Transposed sparse matrix multiply by dense matrix for neural network training

Abstract

Machine learning systems that implement neural networks typically operate in an inference mode or a training mode. In the training mode, inference operations are performed to help guide the training process. Inference mode operation typically involves forward propagation and intensive access to certain sparse matrices, encoded as a set of vectors. Back propagation and intensive access to transposed versions of the same sparse matrices provide training refinements. Generating a transposed version of a sparse matrix can consume significant additional memory and computation resources. In one embodiment, two additional encoding vectors are generated, providing efficient operations on sparse matrices and also on transposed representations of the same sparse matrices. In a neural network the efficient operations can reduce the amount of memory needed for backpropagation and reduce power consumption.