High-performance real-world optical computing trained by in situ gradient-based model-free optimization

• URL: http://arxiv.org/abs/2307.11957v6
• Date: Thu, 21 Nov 2024 16:13:48 GMT
• Title: High-performance real-world optical computing trained by in situ gradient-based model-free optimization
• Authors: Guangyuan Zhao, Xin Shu, Renjie Zhou,
• Abstract summary: We propose a gradient-based model-free optimization (G-MFO) method based on a Monte Carlo gradient estimation algorithm. G-MFO treats an optical computing system as a black box and back-propagates the loss directly to the optical computing weights' probability distributions. Our experiments on diffractive optical computing systems show that G-MFO outperforms hybrid training on the MNIST and FMNIST datasets.
• Score: 2.2407602847819827
• License:
• Abstract: Optical computing systems provide high-speed and low-energy data processing but face deficiencies in computationally demanding training and simulation-to-reality gaps. We propose a gradient-based model-free optimization (G-MFO) method based on a Monte Carlo gradient estimation algorithm for computationally efficient in situ training of optical computing systems. This approach treats an optical computing system as a black box and back-propagates the loss directly to the optical computing weights' probability distributions, circumventing the need for a computationally heavy and biased system simulation. Our experiments on diffractive optical computing systems show that G-MFO outperforms hybrid training on the MNIST and FMNIST datasets. Furthermore, we demonstrate image-free and high-speed classification of cells from their marker-free phase maps. Our method's model-free and high-performance nature, combined with its low demand for computational resources, paves the way for accelerating the transition of optical computing from laboratory demonstrations to practical, real-world applications.

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