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Slide 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 Slide 3 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 Slide 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 Slide 5 SLID Roadmap Established AOT 2014 2013 1 st TinySLID -1k Demo (FPGA) 2012CES Introduction 2 nd Generation TinySLID -8k (FPGA) SLID (ASIC- Idea 2010/Sep 2011 2012 2011/Jul SLID (ASIC- ) idea 2013CES Introduction Existing HW Planned HW Fuzzy SLID (FPGA) Slide 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 (< 50S ) 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 Slide 7 SLID Key benefits 1) Speed times faster than Exact Match on PC ! times faster than Fuzzy Match on PC ! 30.000 2.000.000 Slide 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 Slide 9 SLID Key benefits redish yellowish Blueish greenish Blackish Is it possible to look for close values, eg. Colours who are close to the original value 2)Search with fuzzy values Slide 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 Slide 11 SLID Key benefits 3)Edge detection Slide 12 SLID Key benefits 3)Edge detection - Detects immediately address of red bodies. - Size and form can be instantly recognized Slide 13 SLID Key benefits - Detects immediately address of red bodies. - Size and form can be instantly recognized 4) Edge Detection => Its possible to look for shape. Slide 14 SLID target applications Face recognition Immediate search result ! Search Object Slide 15 SLID target applications Weather pattern recognition Immediate search result ! Search Object Slide 16 SLID target applications Chart pattern recognition (eg. Stock pattern) Immediate search result ! Search Object Slide 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 Slide 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. Slide 19 SLID target applications Search Object GATCATTGA DNA Search Conventional search Slide 20 SLID target applications Search Object GATCATTGA DNA Search Search with SLID Immediate search result ! Slide 21 SLID target applications Moving object recognition No change to the background A car has passed through it Slide 22 SLID target applications Moving object recognition The area that does not match is the area that has moved. Slide 23 SLID target applications Moving object recognition Super-simple and fast recognition of a moving body Slide 24 SLID target applications Stereo Match Image Image Measure depth by the difference in position in the horizontal direction. Slide 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 Slide 26 SLID vs CPU CPU detection search mechanism = serial search mechanism scanning all memory adresses CPU search takes extreme long time ! Slide 27 SLID vs CPU Immediate search result ! Slide 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 Slide 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 0 0001 h Data 1 0002 h Data 2 0003 h Data 3 0004 h Data 4 0005 h Data 5 000n h Data n Address Data Slide 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 data & data location)= Pattern Address is used as output Slide 31 CAM Block Diagram Slide 32 SLID Block Diagram Slide 33 Principle of SLID detection Example 1 Slide 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 SLIDs memory. This is the pattern we want to find. Query data Slide 35 Principle of SLID detection A mask is placed over the entire image. Slide 36 Principle of SLID detection Counters are attached here (every pixel). counter Slide 37 Principle of SLID detection Where is Black? Slide 38 Principle of SLID detection Where is Black? Slide 39 Principle of SLID detection Where is Black? Windows is made in the Mask Slide 40 Principle of SLID detection There is a possibility that the required image is somewhere around these 4 pixels. Where is Black? Slide 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. Slide 42 Principle of SLID detection Where is Red? Slide 43 Principle of SLID detection Where is Red? Slide 44 Principle of SLID detection Whats the relationship between the Black and the Red? Slide 45 Principle of SLID detection Where is Green? Slide 46 Principle of SLID detection Where is Green? Slide 47 Principle of SLID detection Whats the relationship between the Black and the Green? Slide 48 Principle of SLID detection Where is Blue? Slide 49 Principle of SLID detection Where is Blue? Slide 50 Principle of SLID detection Whats the relationship between the Black and the Blue? Slide 51 Principle of SLID detection Where is Yellow? Slide 52 Principle of SLID detection Where is Yellow? Slide 53 Principle of SLID detection Whats the relationship between the Black and the Yellow? Here it is! This is fully parallel detection. Slide 54 Principle of SLID detection Example 2 Slide 55 Principle of SLID detection Small size Image 10 columns x 5 rows = 50 pixels Query image This object we would like to search Slide 56 Principle of SLID detection Query image Slide 57 Principle of SLID detection Relative distance is a constant value in this image space. This is the basics of SLID. Query image Slide 58 Principle of SLID detection (Primary judgement) Query image Primary Judgment Slide 59 Principle of SLID detection (Secondary judgment -1) Query image Primary Judgment Slide 60 Principle of SLID detection (Secondary judgment -2) Query image Primary Judgment Secondary Judgment Slide 61 Principle of SLID detection (Tertiary judgment -2) Query image Primary Judgment Secondary Judgment Slide 62 Principle of SLID detection (Tertiary judgment -2) Query image Secondary Judgment Primary Judgment Tertiary Judgment Slide 63 Principle of SLID detection (Tertiary judgment -2) Query image Here! Secondary Judgment Primary Judgment Tertiary Judgment Slide 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) Slide 65 Technology information Slide 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 Slide 67 Support / Contact Sales for SLID is handled by Cross Border Technologies For Japan / US (Axel Bialke) Email: Axel.bialke@crossborder-technologies.com Mobile Phone: +81 80 8030 3330Axel.bialke@crossborder-technologies.com For Korea / Taiwan / China (Eric Kim) Email: Eric.kim@crossborder-technologies.comEric.kim@crossborder-technologies.com Mobile Phone: +82 10 2371 3532 For Europa (Andreas vom Felde) Email: Avf@crossborder-technologies.comAvf@crossborder-technologies.com Mobile Phone: +49 176 3235 5412 Slide 68 Award Slide 69 Demonstrator Add picture Slide 70 Patent add