Vectorization implementation method for pooling of multi-sample multi-channel convolutional neural network

Abstract

The invention discloses a vectorization implementation method for pooling of a multi-sample multi-channel convolutional neural network. The method comprises the following steps: step 1, storing inputfeature data set data of a convolutional neural network pooling layer according to a sample dimension priority mode; 2, dividing an input feature data set data matrix into a plurality of matrix blocksby the vector processor according to columns; 3, sequentially extracting matrix blocks with specified sizes by the vector processor according to rows, and transmitting the matrix blocks to a data buffer area of an array memory of the vector processor; 4, each core of the vector processor performs pooling vectorization calculation on the matrix blocks in the respective data buffer area in parallel, and calculation results are transmitted to an off-chip memory in sequence; and step 5, repeating the step 3 to the step 4 until all pooling layer calculation is completed. The method can give full play to the calculation performance of the vector processor, and has the advantages of simple implementation method, high implementation efficiency, low power consumption, good effect and the like.

Description

technical field [0001] The invention relates to the technical field of vector processors, in particular to a method for realizing vectorization of multi-sample multi-channel convolutional neural network pooling. Background technique [0002] In recent years, deep learning models based on deep convolutional neural networks have made remarkable achievements in image recognition and classification, target detection, video analysis, etc. The rapid development of related technologies such as data processing and processors. Convolutional Neural Networks (CNN) is a type of Feedforward Neural Networks (Feedforward Neural Networks) that includes convolution calculations and has a deep structure. It is one of the representative algorithms for deep learning. The input layer of the convolutional neural network can process multi-dimensional data. Since the convolutional neural network is the most widely used in the field of computer vision, when designing the convolutional neural networ...

AI Technical Summary

Problems solved by technology

However, the size of the third dimension in this type of method is uncertain, which does not match the number of processing units of the vector processor, and the size of the third dimension of different convolutional neural network models and different convolutional layers is different, which makes the loading data efficiency of this type of method Will be greatly affected and not universal

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