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All-optical neural network with nonlinear activation functions

Ying Zuo, Bohan Li, Yujun Zhao, Yue Jiang, You-Chiuan Chen, Peng Chen, Gyu-Boong Jo, Junwei Liu, Shengwang Du

Artificial neural networks (ANNs) have been widely used for industrial applications and have played a more important role in fundamental research. Although most ANN hardware systems are electronic-based, their optical implementation is particularly attractive because of its intrinsic parallelism and low energy consumption. Here, we demonstrate a fully functioning all-optical neural network (AONN), in which linear operations are programmed by spatial light modulators and Fourier lenses, while nonlinear optical activation functions are realized in laser-cooled atoms with electromagnetically induced transparency. Because all errors from different optical neurons are independent, it is possible to scale up the size of such an AONN. Moreover, our hardware system is reconfigurable for different applications without the need to modify the physical structure. We confirm its capability and feasibility in machine-learning application by successfully classifying order and disorder phases of a statistical Ising model. The demonstrated AONN scheme can be used to construct various ANN architectures with intrinsic optical parallel computation.

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