increase in the number of manufacturers -Mass production -The emergence of new technology -Increase in demand -Invention of new materials with less cost -Cheaper and more accessible after market parts and repair -Multifunctional structures 44
  45. 45. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES Overview • Introduction • Comparison with Conventional Chips • Performance Analysis • Cost Analysis • Application • Conclusion 45
  46. 46. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES Application 46
  47. 47. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES Application Public safety Object detection  Roller robot  search-and-rescue robots  has 32 video cameras  beam back data from hazardous environments.  solar-powered leaf  detect changes in the environment  send out environmental and forest fire alerts. http://research.ibm.com/cognitive-computing/neurosynaptic-chips.shtml#fbid=JOW40Q37jNX http://asmarterplanet.com/blog/2014/08/introducing-brain-like-chip-revolutionize-computing.html 47
  48. 48. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES Application Public safety  Jellyfish Robot:  monitor shipping lanes for safety  sense tsunamis  environmental protection Based on its advantage:  smarter sensors built from these chips could bring the real-time capture and analysis of various types of data closer to the point of collection. http://research.ibm.com/cognitive-computing/neurosynaptic-chips.shtml#fbid=JOW40Q37jNX 48
  49. 49. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES Application Vision assistance Vision assistance for the blind Emulating the visual cortex, low-power, light-weight eye glasses designed to help the visually impaired could be outfitted with multiple video and auditory sensors that capture and analyze this optical flow of data. Image Processing http://research.ibm.com/cognitive-computing/neurosynaptic-chips.shtml#fbid=JOW40Q37jNX http://www.artificialbrains.com/darpa-synapse-program 49
  50. 50. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES Application Vision assistance  Synesthetic feedback Surround sound is used to indicate the location of point of interest and provide an audible guidance through the pathway.  Visual cues For users with residual sight, point of interest or obstacles can be highlighted by displaying either overlapping symbols or braille keywords. http://research.ibm.com/cognitive-computing/neurosynaptic-chips.shtml#fbid=JOW40Q37jNX http://www.artificialbrains.com/darpa-synapse-program 50
  51. 51. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES Application Vision assistance  Neuromorphic Vision Sensors Dynamic vision sensor, output activity-driven events, inspire new forms of machine vision and audition. These kinds of vision sensor can be used in such as robotics and surveillance. 51
  52. 52. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES Application Vision assistance Several neuromorphic chips connected serially by analogy to human visual system: can attend to interesting objects in the visual field. 52
  53. 53. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES Application health monitoring Thermometers that can smell for home health monitoring  Sensors combined with neuromorphic chips the in future medical devices could recognize odors from certain bacteria.  This is a hand-held thermometer for diagnosing minor illness or infection, It can smell what disease you have and notify you if a doctor visit is required. http://research.ibm.com/cognitive-computing/neurosynaptic-chips.shtml#fbid=JOW40Q37jNX 53
  54. 54. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES Application Future computing technology  super computer highly scalable computational challenges hierarchical storage-class memory interactive supercomputing at the exascale level The Neuromorphic Computing Platform should enable the development of prototype systems for prediction making, data mining, spatial and temporal pattern detection. 54
  55. 55. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES Application Future computing technology  super computer Weather forecast Oil exploringData mining Cosmological simulation 55
  56. 56. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES Application others  coversation flower A flower that can see and hear, recognizing people and responding to a conversation. It’s got a series of cameras and microphones in the center, 360-degree binocular vision and acoustic triangulation to localize speakers. Flower opens in response to energy and reciprocity of the conversation. can be used in Business meetings Conversation sensors could identify and understand voice and appearance to automatically generate transcripts. Speech recognition http://research.ibm.com/cognitive-computing/neurosynaptic-chips.shtml#fbid=JOW40Q37jNX 56
  57. 57. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES Application vision in the future How about using a brain chip in the hospital operating room? During exploratory surgery, doctors could perform real-time analysis of human tissue samples—reducing the need for tissue removal or for additional surgeries. Automakers could use the chip to help pilot the driverless cars of the future. neuromorphic chips could be integrated into smart phones to improve their visual and voice recognition. http://research.ibm.com/cognitive-computing/neurosynaptic-chips.shtml#fbid=JOW40Q37jNX http://www-03.ibm.com/press/us/en/pressrelease/41710.wss 57
  58. 58. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES Overview • Introduction • Comparison with Conventional Chips • Performance Analysis • Cost Analysis • Application • Conclusion 58
  59. 59. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES Conclusion • Geometrical Scale – The size scales down, the numbers scales up – Post 7-nm Challenge • Cognitive computing 59
  60. 60. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES Conclusion Geometrical Scale • The size scales down, the numbers scales up Graph source: www.iue.tuwien.ac.at/phd/filipovic/node20.html 60
  61. 61. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES Conclusion Post 7-nm Challenge • The imminent breakdown in conventional chip operation and chip materials as we shrink transistor gates from today's 14nm process size to 10nm and 7nm. • Beyond 7nm gate current leakage • The limit of silicon technologies • New directions for next generation of computers: – New materials: Carbon nanotubes replacing CMOS – New architecture: Neurosynatpic chips http://www.theregister.co.uk/2014/07/09/ibm_3billion_megabuck_r_and_d/ 61
  62. 62. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES Conclusion Cognitive Computing 62 http://www.kurzweilai.net/ibm-unveils-cognitive-computing-chips-combining-digital-neurons-and-synapses
  63. 63. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES Conclusion Cognitive Computing • The machine would eventually smarter than human. 63 http://p9.hostingprod.com/@modha.org/blog/2013/06/ibm_mapping_the_path_to_cognit.html
  64. 64. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES THANK YOU! 64
  65. 65. MT5009 ANALYZING HI-TECHNOLOGY OPPORTUNITIES Q&A 65
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These slides use concepts from my (Jeff Funk) course entitled analyzing hi-tech opportunities to analyze how Neurosynaptic chips are becoming economic feasible for supercomputing applications. Neurosynaptic chips use a different architecture, one that mimics the brain with neurons and synapses. These neurons and synapses are built with conventional architecture. This presentation describes the advantages and disadvantages of synaptic chips when compared to conventional chips and how rapid rates of progress in speed, density, and power efficiency are making synaptic chips economically feasible for supercomputing applications. The biggest disadvantage for synaptic chips is in software; a new operating system and application software are needed.

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