Method and apparatus for performing operation of convolutional layers in convolutional neural network

An apparatus for performing an operation of a convolutional layer in a convolutional neural network, comprising:

• one or more processors configured to execute instructions stored in a memory, execution of the instructions causing the one or more processors to perform the following steps:
• 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

A method for performing an operation of a convolutional layer in a convolutional neural network, comprising:

• folding unfolded feature data provided to the convolution layer in at least one dimension of width and height to generate folded feature data;
• pre-processing the folded feature data and an original convolution kernel of the convolution layer;
• 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;
• and
• convolving the pre-processed folded feature data with the one or more folded convolution kernels.