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Scalable Optical Convolutional Neural Networks Based on Free-Space Optics Using Lens Arrays and a Spatial Light
1Department of Physics Education, Kyungpook National University, 80 Daehakro, Bukgu, Daegu 41566, Republic of Korea.
Journal of Imaging
|November 24, 2023
Summary
A novel scalable optical convolutional neural network (SOCNN) using free-space optics and Koehler illumination overcomes limitations of prior systems. This approach enables input scaling and incoherent light, offering massive optical parallelism for multilayer networks.
Area of Science:
- Optics
- Artificial Intelligence
- Computer Vision
Background:
- Traditional 4f correlator systems face limitations in scalability and illumination uniformity.
- Abbe illumination in previous systems can lead to crosstalk and non-uniformity.
- Addressing these issues is crucial for advancing optical neural network architectures.
Purpose of the Study:
- To propose a scalable optical convolutional neural network (SOCNN) that overcomes the limitations of existing 4f correlator systems.
- To leverage free-space optics and Koehler illumination for improved performance.
- To enable scaling of input arrays and the use of incoherent light sources.
Main Methods:
- Development of a scalable optical convolutional neural network (SOCNN) architecture.
- Implementation using free-space optics.
- Utilizing Koehler illumination instead of Abbe illumination for enhanced uniformity and reduced crosstalk.
- Analysis of limitations in kernel size scaling and parallel throughput.
Main Results:
- The proposed SOCNN effectively addresses limitations of previous 4f correlator systems.
- Koehler illumination provides more uniform illumination and significantly reduces crosstalk compared to Abbe illumination.
- The SOCNN architecture supports input array scaling and the use of incoherent light sources.
- Analysis confirmed the potential for multilayer convolutional neural networks with massive optical parallelism.
Conclusions:
- The scalable optical convolutional neural network (SOCNN) presents a viable solution for advanced optical computing.
- The use of Koehler illumination and free-space optics enhances performance and scalability.
- This architecture paves the way for highly parallelized optical neural networks.
