METHOD OF TRAINING A LOGISTIC REGRESSION CLASSIFIER WITH A RESISTIVE RANDOM ACCESS MEMORY

摘要

The present invention concerns a method for training a logistic regression classifier on a dataset by using a resistive RAM as hardware accelerator, each row of the resistive RAM comprising M cells which can be programmed in a first resistance state or a second resistance state. The probability of a data element x belonging to a class is modelled by a logistic function applied to a score w^Tx of said element, where w is a parameter vector of the model. The logistic regression classifier is trained by populating the resistive RAM with samples of a model parameter vector which are obtained by MCMC sampling. Once populated, the resistive RAM can be used for classifying new data.