Artificial intelligence accelerator and operation thereof

A processing method applied to an artificial intelligence accelerator, wherein the artificial intelligence accelerator receives a binary input data set and a selected layer of a plurality of layers of an overall weight pattern to perform a convolution operation, and the input data set is divided into a plurality of data subsets, and the processing method comprises:

• using a plurality of processing tiles, and each of the processing tiles comprises operations of:
• using a receive-end component to receive one of the data subsets;
• using a weight storage unit to store a part of the overall weight pattern, wherein the partial weight storage unit comprises a plurality of weight blocks, and each of the weight blocks stores a block part of the partial weight pattern in order of bits, wherein a cell array structure of the weight storage unit, with respect to a corresponding one of the data sets, is configured
• to perform a convolution operation on the data subset with each block part respectively to obtain a plurality of sequential weight operation values;
• using a block-wise output circuit that comprises a plurality of shifters and a plurality of adders to sum up the plurality of weight operation values through a multistage shifting and adding operation, so as to obtain a weight output value expected from a direct convolution operation performed on the data subset with the partial weight pattern;
• and
• using a summation output circuit that comprises a plurality of shifters and a plurality of adders to sum up the plurality of weight output values through a multistage shifting and adding operation, so as to obtain a sum value expected from a direct convolution operation performed on the input data set with the overall weight pattern.