Low Power Artificial Neural Network Architecture

Abstract

Recent artificial neural network architectures improve performance and power dissipation by leveraging resistive devices to store and multiply synaptic weights with input data. Negative and positive synaptic weights are stored on the memristors of a reconfigurable crossbar array (MCA). Existing MCA-based neural network architectures use high power consuming voltage converters or operational amplifiers to generate the total synaptic current through each column of the crossbar array. This paper presents a low power MCA-based feedforward neural network architecture that uses a spintronic device per pair of columns to generate the synaptic current for each neuron. It is shown experimentally that the proposed architecture dissipates significantly less power compared to existing feedforward memristive neural network architectures.