Apparatus and method for performing convolutional neural network training

Abstract

The present invention provides an apparatus and a method for performing convolution neural network inverse training. The apparatus comprises an instruction storage unit, a controller unit, a data access unit, an interconnection module, a main computing module, and a plurality of slave computing modules. The method comprises: for each layer, carrying out data selection on the input neuron vector according to the convolution window; and taking the data from the previous layer and the data gradient from the subsequent layer that are obtained according to selection as the inputs of the computing unit of the apparatus; calculating and updating the convolution kernel; and according to the convolution kernel, the data gradient, and the derivative function of the activation function, calculating the data gradient output by the apparatus, and storing the data gradient to a memory so as to output to the previous layer for inverse propagation calculation. According to the apparatus and method provided by the present invention, data and weight parameters involved in the calculation are temporarily stored in the high-speed cache memory, so that convolution neural network inverse training can be supported more flexibly and effectively, and the executing performance of the application containing a large number of memory access is improved.

Description

technical field [0001] The invention relates to a device and method for performing reverse training of convolutional neural network, which is used to efficiently and flexibly execute reverse training operation of convolutional neural network according to the reverse training operation instruction of convolutional neural network, and can well solve the problem of More and more algorithms in the current computer field include a large number of reverse training operations of convolutional neural networks. Background technique [0002] Convolutional neural network is an efficient recognition algorithm widely used in pattern recognition, image processing and other fields in recent years. It has the characteristics of simple structure, few training parameters and strong adaptability, translation, rotation and scaling. Since the feature detection layer of CNN / DNN learns through training data, when using CNN / DNN, it avoids explicit feature extraction and learns implicitly from train...

AI Technical Summary

Problems solved by technology

[0016] The purpose of the present invention is to provide a device that supports reverse training of convolutional neural networks, so as to solve the problems existing in the prior art, such as limited inter-chip communication, insufficient on-chip cache, etc.

Images