A method for extracting image data in parallel from multiple convolution windows by an accelerator device including multiple data processing units (321, 322, 323, 491, 493, 494, 499, 521, 522, 523, 529) for transforming an image convolution operation into a multiplication of two-dimensional matrixes, the method comprising: dividing an image (310, 510) into N convolution windows grouped into multiple groups of convolution windows, wherein each group of convolution windows includes a number of convolution windows corresponding to the number of available data processing units (321, 322, 323, 491, 493, 494, 499, 521, 522, 523, 529); extracting image data in parallel from the convolution windows (311, 312, 313, 511) in a first group of convolution windows of the multiple groups of convolution windows by using the available data processing units (321, 322, 323, 491, 493, 494, 499, 521, 522, 523, 529), wherein extracting image data from a convolution window by a corresponding processing unit (521, 522, 523, 529) comprises representing corresponding image data by using a one-dimensional vector having a length of Cx Rx S, wherein C is a number of channels in the image (310, 510), R is a number of rows in each convolution window, and S is a number of columns in each convolution window; extracting, in response to completing the extraction of image data from the first group of convolution windows, image data in parallel from the convolution windows in a second group of convolution windows of the multiple groups of convolution windows by using the available data processing units (321, 322, 323, 491, 493, 521, 522, 523, 529), wherein the extracting the image data is repeated until the image data in all N convolution windows of the image (310, 510) is extracted; and storing all extracted image data in the multiple groups of convolution windows into a target memory by using a two-dimensional matrix having N rows and CxRxS columns, wherein a convolution kernel represented by a two-dimensional matrix having F rows and CxRxS columns is transposed into a two-dimensional matrix having CxRxS rows and F columns, so that an image convolution operation is transformed into a multiplication of the two two-dimensional matrixes. dividing an image (310, 510) into N convolution windows grouped into multiple groups of convolution windows, wherein each group of convolution windows includes a number of convolution windows corresponding to the number of available data processing units (321, 322, 323, 491, 493, 494, 499, 521, 522, 523, 529); extracting image data in parallel from the convolution windows (311, 312, 313, 511) in a first group of convolution windows of the multiple groups of convolution windows by using the available data processing units (321, 322, 323, 491, 493, 494, 499, 521, 522, 523, 529), wherein extracting image data from a convolution window by a corresponding processing unit (521, 522, 523, 529) comprises representing corresponding image data by using a one-dimensional vector having a length of Cx Rx S, wherein C is a number of channels in the image (310, 510), R is a number of rows in each convolution window, and S is a number of columns in each convolution window; extracting, in response to completing the extraction of image data from the first group of convolution windows, image data in parallel from the convolution windows in a second group of convolution windows of the multiple groups of convolution windows by using the available data processing units (321, 322, 323, 491, 493, 521, 522, 523, 529), wherein the extracting the image data is repeated until the image data in all N convolution windows of the image (310, 510) is extracted; and storing all extracted image data in the multiple groups of convolution windows into a target memory by using a two-dimensional matrix having N rows and CxRxS columns, wherein a convolution kernel represented by a two-dimensional matrix having F rows and CxRxS columns is transposed into a two-dimensional matrix having CxRxS rows and F columns, so that an image convolution operation is transformed into a multiplication of the two two-dimensional matrixes.