Deep neural network learning method, processor and deep neural network learning system

Abstract

Embodiments of the present invention provide a deep neural network learning method. The method comprises: conducting, by a plurality of processors, forward processing on data distributed to the processors layers in parallel layer by layer from a first layer to a last layer, and acquiring error information when forward processing is finished; and conducting, by the plurality of processors, backward processing on the error information layer by layer from last layer to first layer, wherein each of the plurality of processors immediately transfers a parameter correction value to other processors after backward processing of a current layer of a corresponding deep neural network model generates the parameter correction value. With the method according to the embodiments of the present invention, time consumed by transfer of the parameter correction values is reduced, and efficiency of training the deep neural network models is effectively improved; and particularly under the conditions of a large volume of training data and a great number of layers of each deep neural network model, such manner can greatly reduce used time, and effectively save model training time. Further, the embodiments of the present invention provide a processor, and a deep neural network learning system.

Description

technical field [0001] Embodiments of the present invention relate to the field of neural networks, and more specifically, embodiments of the present invention relate to a deep neural network learning method, a processor, and a deep neural network learning system. Background technique [0002] This section is intended to provide a background or context for implementations of the invention that are recited in the claims. The descriptions herein are not admitted to be prior art by inclusion in this section. [0003] Deep neural network learning is a new field in machine learning research. It interprets data by imitating the mechanism of the human brain. It is an intelligent model that analyzes and learns by establishing and simulating the human brain. [0004] Such as figure 1 The schematic diagram of the deep neural network model is shown, and the general training data will be sent to the model for training in sequence. However, when there are a lot of data to be trained, ...

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Problems solved by technology

[0006] However, because of the addition of the transmission process of the parameter correction amount, the model training time is extended, and the training efficiency is correspondingly reduced, which is obviously contrary to the original purpose of parallel processing to improve training efficiency.

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