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Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

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High-Performance Real-World Optical Computing Trained by in Situ Gradient-Based Model-Free Optimization.

Guangyuan Zhao, Xin Shu, Renjie Zhou

IEEE Transactions on Pattern Analysis and Machine Intelligence

|September 24, 2024

Summary

This summary is machine-generated.

We developed a gradient-based model-free optimization (G-MFO) method for efficient optical computing training. This approach accelerates optical computing applications by overcoming simulation-to-reality gaps and reducing computational demands.

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Area of Science:

Optics
Computer Science
Machine Learning

Background:

Optical computing offers high-speed, low-energy data processing.
Current optical computing methods struggle with computationally intensive training and simulation-to-reality discrepancies.

Purpose of the Study:

To introduce a gradient-based model-free optimization (G-MFO) method for efficient in situ training of optical computing systems.
To address the computational demands and simulation biases in optical computing training.

Main Methods:

Developed a Monte Carlo gradient estimation algorithm for G-MFO.
Treated optical computing systems as black boxes, back-propagating loss to weight probability distributions.
Avoided computationally heavy and biased system simulations.

Main Results:

G-MFO demonstrated superior performance compared to hybrid training on MNIST and FMNIST datasets.
Achieved image-free, high-speed cell classification using marker-free phase maps.
Showcased the model-free nature and low computational resource requirements of G-MFO.

Conclusions:

G-MFO enables computationally efficient in situ training for optical computing.
The method bridges the gap between lab demonstrations and real-world applications.
G-MFO accelerates the adoption of optical computing by improving training efficiency and performance.