Reconfigurable classifier based on spin torque driven magnetization switching in electrically connected magnetic tunnel junctions

TL;DR

This paper introduces a reconfigurable neuromorphic classifier using magnetic tunnel junctions that can learn to classify spoken vowels, outperforming traditional software neural networks with similar parameters.

Contribution

The work demonstrates a hardware-based, reconfigurable neural network using magnetic tunnel junctions capable of learning and classifying spoken vowels, leveraging existing STT-MRAM technology.

Findings

A network of three magnetic tunnel junctions achieved high vowel classification accuracy.

The hardware classifier outperformed software neural networks with the same number of parameters.

The approach paves the way for large-scale neuromorphic systems using established magnetic memory technology.

Abstract

A promising branch of neuromorphic computing aims to perform cognitive operations in hardware leveraging the physics of efficient and well-established nano-devices. In this work, we present a reconfigurable classifier based on a network of electrically connected magnetic tunnel junctions that categorizes information encoded in the amplitude of input currents through the spin torque driven magnetization switching output configuration. The network can be trained to classify new data by adjusting additional programming currents applied selectively to the junctions. We experimentally demonstrate that a network composed of three magnetic tunnel junctions can learn to classify spoken vowels with a recognition rate that surpasses the performance of software multilayer neural networks with the same number of trained parameters in this task. These results, obtained with the same nano-devices and working principle employed in industrial spin-transfer torque magnetic random-access memories (STT-MRAM), constitute an important step towards the development of large-scale neuromorphic networks based on well-established technology.