Optical Neural Networks for Image Classification: A GPU-Accelerated Two-Stage Architecture
Year: 2025
Type: article
Abstract: We present a novel optical neural network architecture that achieves state-of-the-art performance on the MNIST digit classification task by simulating light propagation through learnable optical elements. Our approach combines two cascaded optical stages with learnable amplitude and phase modulation, achieving 98.657 % accuracy on the Kaggle digit recognizer leaderboard with only 1000 epochs of training completed in 20 minutes. The architecture leverages GPU-accelerated Fast Fourier Transforms (FFTs) to efficiently simulate optical diffraction and interference phenomena, demonstrating that physics-inspired neural architectures can achieve competitive performance with remarkable training efficiency.
Author: Angulo Lafuente, Francisco
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7084580387
FWCI:
Cites:
Cited by:
Related to: