Image recognition method and device based on optical neural network structure and electronic equipment

Abstract

The invention discloses an image recognition method based on an optical neural network structure, an image recognition device and an electronic device, and the optical neural network structure consists of an X-layer neural network; The image recognition method comprises the steps of obtaining a to-be-recognized image; inputting the to-be-identified image into the optical neural network structure;determining a recognition result of the to-be-recognized image based on an output result of the optical neural network structure; wherein the optical neural network structure is used for obtaining aninput vector of the ith neural network for the ith neural network, and i is a positive integer greater than 0 and less than X + 1; performing linear transformation on the input vector based on Yi inner product calculation units to obtain Yi linear transformation results; activating the Yi linear transformation results through a nonlinear crystal to obtain Yi activation results; and taking the Yi activation results as output vectors of the layer of neural network. According to the scheme, a novel optical neural network structure is applied, and the image recognition speed is further increased.

Description

technical field [0001] The invention belongs to the technical field of data processing, and in particular relates to an image recognition method, device and electronic equipment based on an optical neural network structure. Background technique [0002] Currently, machine learning has become a very important tool. Among them, deep learning based on deep neural network has received extensive attention and has been applied in important fields such as image recognition, speech recognition, and natural language translation. Among them, deep learning based on the traditional central processing unit (Central Processing Unit, CPU) is not the optimal solution; R&D personnel have developed a variety of hardware structures to meet the requirements of deep learning algorithms, such as graphics processing units (Graphics Processing Unit, GPU ) and Tensor Processing Unit (TPU). Although they can accelerate deep learning algorithms, these hardware structures are often based on electroni...

AI Technical Summary

Problems solved by technology

Although they can accelerate deep learning algorithms, these hardware structures are often based on electronic components, and their calculation speed cannot exceed the theoretical limit of linear polynomial growth, which is likely to affect the speed and efficiency of image recognition and other operations.

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