Ferroelectric in-memory computing

Abstract

A computer program product for training a machine learning model. A processor executes program instructions stored on a computer readable media to perform a forward pass read by applying a bias input voltage to a gate of a first resistive processing unit (RPU) configured for infrequent writes and storing a first conductance value representing a synaptic weight value to read from the first RPU a product of the bias input voltage and the first conductance value. The processor performs a backward pass to compute a loss value for the forward pass read and to compute a gradient value to minimize the loss value. The processor applies a coercive input voltage to a gate of a second RPU configured differently for frequent writes to write to the second RPU a second conductance value related to the gradient value and representing a gradient accumulation value.

Dependencies are inferred from the claim text.