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IEEE Education Society: Reshaping the Future of Technology

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Manuel Gericota (Instituto Politecnico Porto, Portugal) and Javier García-Zubía (University of Deusto, Bilbao, Spain), chair the meeting from the Spanish and Portuguese Chapter of the IEEE Education Society, linked to the webinar video at
https://www.youtube.com/watch?v=Uab7f-LCYuE
Although all topics are related to computing, different perspectives and application areas will guarantee that a great diversity of interesting points will be covered during the meeting. The presentation of Manuel Castro and Unai Hernández-Jayo will be centered around the Internet of Things (IoT) (Manuel in education and Unai in its application to autonomous cars), the one from Manuel Caeiro will be focused in wearables, electronic devices and systems incorporated in some part of our body or clothes, and the last one from Manuel Gericota will explain why Field Programmable Gate Arrays (FPGAs) are being (again) one of the current hot topics.
A brief introduction about the MOOC - Foundations to Open Education and OERs repositories, is presented.

Publicado en: Educación
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IEEE Education Society: Reshaping the Future of Technology

  1. 1. European Erasmus Plus Projects and Certification in Cloud Computing and IoT IN-CLOUD & IoT4SMEs Projects Prof. Dr. Manuel Castro, http://www.slideshare.net/mmmcastro/ Electronics Technology Professor, UNED, Madrid, SPAIN IEEE Fellow, Sr Past President, IEEE Education Society
  2. 2. European Erasmus Plus Projects and Certification in Cloud Computing and IoT IN-CLOUD & IoT4SMEs Projects Prof. Dr. Manuel Castro, http://www.slideshare.net/mmmcastro/ Electronics Technology Professor, UNED, Madrid, SPAIN IEEE Fellow, Sr Past President, IEEE Education Society
  3. 3. EDUCATION SOCIETY http://ieee-edusociety.org/
  4. 4. Motivation  SMEs are fundamental for European economy …  But many fail in the first 5 years  New strategic skills must be acquired to ensure survival. E.g.: Cloud computing
  5. 5. Cloud computing benefits Availability encrease Shorter time to market Flexible model Cost reduction On-demand scaling Try before buying
  6. 6. Reasons why SME don’t give the jump to the cloud  Lose control over their infrastructures  Lack of understanding of infrastructures, costs and application scenarios  ICT knowledge of users, managers and entrepreneurs
  7. 7. IN-CLOUD aims 1. Raise awareness on how the cloud can boost economic growth and innovation to:  SMEs, public administrations and Universities 2. Create VET qualifications, based on sector analysis, to give training about cloud computing
  8. 8. IN-CLOUD partners  8 partners  6 countries  Geographic coverage  6 languages
  9. 9. IN-CLOUD courses will provide …  Business and financial skills  Technical skills  Cloud project management  Contract negotiation  Security  Data integration
  10. 10. Certifications 1. Cloud Professional for Public Administrations 2. Cloud Professional for Business 3. Cloud Professional for Education 4. Cloud Technology Professional
  11. 11. Cloud Professional for Public Administrations  Unit 1: Introduction to cloud computing  Unit 2: Security basics  Unit 3: Cloud models  Unit 4: Cloud services and applications for public administrations and for citizens/community  Unit 5: Legal and technical aspects of cloud computing for public administration and for citizens/community
  12. 12. Cloud Professional for Business  Unit 1: Introduction to cloud computing  Unit 2: Security basics  Unit 3: Cloud models  Unit 4: Cloud business services and applications  Unit 5: Legal and technical aspects of cloud computing for business
  13. 13. Cloud Professional for Education  Unit 1: Introduction to cloud computing  Unit 2: Security basics  Unit 3: Cloud models  Unit 4: Cloud services and applications for education and training  Unit 5: Legal and technical aspects of cloud computing for education and training
  14. 14. Cloud Technology Professional  Unit 1: Cloud security  Unit 2: Cloud models and providers  Unit 3: Cloud architecture  Unit 4: Cloud virtualization  Unit 5: Cloud service and application development and implementation
  15. 15. Motivation  6.4 billion connected things will be in use worldwide in 2016, up 30 percent from 2015, and will reach 20.8 billion by 2020. Gartner  While in 2014 just 300,000 developers contributed to the IoT, 4.5 million developers by 2020 are expected: 57% compound annual growth rate and a massive market opportunity. VisionMobile  Expected to grow from 130.33 B$ in 2015 to 883.55 B$ by 2022.  IoT market value in the EU will exceed one trillion euros in 2020
  16. 16. How many things?
