Optical neural networks are emerging as a promising type of machine learning hardware capable of energy-efficient, parallel computation. Today’s optical neural networks are mainly developed to perform optical inference after in silico training on digital simulators. However, various physical imperfections that cannot be accurately modeled may lead to the notorious “reality gap” between the digital simulator and the physical system. To address this challenge, we demonstrate hybrid training of optical neural networks where the weight matrix is trained with neuron activation functions computed optically via forward propagation through the network. We examine the efficacy of hybrid training with three different networks: an optical linear classifier, a hybrid opto-electronic network, and a complex-valued optical network. We perform a study comparative to in silico training, and our results show that hybrid training is robust against different kinds of static noise. Our platform-agnostic hybrid training scheme can be applied to a wide variety of optical neural networks, and this work paves the way towards advanced all-optical training in machine intelligence.

中文翻译：

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光学神经网络的混合训练

光学神经网络正在成为一种很有前途的机器学习硬件，能够进行节能、并行计算。当今的光学神经网络主要是为了在in silico之后进行光学推理而开发的数字模拟器培训。然而，各种无法准确建模的物理缺陷可能会导致数字模拟器与物理系统之间存在臭名昭著的“现实差距”。为了应对这一挑战，我们展示了光学神经网络的混合训练，其中权重矩阵使用通过网络前向传播光学计算的神经元激活函数进行训练。我们使用三种不同的网络检查混合训练的功效：光学线性分类器、混合光电网络和复值光学网络。我们进行了一项与in silico 相比的研究训练，我们的结果表明混合训练对不同类型的静态噪声具有鲁棒性。我们与平台无关的混合训练方案可以应用于各种光学神经网络，这项工作为机器智能中的高级全光学训练铺平了道路。