Hybrid deep learning with optical 4f correlator: simulation and performance analysis
Year: 2025
Type: conference-abstract
Abstract: Deep Convolutional Neural Networks (DCNNs) are renowned in image classification but are computationally demanding. This paper presents a 4f optical correlator simulation to implement a deep optical CNN (DOCNN) in the Fourier domain. The system models light propagation through optical and electro-optical components, performing not only convolution but also non-linearity and pooling in the optical domain, while the final classification is executed digitally. Results show that 7% improvement in accuracy is obtained when executing more layers in the optical domain and in a Deep OCNN fashion. This suggests that computation in the optical domain and in free space is promising when deep neural models are implemented, which is further underscored by notable improvements in latency and power consumption.
DOI: 10.1117/12.3056567
Authors: Maryam Dehbashizadeh Chehreghan, Ripalta Stabile
Institution: Eindhoven University of Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Award: G3297116126
OpenAlex ID: W4410640996