  17. 17. National initiatives  USA: “Manufacturing USA” network of excellence institutes and labs to support the diffusion of the Industry 4.0 and ICT related technologies, through a PPP., with about 500 M$ public investment.  UK: national investment of 40 M£ on IoT announced in 2015, including a 10 M£ single project to realize a demonstrator to exhibit the capacity of IoT in a city region.  Germany: “Industrie 4.0” action plan sponsored at the federal level with the involvement of large industry players and technology public, with 1 B€ public investment.  France: “Industrie du Futur” reindustrialization plan and investment in technologies I4.0 , with a public commitment € 10 B€.  Italy: “Piano Nazionale Industria 4.0” launched in Sept. 2016, with 10 B€ public investments and 32 B€ private investments in 4 years.
  18. 18. Objectives Aim: Qualifying new professionals able to support the digital transformation of the European companies exploiting to the advantages offered by the IoT technology. This objective is reached by pursuing the specific objectives of:  raising awareness among European Small and Medium Enterprises of the IoT technologies and applications and of the potential benefits for their competitiveness and economical growth;  creating VET qualifications for professionals inside European Companies, enhancing their digital competences and training them to introduce  and manage IoT technologies and applications.
  19. 19. Partnership
  20. 20. Certifications 1) Iot Decision Maker 2) IoT with microcontrollers 3) IoT with microprocessor 4) IoT Data Analyst
  21. 21. 1. Iot for Decision Makers 1.1) IoT technology 1.2) IoT business stragegy 1.3) Overview of data analysis 1.4) Legal aspects 1.5) Basics of networking and security
  22. 22. 2. IoT with microcontrollers: Arduino 2.1) Introduction (technology + business) 2.2) Device architecture and sensors for microcontrollers 2.3) Programming microcontrollers 2.4) Platforms for microcontrollers and applications 2.5) Networking and Security (for microcontrollers)
  23. 23. 3. IoT with microprocessor: Raspberry Pi 3.1) Introduction (technology + business) 3.2) Device architecture and sensors for microprocessors 3.3) Programming microprocessors 3.4) Platforms for microprocessors and applications 3.5) Networking and Security (for microprocessors)
  24. 24. 4. IoT Data Analyst 4.1) Introduction (technology + business) 4.2) Device architecture and sensors 4.3) Networking and Security 4.4) IoT data analysis 4.5) IoT platforms
  25. 25. MOOC - Foundations to Open Education and OERs repositories IEEEx > joint effort with edX  First MOOC of the IEEE Education Society  19th August – 17th September 2017  Future actions
  26. 26. MOOC - Foundations to Open Education and OERs repositories  Week 1. Introduction to Open Education & OERs  Week 2. Repositories  Week 3. Applications to Academia & Industry  Week 4. OER Applications
  27. 27. European Erasmus Plus Projects and Certification in Cloud Computing and IoT IN-CLOUD & IoT4SMEs Projects Prof. Dr. Manuel Castro, http://www.slideshare.net/mmmcastro/ Electronics Technology Professor, UNED, Madrid, SPAIN IEEE Fellow, Sr Past President, IEEE Education Society
  28. 28. IEEE EDUCATION SOCIETY: RESHAPING  THE FUTURE OF TECHNOLOGY Internet of Vehicles and  autonomous vehicles Unai Hernández Jayo Unai.hernandez@deusto.es June 2017 Mobility Unit, DeustoTech
  29. 29. 2 Faro (Portugal). June 2017 Unai Hernández Jayo Motivation Context • Thanks to the concept of Internet of Things (IoT), all kind of devices, stuffs, etc. can be interconnected, “without” human interaction
  30. 30. 3 Faro (Portugal). June 2017 Unai Hernández Jayo Purpose of IoT • “The purpose of the IoT is to give humans  superpowers” (Tim O’Reilly and Cory Doctorow) – Produce and consume information in real time – Automatic response of “the system” according to the  environment – Humans provide intelligence to the devices
  31. 31. 4 Faro (Portugal). June 2017 Unai Hernández Jayo New paradigms • Smart cities
