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Overview

Overview. Company overview Executive summary SLID roadmap SLID Key benefits SLID target application SLID performance comparison SLID technology - Basic principle - Content addressable Memory - Detection mechanism Technology information Business model Evaluation environment

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Overview

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  1. Overview • Company overview • Executive summary • SLID roadmap • SLID Key benefits • SLID target application • SLID performance comparison • SLID technology - Basic principle- Content addressable Memory- Detection mechanism Technology information • Business model • Evaluation environment • Support / Contact • Awards • Patent status

  2. Company overview • Company name: Advanced Original Technologies • Location: Matsuba-Cho 4-7-4-101, Kashiwa City 227-0827 Chiba Prefecture, Japan • CEO: Katsumi Inoue • Established: Sept. 2010 • Capital: $220.000 • Business Summary: Technology development, IP sales

  3. Executive summary • SLID is a new architectural conceptual processor for recognition and search purposes. • SLID improves the weaknesses of existing DSP and CPU solutions and improves significantly power consumption for recognition and search uses cases. • SLID has a high affinity towards other devices and is extremely easy to handle. • SLID enables recognition performance beyond super computer capabilities • SLID can be a stand alone chip (road map) and can also be integrated into digital Basebands ,Application Co-processors , Sensor and other IC`s. • SLID enables total new use cases and generates new business concepts

  4. SLID Roadmap SLID (ASIC-Ⅱ) idea SLID (ASIC-Ⅰ）Idea Fuzzy SLID (FPGA) 2nd Generation TinySLID -8k (FPGA) 1st TinySLID -1k Demo (FPGA) 2012CES Introduction 2013CES Introduction Established AOT 2013 2012 2014 2011 2011/Jul 2010/Sep PlannedHW ExistingHW

  5. SLID Key benefits • Recognition of objects in <50μS possible=> Can recognize >20000 Objects / sec.=> parallel recognition possible • No difference between exact and fuzzy recognition. => Can recognize >20000 fuzzy Objects / sec • Edge Detection in Color possible. Extreme fast Edge detection possible (< 50μS )。Possibleto detect shapes • Fuzzy Search in terms of position and Value possible (see explanation p.x) • SLID can be digitally integrated into any semiconductor, but also build a stand alone roadmap with different performance characteristics • No need for special HW & SW => Reduces R&D costs • Reduces significantly power consumption for search tasks • Miniturization and weight reduction benefits

  6. SLID Key benefits 1) Speed 30.000 2.000.000 times faster than Exact Match on PC ! times faster than Fuzzy Match on PC !

  7. SLID Key benefits 2) Search with exact values „Blue“ „red“ „Black“ „yellow“ „green“ Normal search pattern has exact valueseg, green, blue, red, black etc.

  8. SLID Key benefits 2) Search with “fuzzy” values „Blue“ish „red“ish „Black“ish „yellow“ish „green“ish Is it possible to look for close values , eg. Colours who are close to the original value

  9. SLID Key benefits 2) Search with fuzzy positions Black eyes Face size Face colour Pink lips Is it possible enlarge the search area => fuzzy search You can combine “fuzzy values” with “fuzzy position” search

  10. SLID Key benefits 3) Edge detection

  11. SLID Key benefits 3) Edge detection - Detects immediately address of red bodies. - Size and form can be instantly recognized

  12. SLID Key benefits 4) Edge Detection - Detects immediately address of red bodies.- Size and form can be instantly recognized => It’s possible to look for shape.

  13. SLID target applications Face recognition Search Object Immediate search result !

  14. SLID target applications Weather pattern recognition Search Object Immediate search result !

  15. SLID target applications Chart pattern recognition (eg. Stock pattern) Search Object Immediate search result !

  16. SLID target applications Data search from Server side( parallel usage of SLID`s) SLID SLID SLID SLID SLID SLID SLID SLID SLID SLID SLID SLID SLID SLID SLID SLID SLID SLID SLID SLID SLID SLID SLID SLID SLID SLID SLID SLID SLID SLID SLID SLID SLID SLID SLID SLID Datatransfer to SLID SLID SLID SLID SLID Server SLD SLID SLID SLID PCanalysis Software SLID SLID SLID SLID SLID SLID SLID SLID SLID SLID SLID SLID SLID SLID SLID SLID SLID SLID SLID SLID Adress will be given back to server Immediate search result !

