Recursive neural network using random access memory

Abstract

Techniques for manipulation of a recursive neural network using random access memory are disclosed. Neural network descriptor information and weight matrices are stored in a semiconductor random access memory device which includes neural network processing logic. The network descriptor information and weight matrices comprise a trained neural network functionality. An input matrix is obtained for processing on the memory device. The trained neural network functionality is executed on the input matrix, which includes processing data for a first layer from the neural network descriptor information to set up the processing logic; manipulating the input matrix using the processing logic and at least one of the weight matrices; caching results of the manipulating in a storage location of the memory device; and processing recursively the results that were cached through the processing logic. Additional data for additional layers is processed until the neural network functionality is complete.