  32. 32. 5 Faro (Portugal). June 2017 Unai Hernández Jayo New paradigms • Industry 4.0
  33. 33. 6 Faro (Portugal). June 2017 Unai Hernández Jayo New paradigms • Smart Grid
  34. 34. 7 Faro (Portugal). June 2017 Unai Hernández Jayo IoT combines a lot of concepts
  35. 35. 8 Faro (Portugal). June 2017 Unai Hernández Jayo Then… Imagine that vehicles could talk 
  36. 36. 9 Faro (Portugal). June 2017 Unai Hernández Jayo Internet of Vehicles • The Internet of Vehicles (IoV) is the convergence of  the mobile Internet and the Internet of Things.  • Comprising new and current vehicles, either fitted  or integrated with RF equipment. • Vehicles already have: – Human machine interface – Sensors – Actuators
  37. 37. 10 Faro (Portugal). June 2017 Unai Hernández Jayo IoV and ITS • Although the IoV is a fashion concept, Intelligent  Transport Systems (ITS) combine all the technologies  that have been included in this term
  38. 38. 11 Faro (Portugal). June 2017 Unai Hernández Jayo What is the IoV? • IoV technology refers  to dynamic mobile  communication  systems that  communicate  between vehicles and  public networks using  V2V (vehicle‐to‐ vehicle), V2I (vehicle‐ to‐Infrastructure) and  V2P (vehicle‐to‐ Pedrestians)  interactions. 
  39. 39. 12 Faro (Portugal). June 2017 Unai Hernández Jayo What is the IoV? • It enables information sharing and the gathering of information  on vehicles, roads and their surrounds. Moreover, it features the  processing, computing, sharing and secure release of information  onto information platforms It could be a distributed or centralized architecture
  40. 40. 13 Faro (Portugal). June 2017 Unai Hernández Jayo ITS station architecture
  41. 41. 14 Faro (Portugal). June 2017 Unai Hernández Jayo ITS architecture
  42. 42. 15 Faro (Portugal). June 2017 Unai Hernández Jayo Cooperative autonomous vehicles
  43. 43. 16 Faro (Portugal). June 2017 Unai Hernández Jayo Autonomous driving levels Source: https://iq.intel.com/autonomous‐cars‐road‐ahead/
  44. 44. 17 Faro (Portugal). June 2017 Unai Hernández Jayo Future vehicles: autonomous or  cooperative • Isolated autonomous vehicles:
  45. 45. 18 Faro (Portugal). June 2017 Unai Hernández Jayo Future vehicles: isolated autonomous  or cooperative • Isolated autonomous vehicles:
  46. 46. 19 Faro (Portugal). June 2017 Unai Hernández Jayo Future vehicles: isolated autonomous  or cooperative • Isolated autonomous vehicles:
  47. 47. 20 Faro (Portugal). June 2017 Unai Hernández Jayo Future vehicles: isolated autonomous  or cooperative • Advantages of isolated autonomous vehicles: – Without the need for a driver, cars could become mini‐ leisure rooms – Increase auto‐safety? – Travelers would be able to journey overnight and sleep for  the duration – Reduced or non‐existent fatigue from driving? – Speed limits could be increased? – Reduction in insurance premiums for car owners? – Efficient travel also means fuel savings – (…)
  48. 48. 21 Faro (Portugal). June 2017 Unai Hernández Jayo Future vehicles: isolated autonomous  or cooperative • Connected and Autonomous Vehicles:  combination of IoV + Autonomous Vehicles.  Source: http://www.4erevolution.com/en/la‐meteo‐prochain‐secteur‐a‐etre‐uberise/
  49. 49. 22 Faro (Portugal). June 2017 Unai Hernández Jayo Future vehicles: isolated autonomous  or cooperative • Connected and Autonomous Vehicles:  combination of IoV + Autonomous Vehicles.  Source: http://safecarnews.com/australia‐stablishes‐connected‐vehicle‐network‐c‐its‐by‐2017//
  50. 50. 23 Faro (Portugal). June 2017 Unai Hernández Jayo Future vehicles: isolated autonomous  or cooperative • Advantages of connected and Autonomous  Vehicles: – Public safety – Traffic management – Traffic coordination – Traffic information support – Emission reduction – New business models – (…) Source: http://safecarnews.com/australia‐stablishes‐connected‐vehicle‐network‐c‐its‐by‐2017//
  51. 51. 24 Faro (Portugal). June 2017 Unai Hernández Jayo So… what could be the future?
