HiMax: Characterization of the CogniMem Device EE x96 Preliminary Design Review Advisor: Tep Dobry Sub Advisor: Neil Scott Members: Raymundo Flores EE.

Slides:



Advertisements
Similar presentations
Introduction to Neural Networks 2. Overview  The McCulloch-Pitts neuron  Pattern space  Limitations  Learning.
Advertisements

Kien A. Hua Division of Computer Science University of Central Florida.
The physical parts of Computer
EE 496 Poster Session Instructions Rev. 2/9/15 WS.
RIT Senior Design Project D3 Engineering Camera Platform Friday November 6, :00am to 11:00am.
Characterization Presentation Neural Network Implementation On FPGA Supervisor: Chen Koren Maria Nemets Maxim Zavodchik
Handwritten Character Recognition Using Artificial Neural Networks Shimie Atkins & Daniel Marco Supervisor: Johanan Erez Technion - Israel Institute of.
HiMax: Characterization of the CogniMem Device EE x96 Project Proposal Advisor: Tep Dobry Sub Advisor: Neil Scott Members: Raymundo Flores EE 296 Darnel.
Performed by : Rivka Cohen and Sharon Solomon Instructor : Walter Isaschar המעבדה למערכות ספרתיות מהירות High Speed Digital Systems Laboratory הטכניון.
HiMax: EE396 Biometric With CogniMem Device (Neural Processor)
Electrical & Computer Engineering, ECE Faculty Advisor Wayne Burleson Team Members Chinedu Okongwu Andrew Maxwell Awais Kazi Collaborators W. Richards.
Controls Lab Interface Improvement Project #06508Faculty Advisors: Dr. A. Mathew and Dr. D. Phillips Project Objectives This work focused on the improvement.
FINAL PRESENTATION HiMax: Facial Biometrics With a CogniMem Device (Neural Processor)
Using Neural Networks to Improve the Performance of an Autonomous Vehicle By Jon Cory and Matt Edwards.
PRELIMINARY DESIGN REVIEW HiMax: Facial Biometrics With a CogniMem Device (Neural Processor)
Artificial Intelligence
Distinctions Between Computing Disciplines
Hardware and Software Basics. Computer Hardware  Central Processing Unit - also called “The Chip”, a CPU, a processor, or a microprocessor  Memory (RAM)
1 Test Slide Text works. Text works. Graphics work. Graphics work.
Case Studies Dr Lee Nung Kion Faculty of Cognitive Sciences and Human Development UNIVERSITI MALAYSIA SARAWAK.
Technion – Israel Institute of Technology Department of Electrical Engineering High Speed Digital Systems Lab Spring 2009.
Capstone Sequence CEN UG Program CEN Curriculum Sub-committee Manuel Bermudez, Doug Dankel, Karl Gugel, Herman Lam, Mark Schmalz, Eric Schwartz 1.
Tom Allen Computer Science Department Trinity University.
Machine Learning. Learning agent Any other agent.
Chapter 14: Artificial Intelligence Invitation to Computer Science, C++ Version, Third Edition.
A Survey of Mobile Cloud Computing Application Models
Multiple Autonomous Ground/Air Robot Coordination Exploration of AI techniques for implementing incremental learning. Development of a robot controller.
Team Welcome to Woop Woop Project WiFi Clock. Introduction Team Members  Rosemary Peters  Kirby Wigton  Nate Perkins  Joe Haggberg Advisor Dr. Aziz.
Smart Pathfinding Robot. The Trouble Quad Ozan Mindek Team Leader, Image Processing Tyson Mowery Packaging Specialist Jungwoo Seo Webmaster, Networking.
Visualization of Parallel Programming A Tool for Understanding Message Passing in Parallel Systems Andrew Schwartz ‘13 Computer Science Union College Advisor:
Department of Electrical and Computer Engineering Team BeepachU November 26, 2013 Midway Design Review.
Thoughts about Trends1 Chapter 5: How to Think about Trends R. W. Hamming (from Beyond Calculation)
Association of Universities in the East of England Presented by Dr Phil Fiddaman Senior Knowledge Transfer Coordinator University of Hertfordshire Imperial.
© 2006 Cisco Systems, Inc. All rights reserved.Cisco Public 1 Version 4.0 Gathering Network Requirements Designing and Supporting Computer Networks – Chapter.
Project Title (as descriptive as possible) Group Members CPE495 Group ??? Computer Engineering Design I Electrical and Computer Engineering The University.
Fundamentals of Information Systems, Third Edition2 Principles and Learning Objectives Artificial intelligence systems form a broad and diverse set of.
SMARTPHONE HARDWARE Mainak Chaudhuri
