Abstract: An execution method for convolution computation is disclosed, which includes: dividing an input image of N channels into a first tile to an X-th tile according to a feature tile; sequentially performing convolution computations on the data in the first tile to the X-th tile of the input image of the N channels, and storing the computation results as output data; mapping the data in each of the tiles by a kernel, and performing multiply-accumulate operations on the mapped data in each of the tiles, wherein each time the multiply-accumulate operation performed on the data mapped by the kernel is complete, the kernel is shifted to change the mapped data in said tile, and multiply-accumulate operation is performed on the changed mapped data until the multiply-accumulate operations performed on all of the data in said tile are complete, thereby finishing the convolution computation of said tile.