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Recognetics provides a powerful, high speed, low power, small size |
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and low cost solution for pattern recognition |
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CM-IR2K Image Recognition Board |
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The CM-IR2K Image Recognition Board offers developers and OEMs a comprehensive platform to evaluate the CogniMem neural network for real-time video recognition applications. The board features a Micron CMOS sensor, 2 CogniMem chips, each with 1024 neurons, an Actel FPGA accessible to programmers and a Flash memory to save the application settings and knowledge. User I/Os are implemented on the board in a flexible variety of configurations, including an I2C serial bus, an RS232 bus and 8 general purpose I/Os brought to header pins. |
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Specifications |
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Micron MT9V022 video sensor |
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Monochrome, Progressive scan |
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752x480 pixels |
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60 frames per second |
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Global shutter |
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External trigger input |
High-speed Recognition engine |
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Internal feature extraction from a region of |
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interes |
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Single region per frame as small as |
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16x16 pixels and as large as a full frame |
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Output category with the best match in 11 |
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us after receipt of the last vector data |
I/O buses |
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Miniature USB Hi Speed (480 Mbps) |
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I2C serial interface (100-400 kbit) |
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Serial output (115,200 baud) |
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8 parallel outputs (LVTTL 16mA) |
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Save project to Flash memory |
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Neural network |
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Classify pattern vectors of up to 256 bytes |
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Up to 32768 categories |
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Classification status in 1 clock cycle |
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(status= identified, uncertain or unknown) |
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Category readout cycle in 36 clock cycle |
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per firing neuron from smallest distance |
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and up (equil to 3 microsec at 27 Mhz) |
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Built-in model generator |
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1024 neurons working in paralell |
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Expandable through neuron expansion |
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modules |
FPGA Program |
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Optional core modules (input and output , |
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data conditioning, signature extraction |
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decision logic, etc. |
Mechanical and Electrical |
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3.3v @ <250 mA |
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55กม66mm |
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Ordering information |
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CM-EB, includes: |
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One CM-EMB2K module, |
CogniMem development library |
Easy Video Trainer software |
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Optional stackable CM-EMB2K modules with 1024 or 2048 neurons |
| For more information, you can download some documentations from here. |
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