  52. 52. Thanks for your interest
  53. 53. Commercial Of-The-Shelf (COTS) Wearables Supporting Self-Quantification Manuel Caeiro Rodríguez UNIVERSITY OF VIGO TELEMATIC ENGINEERING DEPARTMENT
  54. 54. SNOLA 2
  55. 55. SNOLA
  56. 56. VIRTUAL CITY ENVIRONMENT FOR ENGINEERING PROBLEM BASED LEARNING 2014-2015 http://ecity-project.eu VIRTUAL CITY ENVIRONMENT FOR ENGINEERING PROBLEM BASED LEARNING 2014-2015 http://ecity-project.eu THIS PROJECT HAS BEEN FUNDED WITH SUPPORT FROM THE EUROPEAN COMMISSION. THIS PUBLICATION REFLECTS THE VIEWS ONLY OF THE AUTHOR, AND THE COMMISSION CANNOT BE HELD RESPONSIBLE FOR ANY USE WHICH MAY BE MADE OF THE INFORMATION CONTAINED THEREIN. © eeGeo Virtual City Design and manage your own city Problem-based Learning 8 scenarios avaliable to engage into engineering
  57. 57. eCity Results: a set of 8 problems Energy Distribution Renewable Energies Internet Server Providers Mobile Connection Earthquake Protection Flood Protection Pollution Public Transportation
  58. 58. Wearables 6
  59. 59. COTS Wearables 7 Source:IDC
  60. 60. Wearables Variety: Wrist (186) 8 Source:IDC
  61. 61. Wearables Variety: Head (81) 9 ● Google Glasses ● Snapchat Spectacles ● Microsoft Hololens ● Sony Playstation VR Source vandrico.com/wearables
  62. 62. Wearables Variety: Ear (12) 10 ● Jabra Elite Sport Source vandrico.com/wearables
  63. 63. Wearables Variety: Feet (13) 11 ● IDM Perform ● Kinemaitx Tune Source vandrico.com/wearables
  64. 64. Wearables Variety: Arm (10) 12 ● Shot Tracker ● Qardio Qardioarm Source vandrico.com/wearables
  65. 65. Wearables Variety: Chest (20) 13 ● Polar H7 ● Monbaby Source vandrico.com/wearables
  66. 66. Wearables Variety: Other Locations 14 ● Ankle (4) ● Back (1) ● Body (10+39) ● Fingers (9) ● Legs (14) ● Neck (11) ● Pelvis (2) ● Shoulders (3) ● Thighs (2) ● Waist (10) Source vandrico.com/wearables
  67. 67. Wearable Applications 15 Lifestyle Medical Industrial Entertainment Fitness Gaming
  68. 68. Self-quantification 16
  69. 69. Wearables and Self-quantification 17 When do you wear your wearable? ● 40% of wearable users say they feel naked when not wearing them ● 25% even sleep with them Wearables are the most personal devices Source Ericson Consumerlab Wearable Technology and Internet of Things, 2016
  70. 70. COTS Wearables: Problems and Issues Whom to share personal data? ● 50% of wearable users share data from wearables online ● 67% are open to sharing data with third party entities provided its anonymous ● 70% perceive wearable manufacturers to be serious in protecting their wearable data 18 Source Ericson Consumerlab Wearable Technology and Internet of Things, 2016 Wearable Manufactures as Personal Data Brokers
  71. 71. Wearable Applications: Education 19 Education Education ● Two different kinds of projects o As educational tools o As quantification supports to feature students  Sleep  Stress
  72. 72. Objectives ●Collecting data: different solutions ●Homogenization: data models, reference points, sensors (Heart Rate, Skin Temperature, Galvanic Skin Response, Accelerometer). ●Proposing indicators for an academic environments: o Sleep indicators: sleep quality, sleepiness, chronotype, sleep sensitivity and sleep regularity. o Stress indicators: snapshot, accumulated, latent and variability. 20
  73. 73. COTS Wearables: Problems and Issues How to collect Data? ● Wearables are still tethered to the smartphone via Bluetooth and follow a walled garden approach ● 4 data transfer mode o Warehouse vs. wearable transfer o Direct vs. indirect access 21
  74. 74. COTS Wearables: Problems and Issues How to collect Data? ● Wearables are still tethered to the smartphone via Bluetooth and follow a walled garden approach ● 4 data transfer mode o Warehouse vs. wearable transfer o Direct vs. indirect access 22
  75. 75. COTS Wearables: Problems and Issues How to collect Data? ● Wearables are still tethered to the smartphone via Bluetooth and follow a walled garden approach ● 4 data transfer mode o Warehouse vs. wearable transfer o Direct vs. indirect access 23
  76. 76. COTS Wearables: Problems and Issues How to collect Data? ● Wearables are still tethered to the smartphone via Bluetooth and follow a walled garden approach ● 4 data transfer mode o Warehouse vs. wearable transfer o Direct vs. indirect access 24
