| CPC G06V 10/955 (2022.01) [G06F 7/533 (2013.01); G06F 18/2163 (2023.01); G06N 3/04 (2013.01); G06V 10/454 (2022.01); G06V 10/82 (2022.01)] | 22 Claims |
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1. A processor-implemented method implementing a convolution neural network, the method comprising:
generating a plurality of differential groups by grouping a plurality of raw windows of an input feature map into the plurality of differential groups, the plurality of raw windows are sub-feature maps of the input feature map;
generating differential windows by performing, for each respective differential group of the differential groups, a differential operation between raw windows of the respective differential group, the differential operation being an element-wise differential operation between two raw windows of each respective differential group;
determining a reference element of an output feature map corresponding to a reference raw window among the raw windows by performing a convolution operation between a kernel and the reference raw window; and
determining remaining elements of the output feature map by performing a reference element summation operation based on the reference element and each of a plurality of convolution operation results determined by performing respective convolution operations between the kernel and each of the differential windows.
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