光学神经网络的可重编程电光非线性激活函数
Reprogrammable Electro-Optic Nonlinear Activation Functions for Optical Neural Networks
摘要：
介绍了一种用于光学神经网络中非线性激活函数的电光硬件平台。光-光非线性通过将输入光信号的一小部分转换成模拟电信号来操作，该模拟电信号用于强度调制原始光信号而不降低处理速度。我们的方案允许在相对较低的光功率阈值下传输完整的非线性开关对比度，并消除了网络每层之间具有额外光源的要求。此外，激活函数可通过电偏置重新配置，允许其被编程或训练以合成各种非线性响应。使用数值模拟，我们证明了这种激活函数显着提高了光学神经网络的表现力，使它们能够在两个基准机器学习任务上表现良好：学习多输入异或（XOR）逻辑函数和对MNIST数据集的手写数字图像进行分类。非线性激活函数的加入将MNIST任务的测试准确率从85%提高到94%。
英文摘要：
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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