  77. 77. COST Wearables Data models: ●Data integration problems oDifferences in the names oDifference in the beginning of measurements oDifferences related to the sensors available. ●Lack of in accuracy sensors 25
  78. 78. Sleep indicator ●Sleep apps 26
  79. 79. Sleep indicator ●Chronotype 27 Type Start Bed Time Values End Bed Time Values Extreme morning -/21:30 2 -/05:00 2 Moderately morning 21:30/22:45 1 05:00/06:30 1 Intermediate 22:45/00:45 0 06:30/08:30 0 Moderately evening 00:45/02:00 1 08:30/10:00 −1 Definitely evening 02:00/- 2 10:00/- −2
  80. 80. Proposal for education 28
  81. 81. 29
  82. 82. Proposal for education 30
  83. 83. Proposal for education 31
  84. 84. Proposal for education ●Services for teachers/developers 32 ● Moderately morning ● Traditional evaluation ● Most stressful activities: 3.2, 4.1, 5.2 ● No problems detected ● Moderately evening ● Continuous evaluation ● Most stressful activities: 2.2, 4.1, 5.2 ● Irregular sleep quality warning ● Latent stress high
  85. 85. Conclusions ●Problems in wearables oLow battery duration oInteroperability problems oSensors precision oTethered to smartphones ●Opportunities oApplication in new environments oFast evolution 33
  86. 86. Thanks for your attention. 34 mcaeiro@gist.uvigo.es farriba@uvigo.com The images shown in this presentation have been used without any commercial purpose
  87. 87. Reconfigurable Computing A new computing paradigm Manuel G. Gericota Department of Electrical Engineering  School of Engineering – Polytechnic of Porto & Faculty of Technology, Natural Sciences and Maritime Sciences University College of Southeast Norway Kongsberg, Norway
  88. 88. Official At Last: Intel Completes $16.7 Billion Buy of Altera Barb Darrow Dec 28, 2015 Chip giant Intel has completed its $16.7 billion mega-deal to buy Altera, thus getting a big toe hold in the burgeoning market for a new type of chip that is much more flexible than the microprocessors Intel has ridden to fame and fortune. Manuel Gericota 2
  89. 89. Why and what? • Why Intel paid an astonishing $16.7 billion dollars for  it? • What chips are that? • What can be better than Intel microprocessors? Manuel Gericota 3
  90. 90. The Moore’s Law “The complexity for minimum  component costs has increased at  a rate of roughly a factor of two  per year. Certainly over the short  term this rate can be expected to  continue, if not to increase.” G. E. Moore, “Cramming more components  onto integrated circuits,” Electronics, vol. 38,  no. 8, pp. 114‐117, April 19, 1965.  Manuel Gericota 4
  91. 91. Device technology • Device technology is  the silicon area used  by a circuit,  expressed by the  length of the  smallest transistor  that can be  fabricated Manuel Gericota 5 Source: International Technology Roadmap for Semiconductors, 2011
  92. 92. Device technology • Device technology is  the silicon area used  by a circuit,  expressed by the  length of the  smallest transistor  that can be  fabricated Manuel Gericota 6 Source: International Technology Roadmap for Semiconductors, 2011
  93. 93. Device technology • Device technology is  the silicon area used  by a circuit,  expressed by the  length of the  smallest transistor  that can be  fabricated Manuel Gericota 7 Source: International Technology Roadmap for Semiconductors, 2011
  94. 94. Microprocessors • Computers based on microprocessors  • Implement software solutions based on pure microprocessor systems • Fixed hardware → one or more generic and/or specific processors  (GPU (Graphic Processing Unit), DSP (Digital Signal Processor), …)  Manuel Gericota 8
  95. 95. Microprocessors • General purpose computing • 1945 ‐ John Von Neumann computer architecture • simple, fixed structure, able to execute any kind of                                              computation, given a properly programmed control,                            without the need for hardware modification • Sequential instruction fetch • a program is coded as a set of instructions to be executed sequentially,  instruction after instruction • In general, the execution of an instruction on a computer can be done  in five cycles: Instruction Fetch, Instruction Decode, Operand Fetch,  Instruction Execute, Write Back Manuel Gericota 9
