Neuroevolution with genetic algorithms and particle swarm optimization can train simulated optical neural networks to accuracies comparable to gradient-based methods on small classification tasks.
Reprogrammable Electro-Optic Nonlinear Activation Functions for Optical Neural Networks
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abstract
We introduce an electro-optic hardware platform for nonlinear activation functions in optical neural networks. The optical-to-optical nonlinearity operates by converting a small portion of the input optical signal into an analog electric signal, which is used to intensity-modulate the original optical signal with no reduction in processing speed. Our scheme allows for complete nonlinear on-off contrast in transmission at relatively low optical power thresholds and eliminates the requirement of having additional optical sources between each layer of the network. Moreover, the activation function is reconfigurable via electrical bias, allowing it to be programmed or trained to synthesize a variety of nonlinear responses. Using numerical simulations, we demonstrate that this activation function significantly improves the expressiveness of optical neural networks, allowing them to perform well on two benchmark machine learning tasks: learning a multi-input exclusive-OR (XOR) logic function and classification of images of handwritten numbers from the MNIST dataset. The addition of the nonlinear activation function improves test accuracy on the MNIST task from 85% to 94%.
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cs.NE 1years
2019 1verdicts
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Efficient training and design of photonic neural network through neuroevolution
Neuroevolution with genetic algorithms and particle swarm optimization can train simulated optical neural networks to accuracies comparable to gradient-based methods on small classification tasks.