HiMax: Characterization of the CogniMem Device EE x96 Project Proposal Advisor: Tep Dobry Sub Advisor: Neil Scott Members: Raymundo Flores EE 296 Darnel Balais EE 496

Presentation Overview: Team and Member Introduction Project Overview & Background Approach Potential Problems Team Expectations End of Semester Project Goals Project Timeline References

Team and Member Introduction: HiMax Group: Communication and Information Sciences, UH Manoa – Develop and research applications using the Cognimem Chip. Department of Electrical Engineering - UH Manoa – Develop and research baseline characteristic of the CogniMem Neural Processor. – Create hardware and develop software for baseline characterization. Darnel Balais: Software Programmer Raymundo Flores: Hardware

Project Overview: This technology is fairly new, so we propose to develop: 1. Procedures to accurately train the Neural Processor, 2. Baselines for "high-level confidence" for pattern recognition for a given physical environment. 3. (For follow-on X96 Project), an application that uses the CogniMem Neural processor, with a "built-in" camera, as an autonomous pointer that identifies any painting / object in a museum setting. The camera, then can be used to communicate to an ultra portable PC via broadband that gathers information about the identified painting.

Project Background: CogniMem 1K Specs - Patented parallel architecture Parallel neurons - Unlimited neural network expansion - Trained by example - Low power consumption Image Recognition Board

Project Background: Current market use of CogniMem Chip:

Project Background: Successful Field Implementation: - The first generation of CogniSight sensors are sorting millions of herrings on a Norwegian fleet of 4 fishing vessels with few hundreds digital neurons trained by fishermen on the job and delivering more than 95% accuracy 24/7.

Approach: Fabricate a mini-museum setting that includes a picture inside a frame that enables us to test the CogniMem processor/camera pattern recognition (imaging) capabilities. Develop a program that trains the CogniMem camera to identify and view images from any angle, orientation and distance.

Potential Problems: Hardware and software has only been recently developed and product knowledge is still limited at this time. – Help can easily be attained from Research Assistants within the HiMax Group. Hardware is very expensive and reacquiring a new chip and camera may take a long time. – Take special care of equipment.

Team Expectations: Use current programming background and even expand that knowledge to design and test the CogniMem Neural Processor to build our proposed device. Obtain valuable research material for further studies of the applications allowable by Neural Processor.

End of Semester Project Goals: Our projected goals are: To develop "high-confidence level" characterization of the CogniMem processor with respect to distance, orientation, and image complexity. Have a working device for a museum application.

Project Timelines:

Presentation Summary: Key Technology: CogniMem Neural Processor (1024 parallel neurons) Method of Modeling: Learn and build knowledge by example vectors (i.e. camera, microphone). No cumbersome programming required. Challenges: Fairly new technology Minimal data/information available Lay "ground" work for future applications Possible Applications: Unlimited

References: Distributer Researchers Developers

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