Temporal pattern recognition with delayed feedback spin-torque nano-oscillators

TL;DR

This paper enhances the memory capacity of spin-torque nano-oscillators using time-delayed feedback, improving their efficiency in pattern recognition tasks for neuromorphic computing.

Contribution

It introduces a method to extend the memory of spin-torque nano-oscillators via time-delayed feedback, optimizing their performance for pattern recognition.

Findings

Extended oscillator memory improves pattern recognition efficiency.

Tunable feedback allows optimization under various conditions.

Enhanced neuromorphic computing capabilities demonstrated.

Abstract

The recent demonstration of neuromorphic computing with spin-torque nano-oscillators has opened a path to energy efficient data processing. The success of this demonstration hinged on the intrinsic short-term memory of the oscillators. In this study, we extend the memory of the spin-torque nano-oscillators through time-delayed feedback. We leverage this extrinsic memory to increase the efficiency of solving pattern recognition tasks that require memory to discriminate different inputs. The large tunability of these non-linear oscillators allows us to control and optimize the delayed feedback memory using different operating conditions of applied current and magnetic field.