  96. 96. Microprocessors • Von Neumann architecture constraints • main advantage of the Von Neumann (VN) computing paradigm • flexibility → it can be used to program almost all existing algorithms • algorithms must be coded according to the VN rules → ‘The algorithm  must adapt itself to the hardware’ • temporal use of the same hardware for a wide variety of applications → VN computation is often characterized as ‘temporal computation’ • all algorithms must be sequentially programmed  many algorithms  cannot be executed with their potential best performance Manuel Gericota 10
  97. 97. Microprocessors Manuel Gericota 11 Frequency (GHz) 41% /year Source: Semiconductor Industry Association ‐ International  Technology Roadmap for Semiconductors, 2011 • Microprocessors’  frequency of operation  is no longer increasing  due to • limits on switching speed • limits on dissipated  power • limits on device scaling  due to leakage current
  98. 98. The big question • The big question • how to increase performance without increasing frequency? • The small answer! • parallelism → multiple core processors • Extra cores on a single processor  multiplies the amount of  data that could be handled by the CPU • Adding more CPU cores never results in perfect scaling Manuel Gericota 12 Intel core i9 18 cores
  99. 99. Amdahl’s Law  • The performance benefit of  parallelism is limited by the  amount of the application  that must run serially, e.g. is  not or cannot be parallelized • relevant if the microprocessor  is running several applications  at a time • unfitted to accelerate single  applications Manuel Gericota 13 Gene Amdahl  (1922‐2015)
  100. 100. Multicore constraints • The software must be written to support multithreading • if not, threads will be primarily run through a single core thus  degrading the efficiency • Thermal restrictions  cores will be running at lower speeds • Sharing the same system bus and memory bandwidth limits  the performance improvement • DRAM speed limits memory access • CPU clock vs DRAM latency is increasing • Multicores in a single chip drive production yields down Manuel Gericota 14
  101. 101. Microprocessors • In sum… • microprocessors are                  running out of steam • Moore’s Law collapsed due to heat  and leakage issues • DARK SILICON → portions of  a device that need to be shut down  in order to avoid overheating Manuel Gericota 15
  102. 102. DARK SILICON Manuel Gericota 16 DARK SILICON trends for different technology nodes Source: M. Shafique, S.  Garg, J. Henkel, D.  Marculescu, "The EDA  challenges in the dark  silicon era," in 51st  ACM/EDAC/IEEE  Design Automation  Conference (DAC),  2014, pp.1‐6
  103. 103. Heterogeneous Computing • More than one type of processor is used to perform  specialized processing capabilities • e.g. graphics rendering system = CPU + GPU • GPU to render 3D scenes and perform mathematically intensive  computations on large datasets • CPU to perform operating system and data networking tasks • CPUs are reaching performance limits & GPUs are difficult to  program + they still use the Von Newman approach  remain  sequential machines  performance is limited Manuel Gericota 17
  104. 104. Application‐Specific Processors (ASIPs) • Processor tailored to run one application only • the processing unit is designed and optimized for that particular  application • the hardware ‘adapts’ itself to the application • instruction cycles are eliminated • instruction set of the application directly implemented in hardware • input data stream in the processor through its inputs → the processor  performs the required computation → results collected at the outputs • ASIP = Application‐Specific Integrated Circuit (ASIC) Manuel Gericota 18
  105. 105. In an ideal world • Ideally, we would like to have the flexibility of the  general purpose processors + the performance of the  application‐specific processors in the same device Manuel Gericota 19
  106. 106. Manuel Gericota 20
  107. 107. Field‐programmable devices • The most versatile non‐ASIC technology • An array of prefabricated generic logic cells and a general interconnect  structure both programmable • programmable semiconductor “switches”  open‐ or short‐circuited • customization is done by configuring the device with a specific data file Manuel Gericota 21 FPGA (Field‐Programmable Gate Array) architecture
  108. 108. Exponential amounts of data • Internet of Things (IoT) • Installation of sensors everywhere • Increases in Internet connectivity • Data requires extensive processing • from the point of origin, through the cloud, into systems  that perform analysis  need for a new approach to  hardware Manuel Gericota 22 generate an  exponential  amount of data
