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TRAINING OF PHOTONIC NEURAL NETWORKS THROUGH IN SITU BACKPROPAGATION

机译:通过原位反向化训练光子神经网络

摘要

Systems and methods for training photonic neural networks in accordance with embodiments of the invention are illustrated. One embodiment includes a method for training a set of one or more optical interference units (OIUs) of a photonic artificial neural network (ANN), wherein the method includes calculating a loss for an original input to the photonic ANN, computing an adjoint input based on the calculated loss, measuring intensities for a set of one or more phase shifters in the set of OIUs when the computed adjoint input and the original input are interfered with each other within the set of OIUs, computing a gradient from the measured intensities, and tuning phase shifters of the OIU based on the computed gradient.
机译:示出了根据本发明的实施例的用于训练光子神经网络的系统和方法。一个实施例包括用于训练光子人工神经网络(ANN)的一组一个或多个光学干扰单元(OIU)的一组方法,其中该方法包括计算对光子ANN的原始输入的损耗,计算基于伴随输入在计算的损失中,当计算的伴随输入和原始输入在一组OIO中彼此干扰时,测量一组OIOS中的一个或多个相移器的强度,从测量的强度计算梯度,基于计算梯度调谐OIU的相移器。

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