CPC G06N 20/00 (2019.01) [G06F 17/16 (2013.01); G06T 1/20 (2013.01)] | 20 Claims |
1. A method for processing data, comprising:
determining a factor associated with a first input of a deep learning model, wherein the factor affects the number of threads for executing the deep learning model;
generating a plurality of first partial inputs by using the first input based on the factor, wherein each first partial input in the plurality of first partial inputs is a part of the first input; and
performing an operation on the plurality of first partial inputs by using the deep learning model, and generating an output of the deep learning model;
wherein the operation is performed in parallel on select ones of the first partial inputs of the plurality of first partial inputs;
wherein the factor determined is at least one of a channel number and a size;
wherein when the factor is the size, the operation performed by the deep learning model is a cross-multiplication operation, the deep learning model further has a second input, and the first input is divided into the plurality of first partial inputs based on determined relative sizes of the first input and the second input; and
wherein the method is performed by at least one processing device comprising a processor coupled to a memory.
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