A signal processing method, wherein the signal processing method is for processing input signals such as a speech signal, a text signal, an image signal or a temperature signal in a convolutional neural network, CNN, and is applied to a device comprising a processor, wherein the processor performs a convolution operation in which convolution of an input signal feature and a weight is converted into a matrix multiplication operation between a signal matrix and a weight matrix, and the method comprises: obtaining a signal matrix and a weight matrix, wherein the signal matrix may come from an input layer or an intermediate layer of the convolutional neural network and is a two-dimensional matrix and comprises a plurality of signals that are capable of being processed by a computer, the weight matrix is a two-dimensional matrix and comprises a plurality of weight coefficients, and a number of columns of the signal matrix is equal to a number of rows of the weight matrix such that the matrices can be multiplied; dividing the signal matrix to obtain a plurality of first fractional signal matrices of X rows and H columns, and dividing the weight matrix to obtain a plurality of first fractional weight matrices of H rows and Y columns, wherein neither the first fractional signal matrices nor the first fractional weight matrices approximate to a square shape such that the first fractional signal matrices and the first fractional weight matrices are rectangular matrices; and performing matrix multiplication and accumulation operation on the plurality of first fractional signal matrices and the plurality of first fractional weight matrices, to obtain a plurality of matrix operation results, wherein the plurality of matrix operation results are used to form a signal processing result, each matrix operation result comprises accumulation of a plurality of matrix multiplication results, and each matrix multiplication result is obtained by performing a matrix multiplication operation on one first fractional signal matrix and one first fractional weight matrix. obtaining a signal matrix and a weight matrix, wherein the signal matrix may come from an input layer or an intermediate layer of the convolutional neural network and is a two-dimensional matrix and comprises a plurality of signals that are capable of being processed by a computer, the weight matrix is a two-dimensional matrix and comprises a plurality of weight coefficients, and a number of columns of the signal matrix is equal to a number of rows of the weight matrix such that the matrices can be multiplied; dividing the signal matrix to obtain a plurality of first fractional signal matrices of X rows and H columns, and dividing the weight matrix to obtain a plurality of first fractional weight matrices of H rows and Y columns, wherein neither the first fractional signal matrices nor the first fractional weight matrices approximate to a square shape such that the first fractional signal matrices and the first fractional weight matrices are rectangular matrices; and performing matrix multiplication and accumulation operation on the plurality of first fractional signal matrices and the plurality of first fractional weight matrices, to obtain a plurality of matrix operation results, wherein the plurality of matrix operation results are used to form a signal processing result, each matrix operation result comprises accumulation of a plurality of matrix multiplication results, and each matrix multiplication result is obtained by performing a matrix multiplication operation on one first fractional signal matrix and one first fractional weight matrix.