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Photoelectric hybrid convolution neural network with coherent nanophotonic circuits
Optical Engineering  (IF1.084),  Pub Date : 2021-11-01, DOI: 10.1117/1.oe.60.11.117106
Xiaofeng Xu, Lianqing Zhu, Wei Zhuang, Dongliang Zhang, Pei Yuan, Lidan Lu

To achieve low-power convolutional neural networks, we develop a photoelectric hybrid neural network (PHNN), which consists of the optical interference unit (OIU) and field-programmable gate array (FPGA). The OIU composed of Mach–Zehnder interferometers (MZI) arrays, used as convolution kernels, performs multiplication and accumulation operations. The convolution kernel is split and reorganized, forming a new unitary matrix, which reduces MZI quantity. FPGA realizes nonlinear calculation, data scheduling and storage, and phase encoding and modulation. Our PHNN has an accuracy rate of 88.79%, and the energy efficiency ratio is 1.73 times that of traditional electronic products.