Course Outline Course Code: CIS 111 Course Title: Introduction to computer science Units: 2 Programmes: B.Sc. Computer Science B.Sc. Management Information.
Basic Computer for Small Business
© 2006 Cisco Systems, Inc. All rights reserved.Cisco Public 1 Version 4.0 Gathering Network Requirements Designing and Supporting Computer Networks – Chapter.
Design Objectives The design should fulfill the functional requirements listed below Functional Requirements Hardware design – able to calculate transforms.
Introduction to Computing Muhammad Saeed. Topics Course Description Overview of Areas Contact Information.
MAPLD 2005/254C. Papachristou 1 Reconfigurable and Evolvable Hardware Fabric Chris Papachristou, Frank Wolff Robert Ewing Electrical Engineering & Computer.
Teleworking in research networks and remote laboratories Kaunas University of Technology Lithuania Rimantas Šeinauskas.
Verification Methodology of Gigabit Switch System 1999/9/9 Yi Ju Hwan.
1 Structure of Aalborg University Welcome to Aalborg University.
Intel SECSIMPro Script Editor Introductory Presentation E N S C R Y P T The E N S C R Y P T Team Brian Crampton, Eric Miles, & Yoshani Thiruvilangam.
An overview of information technology systems. Evolution of IT Department Data Processing (DP) Electronic Data Processing (EDP) Management Information.
Funding provided by: Clients Iowa Space Consortium Department of Electrical and Computer Engineering Advisors Dr John Lamont, Professor E/Cpr E Dr Ralph.
Engineering and Related STEM Careers BY MITCHELL PARTLOW.
Technion - Israel institute of technology department of Electrical Engineering High speed digital systems laboratory 40Gbit Signal Generator for Ethernet.
Accuracy In Your Back Pocket Mid Semester Presentation October 13, 2015.
Dual-Use Wideband Microphone System
Computer Science 340 Software Design & Testing Software Architecture.
Adaptive Tracking in Distributed Wireless Sensor Networks Lizhi Yang, Chuan Feng, Jerzy W. Rozenblit, Haiyan Qiao The University of Arizona Electrical.
OSCAR Octagonal Speech Controlled Autonomous Robot ME Seth Alberty Henry Venes EE Matt FrerichsHuy Nguyen Daniel HumkeDavid Staab Daniel MarquisFahad Wajid.
YUSEND Communication System Design Update ENG February 11, 2009.
Dynamic Node Collaboration for Mobile Target Tracking in Wireless Camera Sensor Networks Liang Liu†,‡, Xi Zhang†, and Huadong Ma‡ † Networking and Information.
Client Senior Design Electrical and Computer Engineering Iowa State University Introduction Abstract Architectural plans are currently being developed.
HiMax: Characterization of the CogniMem Device EE x96 Final Presentation Project Advisor: Neil Scott Faculty Advisor: Tep Dobry Members: Raymundo Flores.
Project Plan Document By: Aaron O’Banion Mark Williams Chris Cobb Todd Astroth Matt Stowe.
1 Chapter 1 Background Fundamentals of Java: AP Computer Science Essentials, 4th Edition Lambert / Osborne.
EE 496 Poster Session Instructions Rev. 4/3/16 WS.
Low Altitude Surveillance Blimp Project Overview
Submitted by: Ala Berawi Sujod Makhlof Samah Hanani Supervisor:
Write less; please more pictures!
AMCOM Digital Archive Design Review - Week 3.
Project METEOR [P08102 RITSAT I]
Group No: xx Implementation of Wi-Fi based Home Automation using Master Slave Communication Project Advisor: Dr. Khalid Mehmood ul Hasan Department of.
Joint Application Development (JAD)
Presentation transcript:

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

Presentation Overview: Team and Member Introduction Brief Project Overview and Background Approach Project Block Diagram Controlled Environment Diagram CogniMem Device Diagram Problems 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

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.

Project Block Diagram: Controlled Environment CogniMem Device

Controlled Environment Diagram: Computer Generated Human Generated

CogniMem Device Diagram:

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. – Weekly meetings Hardware is very expensive and reacquiring a new chip and camera may take a long time. – Take special care of equipment. – Backup Device

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 Timeline

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 Main System Diagrams: Controlled Environment CogniMem Device

References: Distributer Researchers Developers

?Any Questions?