https://doi.org/10.1051/epjconf/202430915001
Deep Classification from Scattered Light
1 Center for Life Nano- & Neuroscience, Italian Institute of Technology, Viale Regina Elena 291, I-00161, Rome, Italy
2 Institute of Nanotechnology of the National Research Council of Italy, CNR-NANOTEC, Piazzale A. Moro 5, I-00185, Rome, Italy
* Corresponding author: sara.penagutierrez@iit.it
Published online: 31 October 2024
Photonic Stochastic Emergent Learning (PSEL) represents an innovative paradigm rooted in mathematical brain modelling and emergent memories. In this study, we explore the intersection of these concepts to address memory storage and classification tasks. Leveraging optical computing principles and random projections, PSEL constructs memory representations from the inherent randomness in nature. Specifically, we select a set of highly similar random states generated by coherent light scattered from a diffusive medium. Classification is performed by organizing the memories spatially into different classes and comparing inputs to those stored memories. The results demonstrate the efficacy of PSEL in memory construction and parallel classification, emphasizing its potential applications in high-performance computing and artificial intelligence systems.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
