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Published on: December 12, 2012
Optical bipolar kth-order neural network based on inner-product representation
Applied Optics
|October 12, 2010
Summary
A novel polarization-encoding scheme simplifies bipolar kth-order neural networks. This optical approach uses light polarization for data multiplication, enabling a compact and efficient neural network design.
Area of Science:
- Optoelectronics
- Artificial Neural Networks
- Optical Computing
Background:
- Bipolar kth-order neural networks (BKNNs) are computationally intensive.
- Existing implementations often require complex circuitry and numerous components.
- Efficient optical implementations of BKNNs are desirable for high-speed processing.
Purpose of the Study:
- To propose a new polarization-encoding scheme for BKNNs.
- To develop a compact architecture for the proposed BKNN.
- To demonstrate the feasibility of using liquid-crystal devices in this architecture.
Main Methods:
- Utilizing inner-product representation for bipolar data.
- Achieving bipolar data multiplication through the rotation of linearly polarized light.
- Computer simulations to validate the proposed architecture and components.
Main Results:
- A novel polarization-encoding scheme for BKNNs was successfully proposed.
- A compact BKNN architecture was designed, eliminating the need for subtractions and fixing neuron threshold levels.
- Computer simulations confirmed the suitability of liquid-crystal televisions as polarization modulators.
Conclusions:
- The proposed polarization-encoding scheme offers a simplified and compact approach to implementing BKNNs.
- Optical implementation using polarization modulation is a viable strategy for BKNNs.
- Liquid-crystal devices are practical components for realizing such optical neural networks.
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