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 , Kashiwa City Chiba Prefecture, Japan
CEO: Katsumi Inoue
Established: Sept. 2010
Capital: $
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. 2nd Generation TinySLID -8k (FPGA)
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
2011
2012
2013
2014
2010/Sep
2011/Jul
Existing　HW
Planned　HW

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 )。Possible to 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“
„green“
„yellow“
Normal search pattern has exact values eg, green, blue, red, black etc.

8. SLID Key benefits 2) Search with “fuzzy” values
„Blue“ish
„red“ish
„Black“ish
„green“ish
„yellow“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 colour
Face size
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 Immediate search result !
Face recognition
Search Object
Immediate search result !

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

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

16. SLID target applications Immediate search result !
Data search from Server side ( parallel usage of SLID`s)
SLID
SLID
SLID
SLID
SLID
SLID
SLID
SLID
Data transfer to SLID
SLID
SLID
SLID
SLID
Server
SLD
SLID
SLID
SLID
PC　analysis Software
SLID
SLID
SLID
SLID
SLID
SLID
SLID
SLID
SLID
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SLID
SLID
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Adress will be given back to server
SLID
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SLID
SLID
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 Immediate search result !
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 Ｔ１ Ｔ２ Ｔ３ 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 search takes extreme long time !
CPU detection search mechanism = serial search mechanism scanning all memory adresses
CPU search takes extreme long time !

26. Immediate search result !
SLID vs CPU
Immediate search result !

27. Inoue :update SLID vs CPU CPU vs. SLID
Does set operating only with values
Does set operating with values
Does set Operating with addresses
Does parallel set operating with addresses and values
Give adress out
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 !!!
0000h
Data 0
0001h
Data 1
0002h
Data 2
0003h
Data 3
0004h
Data 4
0005h
Data 5
000nh
Data n
Address
Data

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
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?

51. Principle of SLID detection
Where
is Yellow?

52. Principle of SLID detection
What’s the relationship between the Black and the Yellow? ５
Here it is!
This is fully parallel detection.

53. Principle of SLID detection
Example 2

54. Principle of SLID detection
This object we would like to search
Query　image
Small size Image
10 columns x 5 rows = 50 pixels

55. Principle of SLID detection
Query　image

56. Principle of SLID detection
Relative distance is a constant　value in this image space. This is the basics of SLID.
Query　image

57. Principle of SLID detection (Primary judgement)
Query　image
Primary
Judgment

58. Principle of SLID detection (Secondary judgment -1)
Query　image
Primary
Judgment

59. Principle of SLID detection (Secondary judgment -2)
Query　image
Primary
Judgment
Secondary
Judgment

60. Principle of SLID detection (Tertiary judgment -2)
Query　image
Primary
Judgment
Secondary
Judgment

61. Principle of SLID detection (Tertiary judgment -2)
Query　image
Primary
Judgment
Secondary
Judgment
Tertiary
Judgment

62. Principle of SLID detection (Tertiary judgment -2)
Here!
Query　image
Primary
Judgment
Secondary
Judgment
Tertiary
Judgment

63. Technology information
FE process: TMSC 90 nm
package: QFN 48
Die size: (see next page)
Power consumpt.: xx mA
Deliverables: RTL code in Verilog source
Software in C-code source
Integration testbench with set of testcases
Synthesis scripts
Documentation: functional specifications,
integration guide
Test reports FPGA Platform (additional cost)

64. Technology information

65. Business model Full access to source code (RTL)
License fee plus royalties
Flexible terms in regards to single/multiple use
License Fee includes training and initial support
Maintenance Program
Customization and IP Integration Design Services available from AOT Technologies

66. Support / Contact
Sales for SLID is handled by Cross Border Technologies
For Japan / US (Axel Bialke) Mobile Phone:
For Korea / Taiwan / China (Eric Kim)
Mobile Phone:
For Europa (Andreas vom Felde)
Mobile Phone:

67. Award　

68. Demonstrator
Add picture

69. Patent
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