Nonlinear Computation with Linear Optics via Source-Position Encoding

• URL: http://arxiv.org/abs/2504.20401v1
• Date: Tue, 29 Apr 2025 03:55:05 GMT
• Title: Nonlinear Computation with Linear Optics via Source-Position Encoding
• Authors: N. Richardson, C. Bosch, R. P. Adams,
• Abstract summary: We introduce a novel method to achieve nonlinear computation in fully linear media.<n>Our method can operate at low power and requires only the ability to drive the optical system at a data-dependent spatial position.<n>We formulate a fully automated, topology-optimization-based hardware design framework for extremely specialized optical neural networks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Optical computing systems provide an alternate hardware model which appears to be aligned with the demands of neural network workloads. However, the challenge of implementing energy efficient nonlinearities in optics -- a key requirement for realizing neural networks -- is a conspicuous missing link. In this work we introduce a novel method to achieve nonlinear computation in fully linear media. Our method can operate at low power and requires only the ability to drive the optical system at a data-dependent spatial position. Leveraging this positional encoding, we formulate a fully automated, topology-optimization-based hardware design framework for extremely specialized optical neural networks, drawing on modern advancements in optimization and machine learning. We evaluate our optical designs on machine learning classification tasks: demonstrating significant improvements over linear methods, and competitive performance when compared to standard artificial neural networks.

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