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Programmable low-threshold optical nonlinear activation functions for photonic neural networks.

Ying Huang, Weiping Wang, Lei Qiao

|April 1, 2022

Summary

Researchers developed novel photonic devices for low-power photonic neural networks (PNNs). These devices enable optical control of nonlinear activation functions, achieving high accuracy in handwritten digit classification tasks.

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Area of Science:

Photonics
Artificial Intelligence
Materials Science

Background:

Photonic neural networks (PNNs) face challenges in implementing nonlinear activation functions.
Optically controlled nonlinear activation functions are crucial for PNNs but difficult to realize.
Low-power and efficient activation functions are needed for scalable PNNs.

Purpose of the Study:

To experimentally demonstrate two types of programmable, low-threshold, optically controlled nonlinear activation functions.
To integrate these functions into convolutional neural networks for PNNs.
To assess the performance of these PNNs in a real-world task.

Main Methods:

Utilized on-chip integrated Germanium-Silicon (Ge-Si) photoelectric detectors.
Employed silicon electro-optical switches for optical control.
Implemented rectified linear unit (ReLU) and sigmoid activation functions with arbitrary slopes.
Embedded the developed activation functions into convolutional neural networks.

Main Results:

Achieved programmable, low-threshold (0.2 mW) nonlinear activation functions.
Generated ReLU and sigmoid functions without additional electrical processing.
Attained high inference accuracies up to 95% on MNIST handwritten digit classification.
Demonstrated suitability for low-power PNNs with multiple propagation layers.

Conclusions:

The developed devices offer a viable solution for implementing nonlinear activation functions in PNNs.
These low-power, optically controlled functions are essential for advancing photonic computing.
The technology enables efficient and scalable PNNs for complex computational tasks.