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

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

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

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

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

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

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.

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

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

SLID Key benefits 3) Edge detection

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

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.

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

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

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

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 SLID SLID SLID SLID SLID SLID SLID Adress will be given back to server SLID SLID SLID SLID Immediate search result !

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.

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

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

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

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

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

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

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

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 !

Immediate search result ! SLID vs CPU Immediate search result !

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

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

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

CAM Block Diagram

SLID Block Diagram

Principle of SLID detection Example 1

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.

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

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

Principle of SLID detection Where is Black?

Principle of SLID detection Where is Black?

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

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

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.

Principle of SLID detection Where is Red?

Principle of SLID detection Where is Red?

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

Principle of SLID detection Where is Green?

Principle of SLID detection Where is Green?

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

Principle of SLID detection Where is Blue?

Principle of SLID detection Where is Blue?

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

Principle of SLID detection Where is Yellow?

Principle of SLID detection Where is Yellow?

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

Principle of SLID detection Example 2

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

Principle of SLID detection Query　image

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

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

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

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

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

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

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

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)

Technology information

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

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 3330 For Korea / Taiwan / China (Eric Kim) Email: Eric.kim@crossborder-technologies.com Mobile Phone: +82 10 2371 3532 For Europa (Andreas vom Felde) Email: Avf@crossborder-technologies.com Mobile Phone: +49 176 3235 5412

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