Disclosed are a method and an apparatus for performing an operation of a convolutional layer in a convolutional neural network. The method comprises: reading unfolded feature data provided to the convolution layer and an original convolution kernel of the convolutional layer from a dynamic random access memory (DRAM); folding the unfolded feature data in at least one dimension of width and height to generate folded feature data; pre-processing the folded feature data and the original convolution kernel; storing the pre-processed folded feature data into a static random-access memory (SRAM); folding the pre-processed original convolution kernel in the at least one dimension to generate one or more folded convolution kernels corresponding to the original convolution kernel; storing the one or more folded convolution kernels in the SRAM; and reading the pre-processed folded feature data and the one or more folded convolution kernels from the SRAM into a calculation unit for convolving the pre-processed folded feature data with the one or more folded convolution kernels. By means of the method and/or apparatus in accordance with embodiments of the present disclosure, channel utilization may be improved, cache occupancy may be reduced, and operation efficiency may be improved.