METHOD AND SYSTEM FOR PREDICTING SPARSE MATRIX VECTOR MULTIPLICATION OPERATION TIME

The present invention relates to a method and system for predicting a sparse matrix vector multiplication operation time. The method comprises the following steps: constructing a convolutional neural network, wherein the convolutional neural network comprises an input layer, a feature processing layer, a data splicing layer and an output layer, the input layer being used for inputting a feature of a row feature matrix, a feature of a column feature matrix and a feature of an architecture parameter extension matrix in a sparse matrix, the feature processing layer being used for extracting the features in the previous layer, the data splicing layer being used for splicing the extracted feature of the row feature matrix, the feature of the column feature matrix and the feature of the architecture parameter extension matrix, and the output layer being used for outputting a prediction result; acquiring a plurality of groups of sparse matrices the sparse matrix vector multiplication operation times of which are kno