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Optical-processor architectures for alternating-projection neural networks.

R J Marks Ii, L E Atlas, S Oh

|September 12, 2009

Summary

This summary is machine-generated.

This study explores optical processor architectures for alternating-projection neural networks. These networks utilize passive optical feedback for rapid, single-view training and one-iteration convergence.

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Area of Science:

Optical computing
Artificial neural networks

Background:

Alternating-projection neural networks (APNNs) are a class of neural networks.
Traditional APNNs often require complex electronic or slow optical components for feedback.

Purpose of the Study:

To investigate novel optical-processor architectures for APNNs.
To enable efficient training and rapid convergence using passive optical feedback.

Main Methods:

Consideration of various optical-processor architectures for APNNs.
Implementation of passive optical feedback for iterative processing.
Elimination of electronics and slow optical elements (e.g., phase conjugators) in the feedback path.

Main Results:

The proposed optical processor can learn new training vectors with a single presentation.
Networks trained for +/-1 outputs can achieve convergence in a single iteration.

Conclusions:

Passive optical feedback offers a viable and efficient method for APNN implementation.
Single-iteration learning and convergence are achievable with these novel architectures.