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Event-driven adaptive optical neural network
Frank Brückerhoff-Plückelmann1, Ivonne Bente1, Marlon Becker2
1Physical Institute, University of Münster, Heisenbergstraße 11, 48149 Münster, Germany.
Science Advances
|October 20, 2023
Summary
This study introduces an adaptive optical neural network with reconfigurable structures and non-volatile memory. The scalable photonic architecture demonstrates potential for advanced artificial intelligence hardware.
Area of Science:
- Photonics
- Artificial Intelligence
- Neuromorphic Computing
Background:
- Optical neural networks offer high-speed computation but often lack adaptability.
- Existing architectures struggle with dynamic reconfiguration and non-volatile memory integration.
Purpose of the Study:
- To develop a large-scale, adaptive optical neural network with structural plasticity.
- To implement non-volatile memory and nonlinear activation functions using phase-change materials.
Main Methods:
- Utilized wavelength-addressable artificial neurons with embedded phase-change materials.
- Employed multimode focusing for activation functions with excitatory and inhibitory responses.
- Trained the network using an evolutionary algorithm to classify text samples.
Main Results:
- Demonstrated a reversible switching contrast of 3.2 decibels for activation functions.
- Successfully trained a network of 736 subnetworks (8398 functional neurons) to distinguish between English and German text.
- Investigated both synaptic and structural plasticity during the training process.
Conclusions:
- The proposed adaptive optical neural network architecture is scalable and demonstrates significant potential for advanced AI applications.
- The integration of structural plasticity and non-volatile memory in photonic systems is feasible.
- Event-driven architectures combined with phase-change materials offer a promising path for next-generation neuromorphic computing.
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