A computer implemented method for processing overhead of memory access, comprising: applying for a memory to perform value padding on at least one convolution operation in a deep learning model; determining input data of the deep learning model; performing deep learning processing on the input data by using the deep learning model; and releasing the memory, wherein, the deep learning model comprises N convolution operations which are set in sequence, and N is an integer greater than 1; the applying for a memory to perform value padding on at least one convolution operation in the deep learning model comprises: determining a size of input data of a first convolution operation in the deep learning model; determining, according to the size of the input data of the first convolution operation, sizes of output data of the N convolution operations and sizes of input data, after being subjected to the value padding, of the N convolution operations; determining K convolution operation sets according to the sizes of the input data, after being subjected to the value padding, of the N convolution operations, wherein the N convolution operations are composed of the K convolution operation sets, and sizes of output data of successive convolution operations contained in each convolution operation set meet a rule of no increase; and applying for K memories, wherein the K memories are in one-to-one correspondence to the K convolution operation sets, capacities of the K memories respectively correspond to the size of the input data, after being subjected to the value padding, of the first convolution operation in the K convolution operation sets, and K is an integer smaller than N; the performing deep learning processing on the input data by using the deep learning model comprises: prior to performing each convolution operation, performing the value padding on input data of the each convolution operation by using each memory corresponding to each convolution operation set to which the each convolution operation belongs. applying for a memory to perform value padding on at least one convolution operation in a deep learning model; determining input data of the deep learning model; performing deep learning processing on the input data by using the deep learning model; and releasing the memory, wherein, the deep learning model comprises N convolution operations which are set in sequence, and N is an integer greater than 1; the applying for a memory to perform value padding on at least one convolution operation in the deep learning model comprises: determining a size of input data of a first convolution operation in the deep learning model; determining, according to the size of the input data of the first convolution operation, sizes of output data of the N convolution operations and sizes of input data, after being subjected to the value padding, of the N convolution operations; determining K convolution operation sets according to the sizes of the input data, after being subjected to the value padding, of the N convolution operations, wherein the N convolution operations are composed of the K convolution operation sets, and sizes of output data of successive convolution operations contained in each convolution operation set meet a rule of no increase; and applying for K memories, wherein the K memories are in one-to-one correspondence to the K convolution operation sets, capacities of the K memories respectively correspond to the size of the input data, after being subjected to the value padding, of the first convolution operation in the K convolution operation sets, and K is an integer smaller than N; determining a size of input data of a first convolution operation in the deep learning model; determining, according to the size of the input data of the first convolution operation, sizes of output data of the N convolution operations and sizes of input data, after being subjected to the value padding, of the N convolution operations; determining K convolution operation sets according to the sizes of the input data, after being subjected to the value padding, of the N convolution operations, wherein the N convolution operations are composed of the K convolution operation sets, and sizes of output data of successive convolution operations contained in each convolution operation set meet a rule of no increase; and applying for K memories, wherein the K memories are in one-to-one correspondence to the K convolution operation sets, capacities of the K memories respectively correspond to the size of the input data, after being subjected to the value padding, of the first convolution operation in the K convolution operation sets, and K is an integer smaller than N; the performing deep learning processing on the input data by using the deep learning model comprises: prior to performing each convolution operation, performing the value padding on input data of the each convolution operation by using each memory corresponding to each convolution operation set to which the each convolution operation belongs.