Nonlinear Computation with Linear Optics via Source-Position Encoding

📝 Original Info

• Title: Nonlinear Computation with Linear Optics via Source-Position Encoding
• ArXiv ID: 2504.20401
• Date: 2025-04-29
• Authors: ** (논문에 명시된 저자 정보를 여기서 삽입하십시오. 예: John Doe, Jane Smith, et al.) **

📝 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.

💡 Deep Analysis

📄 Full Content

Reference