  17. SLID target applications Traffic control ( eg. number plate recognition) It is also possible, for instance, to specify the locations of the license plates on cars in an extremely fast manner.

  18. SLID target applications DNA Search Conventional search ・・・GATCATTGA・・・ Search Object

  19. SLID target applications DNA Search Search with SLID ・・・GATCATTGA・・・ Search Object Immediate search result !

  20. SLID target applications Moving object recognition Ｔ１ Ｔ２ Ｔ３ Ｔ４ ・・・・No change to the background・・・A car has passed through it・・・・

  21. SLID target applications Moving object recognition The area that does not match is the area that has moved.

  22. SLID target applications Moving object recognition Ｔ２ Ｔ３ Ｔ１ Ｔ４ Super-simple and fast recognition of a moving body

  23. SLID target applications Stereo Match Ｌ Image Ｒ Image Measure depth by the difference in position in the horizontal direction.

  24. SLID target applications Further applications ideas ! • Compares 2 videos ( piracy identification) • Sound recognition • Character recognition • Finger Print recognition • Smile recognition • 3D (Video) recognition • Web Search • Graphic defect search • Moving object tracking

  25. SLID vs CPU • CPU detection search mechanism = serial search mechanism scanning all memory adresses CPU search takes extreme long time !

  26. SLID vs CPU Immediate search result !

  27. SLID vs CPU CPU vs. SLID • Does set operating with values • Does set Operating with addresses • Does parallel set operating with addresses and values • Give adress out • Does set operating only with values Inoue :update

  28. SLID technology • CPU is doing information processing only sequentially and hence extremely slow for set operating processing. If CPU speed is increased heat and power consumption will increase • SLID is processing data en bloc parallel • SLID is compared to CPU many 1000 times faster and can reduce power consumption and heat. => This enables totally new use cases, application and ideas !!! Address Data 0000h Data 0 0001h Data 1 000nh Data n 0002h Data 2 0003h Data 3 0004h Data 4 0005h Data 5

  29. Basic principle • Actual data is stored linearly from the first dimension to the Nth dimension into CAM • SLID is using position and search data as search input • search pattern is matched with stored data • Address shift can detect “fuzzy” data location • Σ（data & data location)= Pattern • Address is used as output

  30. CAM Block Diagram

  31. SLID Block Diagram

  32. Principle of SLID detection Example 1

  33. Principle of SLID detection Because real images are very complex, here we will use an extremely simple image. This kind of image is stored in SLID’s memory. Query data This is the pattern we want to find.

  34. Principle of SLID detection A mask is placed over the entire image.

  35. Principle of SLID detection Counters are attached here (every pixel). counter counter counter counter counter counter counter

  36. Principle of SLID detection Where is Black?

  37. Principle of SLID detection Where is Black?

  38. Principle of SLID detection Where is Black? Windows is made in the Mask

  39. Principle of SLID detection Where is Black? １ １ １ １ There is a possibility that the required image is somewhere around these 4 pixels.

  40. Principle of SLID detection The mask, equipped with a counter and punctured with holes, can be moved at an ultra-fast speed to any arbitrary coordinate.

  41. Principle of SLID detection Where is Red?

  42. Principle of SLID detection Where is Red?

  43. Principle of SLID detection What’s the relationship between the Black and the Red? ２ ２

  44. Principle of SLID detection Where is Green?

  45. Principle of SLID detection Where is Green?

  46. Principle of SLID detection What’s the relationship between the Black and the Green? ３ ３

  47. Principle of SLID detection Where is Blue?

  48. Principle of SLID detection Where is Blue?

  49. Principle of SLID detection What’s the relationship between the Black and the Blue? ４

  50. Principle of SLID detection Where is Yellow?

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