The invention discloses a row fixed
data stream mapping method based on graph segmentation, and mainly solves the problems of limited application scene and low
utilization rate of a
processing array in the existing row fixed
data stream mapping method. The method comprises the following implementation steps: 1, acquiring relevant parameters of a
convolutional neural network convolutional layer anda
processing array; 2, generating a mapping graph according to the parameters of the convolutional layer, and determining relevant parameters of the mapping graph; 3, performing mapping graph segmentation according to the mapping graph parameters and the
processing array related parameters; and 4, generating corresponding data
flow mapping according to a graph segmentation result. According to the invention, the mapping
graph based on the row fixed data flow is segmented and mapped according to the processing array scale; while the high data
reusability characteristic of the line fixed data flow is kept, the convolutional layer of any scale can be mapped into the processing array of any scale, and the method has the advantages of being high in flexibility, high in applicability, high in processing unit
utilization rate and high in processing performance, and can be used for accelerating the
data processing process of the
convolutional neural network.