Fully connected optoelectronic neural network for image classification

Alexander Song; Sai Nikhilesh Murty Kottapalli; Peer Fischer

doi:10.1117/12.2692290

13 March 2024 Fully connected optoelectronic neural network for image classification

Alexander Song, Sai Nikhilesh Murty Kottapalli, Peer Fischer

Proceedings Volume PC12903, AI and Optical Data Sciences V; PC1290307 (2024) https://doi.org/10.1117/12.2692290
Event: SPIE OPTO, 2024, San Francisco, California, United States

Abstract

Optical approaches are extremely promising for high-speed, scalable computing, which is necessary for modern deep learning and AI applications. In this study, we introduce a multilayer optoelectronic computing framework that alternates between optical and optoelectronic layers to implement matrix-vector multiplications and rectified linear functions, respectively. The system is designed to be real-time and is highly parallel, utilizing arrays of light emitters and detectors connected with independent analog electronics. We experimentally demonstrate the operation of our system and demonstrate that it outperforms a single-layer analog through simulations.

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Citation Download Citation

Alexander Song, Sai Nikhilesh Murty Kottapalli, and Peer Fischer "Fully connected optoelectronic neural network for image classification", Proc. SPIE PC12903, AI and Optical Data Sciences V, PC1290307 (13 March 2024); https://doi.org/10.1117/12.2692290

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