Convolution calculation method and related equipment

Abstract

The invention discloses a convolution calculation method and related equipment, and the method comprises the following steps: obtaining the input data of any one network layer A in N network layers included in a convolution neural network model, N being an integer greater than 1; determining a target convolution kernel and a target convolution step length corresponding to the network layer A according to a mapping relationship between the network layer and the convolution kernel and the convolution step length; determining M processing element groups required for performing convolution calculation on the input data of the network layer A according to the target convolution kernel and the target convolution step length, wherein each processing element group in the M processing element groups comprises P1 processing elements, each processing element in the P1 processing elements comprises at least one multiply-accumulate unit, and M and P1 are integers greater than or equal to 1; and performing convolution calculation on the input data of the network layer A by using the M processing element groups in parallel to obtain output data of the network layer A. By adopting the embodiment of the invention, the calculation efficiency of convolution calculation in the neural network processor can be improved.

Description

technical field [0001] This application relates to the technical field of artificial intelligence, in particular to a convolution calculation method and related equipment. Background technique [0002] One of the most commonly used models in neural network processors is the convolutional neural network model. However, the convolutional neural network model has a problem of low computational efficiency when performing convolution operations. Therefore, how to improve the calculation efficiency of convolution calculation in the neural network processor has become a technical problem to be solved urgently. Contents of the invention [0003] Embodiments of the present application provide a convolution calculation method and related equipment, which are used to improve the calculation efficiency of convolution calculation in a neural network processor. [0004] In the first aspect, the embodiment of the present application provides a convolution calculation method applied to a...

AI Technical Summary

Problems solved by technology

[0002] One of the most commonly used models in neural network processors is the convolutional neural network model. However, the convolutional neural network model has the problem of low computational efficiency when performing convolution operations.

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