Data sparsity monitoring during neural network training

Abstract

An electronic device that includes a processor configured to execute training iterations during a training process for a neural network, each training iteration including processing a separate instance of training data through the neural network, and a sparsity monitor is described. During operation, the sparsity monitor acquires, during a monitoring interval in each of one or more monitoring periods, intermediate data output by at least some intermediate nodes of the neural network during training iterations that occur during each monitoring interval. The sparsity monitor then generates, based at least in part on the intermediate data, one or more values representing sparsity characteristics for the intermediate data. The sparsity monitor next sends, to the processor, the one or more values representing the sparsity characteristics and the processor controls one or more aspects of executing subsequent training iterations based at least in part on the values representing the sparsity characteristics.