Neural network computing method and device, mobile terminal and storage medium

Abstract

The embodiment of the invention discloses a neural network calculation method and device, a mobile terminal and a storage medium, and the method comprises the steps: obtaining M to-be-executed operators, calculating the dependency relationship among the M to-be-executed operators, and enabling N to be an integer greater than or equal to 2; cutting the M operators to be executed according to the dependency relationship among the M operators to be executed to obtain N operator sets, wherein each operator set in the N operator sets at least comprises one operator, and N is an integer larger thanor equal to 2; And if the N operator sets are mutually independent operator sets, starting N threads to calculate operators in the N operator sets respectively. According to the embodiment of the invention, the reasoning time of the neural network can be reduced.

Description

technical field [0001] The present application relates to the field of communication technology, and in particular to a neural network calculation method, device, mobile terminal and storage medium. Background technique [0002] In the current neural network algorithm framework (for example, Tensorflow Lite), when performing neural network calculations, all operators that need to be executed are added to a queue to be executed, and then the processor calls and executes these operators in turn, that is, in These operators are executed sequentially in one thread. As the neural network becomes more complex and the number of operators increases, the inference time of the neural network will also become longer. Contents of the invention [0003] Embodiments of the present application provide a neural network calculation method, device, mobile terminal, and storage medium, which can reduce the inference time of the neural network. [0004] In the first aspect, the embodiment o...

AI Technical Summary

Problems solved by technology

As the neural network becomes more and more complex, the number of operators increases, which will also lead to longer inference time of the neural network.

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