  109. 109. Internet of Everything • In the future, cheap sensors will increasingly be  attached to almost everything • 5G data connectivity = ‘ubiquitous’ connectivity  almost everything will be connected to one or more  sensors and processed by the communications  infrastructure Manuel Gericota 23
  110. 110. Faster switching and routing  • Large‐scale data centres + cloud computing +  wireless applications  high‐speed serial  transceivers for fast switching and routing • dual‐mode transceivers  56 Gbps enable terabit data  rates • supports both optical and copper interfaces for  applications including chip‐to‐chip, backplane and direct  attach cable Manuel Gericota 24
  111. 111. Ubiquitous data and security • Around 30 billion devices connected by 2020 • Handling security screening  substantial computing  capabilities  • FPGAs are ideally suited to performing these tasks • they operate quickly (hardware performance) • can be reconfigured when needed to upgrade security  settings → can be used to process streams of data that use  rapidly changing encryption or keys (software flexibility)  Manuel Gericota 25
  112. 112. Project Catapult (Microsoft) • Makes extensive use of FPGAs “By exploiting the reconfigurable nature of FPGAs, at the server,  the Catapult architecture delivers the efficiency and  performance of custom hardware without the cost, complexity  and risk of deploying fully customized ASICs into the datacentre.  In doing so, [Microsoft has] achieved an order of magnitude  performance gain relative to CPUs with less than a 30 percent  cost increase, and no more than a 10 percent power increase.” from Microsoft’s blog Manuel Gericota 26
  113. 113. FPGAs in datacentres and search engines • Microsoft has been developing a hardware  infrastructure for its Azure Cloud computing centres • The technology is also being used to accelerate the  speed of Internet searches at its Bing search engine • In cases where a server has to handle a mix of  workloads (making ASICs impractical), FPGAs are a  very energy‐efficient, high performance solution Manuel Gericota 27
  114. 114. FPGA flexibility advantages Manuel Gericota 28 Results from a Forbes survey of more than 300 CTOs and system architects Forbes Insights – The coming data avalanche – and how we’ll handle it Forbes Insights in association with Intel ‐ October of 2016
  115. 115. Intel processors and FPGAs • Intel has introduced hybrid devices that combine Intel® Xeon®  processors with an FPGA • The combination of the multiple processors on the same  silicon will enable designers to create products with  considerably higher performance than individual devices • Intel is simplifying the interfaces between FPGAs and other system  components, and developing  programming libraries  enable  developers to more easily program an FPGA  accelerating the  development of new devices Manuel Gericota 29
  116. 116. Partial and dynamic reconfiguration • The whole Virtex series and 7‐ series, UltraScale and  UltraScale+ families, including  Zynq, from Xilinx, and Stratix V  and Stratix 10 from Intel are  partial and dynamically  reconfigurable  Manuel Gericota 30
  117. 117. A new computing paradigm • Reconfigurable Computing (RC) • The ability to provide a hardware platform that  can be customized on a per‐application basis  under software control Manuel Gericota 31
  118. 118. Reconfigurable computing Manuel Gericota 32
  119. 119. Resource sharing • Multiple  applications share  the same logic  resources in the  spatial and temporal  domains  increase  application  performance Manuel Gericota 33
  120. 120. In the future… Will hardware implementations be as  flexible as software ones !? Manuel Gericota 34
  121. 121. Manuel Castro, Unai Hernandez-Jayo, Manuel Caeiro & Manuel Gericota presented by Javier García-Zubía, Manuel Gericota Spanish and Portuguese Education Chapter Chairs IEEE Education Society Reshaping the future of technology
  122. 122. European Erasmus Plus Projects and Certification in Cloud Computing and IoT IN-CLOUD & IoT4SMEs Projects Prof. Dr. Manuel Castro, http://www.slideshare.net/mmmcastro/ Electronics Technology Professor, UNED, Madrid, SPAIN IEEE Fellow, Sr Past President, IEEE Education Society

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