Download HiMax - University of Hawaii - Department of Electrical Engineering

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:
Image Recognition Board
www.recognetics.com
CogniMem 1K Specs
- Patented parallel
architecture
- 1024 Parallel neurons
- Unlimited neural network
expansion
- Trained by example
- Low power consumption
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.
www.recognetics.com
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:

1.
2.
3.
http://wiki.roadnarrows.com
- Distributer
http://www.general-vision.com
- Researchers
http://www.recognetics.com
- Developers
?Any Questions?