Abstract: Example aspects include techniques for query processing over deep neural network runtimes. These techniques include receiving a query including a query operator and a trainable user defined function (UDF). In addition, the techniques include determining a query representation based on the query, and determining, for performing the query in a neural network runtime, an initial neural network program based on the query representation, the initial neural network program including a differentiable operators corresponding to the query operator. and executing the neural network program in the neural network runtime over the neural network data structure to generate a query result. Further, the techniques include training the initial neural network program via the neural network runtime to determine a trained neural network program, and executing the trained neural network program in the neural network runtime to generate inference information.

Abstract: A model generation method is for generating a machine learning model by replacing a convolution layer of a convolutional neural network with a decomposition layer by matrix decomposition. The model generation method includes sorting weight parameters constituting an original layer of the convolution layer to constitute an equivalent weight matrix equivalent to a weight matrix product which is a product of matrices of weight parameters constituting the decomposition layer, extracting a plurality of ranks by matrix decomposition on the equivalent weight matrix, and building the decomposition layer based on convolution of the weight matrix product corresponding to at least one selected ranks selected from the plurality of ranks.