Model training device, method and system and computer readable storage medium

Abstract

The invention discloses a model training device, method and system and a computer readable storage medium, and the device comprises: a high-bandwidth storage group which is used for storing to-be-trained data; a global memory which is used for storing model parameters of the target model; a processor which is connected with the high-bandwidth memory bank and the global memory and is used for operating the to-be-trained data and the initial model parameters stored in the global memory based on a training algorithm of the target model to obtain target model parameters and storing the target model parameters into the global memory, wherein the processor is established based on the network-on-chip. In the present application, the storage capacity of the high-bandwidth memory is large, so thatmore to-be-trained data can be stored, and the read-write frequency of the high-bandwidth memory is reduced; the processor is built based on the network-on-chip, so that the processor can transmit corresponding data in the processor by means of the network-on-chip, the read-write frequency of the data to be transmitted is reduced, and compared with the prior art, the training efficiency of the model can be improved.

Description

technical field [0001] The present application relates to the technical field of model training, and more specifically, to a model training device, method, system, and computer-readable storage medium. Background technique [0002] At present, models such as neural network models are more and more widely used. In order to ensure the effect of the model, it is necessary to train the model to ensure the processing accuracy of the model. During the training process of the model, in order to improve the calculation speed and increase the parallelism, Multiple computing cores are used to train the model. However, during the training process, the calculated data needs to be transmitted. The existing method is to store the data to be transmitted in the shared memory. When the data needs to be used, it is read from the shared memory. fetch data. [0003] However, when the training process of the model is complex and the amount of calculation is large, the shared memory will be freq...

AI Technical Summary

Problems solved by technology

[0003]However, when the training process of the model is complex and the amount of calculation is large, the shared memory will be frequently read, causing bus and access conflicts, which will affect the efficiency of model training

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