All-optical image identification with programmable matrix transformation

Abstract

An optical neural network is proposed and demonstrated with programmable matrix transformation and nonlinear activation function of photodetection (square-law detection). Based on discrete phase-coherent spatial modes, the dimensionality of programmable optical matrix operations is 30∼37, which is implemented by spatial light modulators. With this architecture, all-optical classification tasks of handwritten digits, objects and depth images are performed. The accuracy values of 85.0% and 81.0% are experimentally evaluated for MNIST (Modified National Institute of Standards and Technology) digit and MNIST fashion tasks, respectively. Due to the parallel nature of matrix multiplication, the processing speed of our proposed architecture is potentially as high as 7.4∼74 T FLOPs per second (with 10∼100 GHz detector).