Optical diffraction neural network online training method and system

Abstract

The invention provides an optical diffraction neural network online training method and system based on an optical reciprocity and phase conjugation principle. For the optical diffraction neural network online training method, in the forward propagation step, input light reaches an imaging surface through a series of phase modulators, and light field distribution of each phase modulator surface and the imaging surface is recorded at the same time; in the loss field calculation step, the error between the intensity of the image plane light field and the standard value is calculated, and the image plane phase conjugation principle light field is modulated according to the error, and the loss light field is obtained through calculation; in the step of back propagation, a complex field generation module is used for generating a loss light field, and the loss light field is subjected to back propagation, and obtained accompanying light fields are recorded on conjugate surfaces of phase modulators one by one; and in the gradient calculation and updating step, the gradient of each pixel of the phase modulator is calculated according to the phase modulator surface light field recorded in the forward propagation step and the accompanying light field recorded in the reverse propagation step, and gradient descent is performed according to the gradient, and iteration is performed until convergence.

Description

technical field [0001] The invention relates to the technical fields of optoelectronic computing and machine learning, in particular to an online training method and system for an optical diffraction neural network based on the principles of optical reciprocity and phase conjugation. Background technique [0002] Machine learning has made tremendous progress in areas such as classification, recognition, and detection of speech and images. The current mainstream machine learning platforms are all based on electronic computing components. As the manufacturing process of electronic chips is gradually approaching its limit, its energy efficiency is difficult to continue to increase. In addition, deep learning technology based on electronic chips still faces bottlenecks such as long training time and limited computing architecture. In response to the many shortcomings of electronic deep learning, researchers from all over the world have begun to study the implementation of next-...

AI Technical Summary

Problems solved by technology

However, the optical diffraction neural network needs to be modeled by an electronic computer before it is realized. This offline training method limits the training speed and application scenarios of the optical diffraction neural network.

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