Efficient temporal memory for sparse binary sequences

Abstract

Systems and computer-implemented methods for training a machine learnable model and for using the machine learned model for inference, both of which using only limited memory resources. During training and inference, the machine learnable model uses previous state information. A state memory is provided which efficiently stores this previous state information. Instead of storing each previous state individually and integrally, for each element of the internal state, a value is stored in the state memory which is indicative of a most recent occurrence of an element of the internal state of the machine learnable model holding or transitioning to a particular binary state value. The states of the machine learnable model are representable as binary values and when states infrequently hold or transition to a particular binary state value.