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Compressing and expanding optical matrix-vector multipliers for enabling optical image encoder-decoders and

¹Department of Electro-optics and Photonics Engineering, School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel. stern@bgu.ac.il.

Light, Science & Applications

|January 3, 2026

Summary

This summary is machine-generated.

Optical matrix-vector multipliers are essential for neural networks. This study introduces an expanding multiplier scheme to demonstrate image processing networks, including autoencoders and image generators.

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Area of Science:

Optoelectronics
Artificial Intelligence
Computer Vision

Background:

Optical matrix-vector multipliers are crucial for realizing neural networks optically.
Both compressing and expanding multiplier schemes are required for complete optical neural network implementation.

Purpose of the Study:

To propose a novel expanding optical matrix-vector multiplier scheme.
To integrate this scheme with existing compressing multipliers.
To demonstrate its application in image processing networks.

Main Methods:

Development of an expanding optical matrix-vector multiplier architecture.
Integration of the expanding multiplier with compressing multipliers.
Implementation and testing of image processor networks (autoencoders, image generators).

Main Results:

Successful demonstration of an expanding multiplier scheme.
Integration with compressing multipliers for comprehensive optical neural network components.
Validation of the scheme in image processing applications.

Conclusions:

The proposed expanding multiplier scheme is a viable component for full optical neural network realization.
This approach enables the development of advanced optical image processing networks.
Further research can explore more complex network architectures and applications.