Fixed weight Hopfield Neural Network based on optical implementation of all-optical MZI-XNOR logic gate

Kussay Nugamesh Mutter; Mohd Zubir Mat Jafri; Azlan Abdul Aziz

doi:10.1117/12.854838

14 May 2010 Fixed weight Hopfield Neural Network based on optical implementation of all-optical MZI-XNOR logic gate

Kussay Nugamesh Mutter, Mohd Zubir Mat Jafri, Azlan Abdul Aziz

Author Affiliations +

Proceedings Volume 7717, Optical Modelling and Design; 77170X (2010) https://doi.org/10.1117/12.854838
Event: SPIE Photonics Europe, 2010, Brussels, Belgium

Abstract

Many researches are conducted to improve Hopfield Neural Network (HNN) performance especially for speed and memory capacity in different approaches. However, there is still a significant scope of developing HNN using Optical Logic Gates. We propose here a new model of HNN based on all-optical XNOR logic gates for real time color image recognition. Firstly, we improved HNN toward optimum learning and converging operations. We considered each unipolar image as a set of small blocks of 3-pixels as vectors for HNN. This enables to save large number of images in the net with best reaching into global minima, and because there are only eight fixed states of weights so that only single iteration performed to construct a vector with stable state at minimum energy. HNN is useless in dealing with data not in bipolar representation. Therefore, HNN failed to work with color images. In RGB bands each represents different values of brightness, for d-bit RGB image it is simply consists of d-layers of unipolar. Each layer is as a single unipolar image for HNN. In addition, the weight matrices with stability of unity at the diagonal perform clear converging in comparison with no self-connecting architecture. Synchronously, each matrix-matrix multiplication operation would run optically in the second part, since we propose an array of all-optical XOR gates, which uses Mach-Zehnder Interferometer (MZI) for neurons setup and a controlling system to distribute timely signals with inverting to achieve XNOR function. The primary operation and simulation of the proposal HNN is demonstrated.

Citation Download Citation

Kussay Nugamesh Mutter, Mohd Zubir Mat Jafri, and Azlan Abdul Aziz "Fixed weight Hopfield Neural Network based on optical implementation of all-optical MZI-XNOR logic gate", Proc. SPIE 7717, Optical Modelling and Design, 77170X (14 May 2010); https://doi.org/10.1117/12.854838

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
8 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

RGB color model

Logic devices

Neural networks

Data modeling

Binary data

Control systems

Computing systems

Show All Keywords

Keywords/Phrases

Search In:

Publication Years