Polarization-based non-linear deep diffractive neural networks

Sai Nikhilesh Murty Kottapalli; Lennart Schlieder; Alexander Song; Valentin Volchkov; Bernhard Schölkopf; Peer Fischer

doi:10.1117/12.3002910

13 March 2024 Polarization-based non-linear deep diffractive neural networks

Sai Nikhilesh Murty Kottapalli, Lennart Schlieder, Alexander Song, Valentin Volchkov, Bernhard Schölkopf, Peer Fischer

Proceedings Volume PC12903, AI and Optical Data Sciences V; PC129030B (2024) https://doi.org/10.1117/12.3002910
Event: SPIE OPTO, 2024, San Francisco, California, United States

Abstract

Coherent light-based optical computing faces challenges in implementing low-power optical nonlinearities, which are essential for optimal neural network performance. This limitation restricts the overall performance and applicability of these networks. To address this, we propose a polarization encoding scheme that enables a low-power nonlinearity. By projecting polarization to amplitude in a sinusoidal manner and subsequently converting amplitude back to the polarization domain, we can implement a non-linear diffractive neural network. The neural network utilizes polarization-encoded inputs and polarization rotation-encoded weights. Optical interconnects between neurons are achieved through the diffraction of the spatially inhomogeneously polarized wavefront. Our approach offers a low-power solution for incorporating nonlinearity into deep diffractive neural networks.

Conference Presentation

Citation Download Citation

Sai Nikhilesh Murty Kottapalli, Lennart Schlieder, Alexander Song, Valentin Volchkov, Bernhard Schölkopf, and Peer Fischer "Polarization-based non-linear deep diffractive neural networks", Proc. SPIE PC12903, AI and Optical Data Sciences V, PC129030B (13 March 2024); https://doi.org/10.1117/12.3002910

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