Method for processing data using an artificial neural network, the method being implemented by a computer system , the artificial neural network comprising several successive pooling (L1, L2) or convolutional (C1, C2) layers of neurons (N11, N) all associated with tensors of input values (MI1) of said layers, each neuron being associated with a receptive field (RCA, RCA) for input values belonging to a tensor (MI1, MI2) of input values of the layer of said neuron, the processing comprising: grouped executions (EXEC_GR) of individual operations which make it possible to obtain the values of neurons of the several successive layers, each grouped execution comprising individual operations on a group of input values of a tensor of input values, the values of this group of values being chosen to correspond to a submatrix associated with this grouped execution, this submatrix being an extract resulting from a division of a matrix referred to as reorganized matrix (RMI1), associated with this tensor of input values and wherein each column (CA, CB, CC, CD) corresponds to a receptive field (RCA, RCB, RCC, RCD) for input values of the tensor of input values of the layer and each row of this column corresponds to a value of said receptive field for input values, the division of the reorganized matrix being configured to divide the reorganized matrix into a plurality of contiguous or overlapping submatrices (B1, ..., B6) having random widths and given heights, and the grouped executions (EXEC_GR) being implemented according to: - the availability of the values of the submatrices of each grouped execution, - a given order of execution of the submatrices of the same layer if the values of several submatrices are available, and, if all the individual operations of a column of a reorganized matrix have been executed, the value of one or more neurons corresponding to the receptive field of the column is obtained. grouped executions (EXEC_GR) of individual operations which make it possible to obtain the values of neurons of the several successive layers, each grouped execution comprising individual operations on a group of input values of a tensor of input values, the values of this group of values being chosen to correspond to a submatrix associated with this grouped execution, this submatrix being an extract resulting from a division of a matrix referred to as reorganized matrix (RMI1), associated with this tensor of input values and wherein each column (CA, CB, CC, CD) corresponds to a receptive field (RCA, RCB, RCC, RCD) for input values of the tensor of input values of the layer and each row of this column corresponds to a value of said receptive field for input values, the division of the reorganized matrix being configured to divide the reorganized matrix into a plurality of contiguous or overlapping submatrices (B1, ..., B6) having random widths and given heights, and the grouped executions (EXEC_GR) being implemented according to: - the availability of the values of the submatrices of each grouped execution, - a given order of execution of the submatrices of the same layer if the values of several submatrices are available, - the availability of the values of the submatrices of each grouped execution, - a given order of execution of the submatrices of the same layer if the values of several submatrices are available, and, if all the individual operations of a column of a reorganized matrix have been executed, the value of one or more neurons corresponding to the receptive field of the column is obtained. <EOS>