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Overview Company overview Executive summary SLID roadmap SLID Key benefits SLID target application SLID performance comparison SLID technology -Basic.

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Presentation on theme: "Overview Company overview Executive summary SLID roadmap SLID Key benefits SLID target application SLID performance comparison SLID technology -Basic."— Presentation transcript:

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2 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

3 Company overview Company name:Advanced Original Technologies Location:Matsuba-Cho , Kashiwa City Chiba Prefecture, Japan CEO:Katsumi Inoue Established:Sept Capital:$ Business Summary: Technology development, IP sales

4 Executive summary 1)SLID is a new architectural conceptual processor for recognition and search purposes. 2)SLID improves the weaknesses of existing DSP and CPU solutions and improves significantly power consumption for recognition and search uses cases. 3)SLID has a high affinity towards other devices and is extremely easy to handle. 4)SLID enables recognition performance beyond super computer capabilities 5)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. 6)SLID enables total new use cases and generates new business concepts

5 SLID Roadmap Established AOT st TinySLID -1k Demo (FPGA) 2012CES Introduction 2 nd Generation TinySLID -8k (FPGA) SLID (ASIC- Ⅰ) Idea 2010/Sep /Jul SLID (ASIC- Ⅱ ) idea 2013CES Introduction Existing HW Planned HW Fuzzy SLID (FPGA)

6 SLID Key benefits 1)Recognition of objects in Can recognize >20000 Objects / sec. => parallel recognition possible 2)No difference between exact and fuzzy recognition. => Can recognize >20000 fuzzy Objects / sec 3)Edge Detection in Color possible. Extreme fast Edge detection possible (< 50μS ) 。 Possible to detect shapes 4)Fuzzy Search in terms of position and Value possible (see explanation p.x) 5)SLID can be digitally integrated into any semiconductor, but also build a stand alone roadmap with different performance characteristics 6)No need for special HW & SW => Reduces R&D costs 7)Reduces significantly power consumption for search tasks 8)Miniturization and weight reduction benefits

7 SLID Key benefits 1) Speed times faster than Exact Match on PC ! times faster than Fuzzy Match on PC !

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

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

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

11 SLID Key benefits 3)Edge detection

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

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

14 SLID target applications Face recognition Immediate search result ! Search Object

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

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

17 SLID target applications Data search from Server side ( parallel usage of SLID`s) Immediate search result ! Server SLID Data transfer to SLID SLID SLD SLID PC analysis Software Adress will be given back to server

18 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.

19 SLID target applications Search Object ・・・ GATCATTGA ・・・ DNA Search Conventional search

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

21 SLID target applications Moving object recognition T2T1T4T3 ・・・・ No change to the background ・・・ A car has passed through it ・・・・

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

23 SLID target applications Moving object recognition T1T2T3T4 Super-simple and fast recognition of a moving body

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

25 SLID target applications Further applications ideas ! 1)Compares 2 videos ( piracy identification) 2)Sound recognition 3)Character recognition 4)Finger Print recognition 5)Smile recognition 6)3D (Video) recognition 7)Web Search 8)Graphic defect search 9)Moving object tracking

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

27 SLID vs CPU Immediate search result !

28 SLID vs CPU 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 CPU vs. SLID Inoue :update

29 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 !!! 0000 h Data h Data h Data h Data h Data h Data 5 000n h Data n Address Data

30 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

31 CAM Block Diagram

32 SLID Block Diagram

33 Principle of SLID detection Example 1

34 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. This is the pattern we want to find. Query data

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

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

37 Principle of SLID detection Where is Black?

38 Principle of SLID detection Where is Black?

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

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

41 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.

42 Principle of SLID detection Where is Red?

43 Principle of SLID detection Where is Red?

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

45 Principle of SLID detection Where is Green?

46 Principle of SLID detection Where is Green?

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

48 Principle of SLID detection Where is Blue?

49 Principle of SLID detection Where is Blue?

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

51 Principle of SLID detection Where is Yellow?

52 Principle of SLID detection Where is Yellow?

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

54 Principle of SLID detection Example 2

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

56 Principle of SLID detection Query image

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

58 Principle of SLID detection (Primary judgement) Query image Primary Judgment

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

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

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

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

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

64 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)

65 Technology information

66 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

67 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:

68 Award

69 Demonstrator Add picture

70 Patent add


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