Optical neural network convolution layer chip, convolution calculation method and electronic equipment

Abstract

An optical neural network convolution layer chip is applied to the field of artificial intelligence and comprises a first coupler, a first beam splitter, a plurality of photon calculation modules anda convolution summation module which are connected in sequence, wherein the first coupler is used for coupling a received optical signal into the first beam splitter; the first beam splitter comprisesa plurality of output ports, the beam splitter is used for splitting the coupled optical signals to obtain a plurality of beams of optical signals, and the plurality of beams of optical signals are input to the photon calculation modules through the output ports one by one; the photon calculation module is used for carrying out amplitude modulation and phase modulation on each beam of optical signals so as to represent input data and a convolution kernel parameter through each beam of modulated optical signals, and converting all the modulated optical signals into electric signals; and the convolution summation module is used for carrying out convolution summation on all the electric signals and completing photon convolution operation of all the input data and convolution kernel parameters. Photons have the characteristics of high speed, high bandwidth and low power consumption, convolution calculation is realized by utilizing the photons, the calculation speed can be greatly improved, and the calculation energy consumption is reduced.

Description

technical field [0001] The present application relates to the field of artificial intelligence, in particular to an optical neural network convolution layer chip, a convolution calculation method and electronic equipment. Background technique [0002] A neural network refers to a series of mathematical models inspired by biology. In recent years, neural network technology has become an important driving force for the development of artificial intelligence technology, and is widely used in image processing, speech recognition, natural language processing and other fields. Convolution operations are an important type of calculation in neural networks, especially in convolutional neural networks. [0003] Chip technology provides powerful computing power for neural network for intelligent processing, supporting the development of neural network technology. With the sharp increase in the amount of data to be processed and the complexity of the neural network model, the von Neu...

AI Technical Summary

Problems solved by technology

With the sharp increase in the amount of data to be processed and the complexity of the neural network model, the von Neumann architecture bottleneck of traditional electronic chip technology and the bottleneck of CMOS technology and devices have become more prominent, resulting in chip power consumption and performance improvements. Problems restrict the application and popularization of neural network technology

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