Neural network operation method and apparatus with stacking-based channel alignment

1 . A neural network operation method comprising:

receiving data for a neural network operation, the data comprising a first map that is a feature map comprising first valid channels, and comprising a second map that is a feature map comprising second valid channels;

determining whether a size of the data is less than or equal to a threshold;

in response to a determination that the size of the data is less than or equal to the threshold, generating stacked data based on combining at least a portion of the first map with at least a portion of the second map such that a last valid channel of the first map is adjacent to a first valid channel of the second map in the stacked data; and

performing a first convolution operation for the first map and a second convolution operation for the second map in parallel using the stacked data; and

generating, in parallel, a first output of a result of the first convolution operation and a second output of a result of the second convolution operation, wherein a number of outputs generated based on a convolution operation on the stacked data is equal to a number of feature maps used to generate the stacked data.

2 . The neural network operation method of claim 1 , wherein the performing comprises performing a convolution operation between the stacked data and a stacked kernel corresponding to the stacked data.

3 . The neural network operation method of claim 1 , wherein the generating of the stacked data comprises:

generating tiles by segmenting the data to have a predetermined width or a predetermined height; and

generating the stacked data by stacking the tiles.

4 . The neural network operation method of claim 1 , wherein the generating of the stacked data comprises generating the stacked data by inputting the data to a direct memory access (DMA) engine.

5 . The neural network operation method of claim 1 , wherein the generating of the stacked data further comprises:

searching for additional data to perform a second neural network operation that is different from the neural network operation performed based on the data;

determining whether the additional data and the data are stackable; and

performing the neural network operation and the second neural network operation in parallel by stacking the additional data and the data based on a result of the determining whether the additional data and the data are stackable.

6 . A non-transitory computer-readable storage medium storing instructions that, when executed by a processor, cause the processor to perform the method of claim 1 .

7 . A neural network operation apparatus comprising:

one or more processors; and

a memory storing instructions that when executed by the one or more processors cause the neural network operation apparatus to:

determine whether a size of the data for a neural network operation is less than or equal to a threshold, the data comprising a first map that is a feature map comprising first valid channels, and comprising a second map that is a feature map comprising second valid channels,

in response to a determination that the size of the data is less than or equal to the threshold, generate stacked data based on combining at least a portion of the first map with at least a portion of the second map such that a last valid channel of the first map is adjacent to a first valid channel of the second map,

perform a first convolution operation for the first map and a second convolution operation for the second map in parallel using the stacked data, and

generate, in parallel, a first output of a result of the first convolution operation and a second output of a result of the second convolution operation, wherein a number of outputs generated based on a convolution operation on the stacked data is equal to a number of feature maps used to generate the stacked data.

8 . The neural network operation apparatus of claim 7 , wherein the instructions are further configured to cause the neural network operation apparatus to perform a convolution operation between the stacked data and a stacked kernel corresponding to the stacked data.

9 . The neural network operation apparatus of claim 7 , wherein the instructions are further configured to cause the neural network operation apparatus to:

generate tiles by segmenting the data to have a predetermined width or a predetermined height, and

generate the stacked data by stacking the tiles.

10 . The neural network operation apparatus of claim 7 , wherein the instructions are further configured to cause the neural network operation apparatus to generate the stacked data by inputting the data to a direct memory access (DMA) engine.

11 . The neural network operation method of claim 1 , wherein, in the stacked data, the first valid channel is self-contiguous and the second valid channel is self-contiguous.

12 . The neural network operation apparatus of claim 7 , wherein, in the stacked data, the first valid channel is self-contiguous and the second valid channel is self-contiguous.