We propose all-optical neural networks characterized by very high energy efficiency and performance density of inference. We argue that the use of microcavity exciton-polaritons allows to take advantage of the properties of both photons and electrons in a seamless manner. This results in strong optical nonlinearity without the use of optoelectronic conversion. We propose a design of a realistic neural network and estimate energy cost to be at the level of attojoules per bit, also when including the optoelectronic conversion at the input and output of the network, several orders of magnitude below state-of-the-art hardware implementations. We propose two kinds of nonlinear binarized nodes based either on optical phase shifts and interferometry or on polariton spin rotations.

中文翻译：

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具有微腔激子极化子的节能神经网络推理

我们提出了具有非常高的能量效率和推理性能密度的全光神经网络。我们认为使用微腔激子极化子可以无缝地利用光子和电子的特性。这导致在不使用光电转换的情况下产生很强的光学非线性。我们提出了一种现实神经网络的设计，并将能量成本估计在每比特阿焦耳的水平，包括网络输入和输出处的光电转换，比最先进的技术低几个数量级硬件实现。我们提出了两种基于光学相移和干涉测量法或极化子自旋旋转的非线性二值化节点。