Associative memory by virtual oscillator network based on single spin-torque oscillator

TL;DR

This paper proposes a novel virtual oscillator network based on a single spin-torque oscillator for energy-efficient associative memory, overcoming fabrication inhomogeneity issues through synchronization driven by feedforward input.

Contribution

It introduces a new approach combining coupled oscillators with neural network concepts using a single spin-torque oscillator for associative memory.

Findings

Successfully associated 60-pixel patterns with various memorized patterns.

Demonstrated that associative memory arises from forced synchronization via feedforward input.

Showed phase differences correspond to pattern pixel colors.

Abstract

A coupled oscillator network may be able to perform an energy-efficient associative memory operation. However, its realization has been difficult because inhomogeneities unavoidably arise among the oscillators during fabrication and lead to an unreliable operation. This issue could be resolved if the oscillator network were able to be formed from a single oscillator. Here, we performed numerical simulations and theoretical analyses on an associative memory operation that uses a virtual oscillator network based on a spin-torque oscillator. The virtual network combines the concept of coupled oscillators with that of feedforward neural networks. Numerical experiments demonstrate successful associations of $60$-pixel patterns with various memorized patterns. Moreover, the origin of the associative memory is shown to be forced synchronization driven by feedforward input, where phase differences among oscillators are fixed and correspond to the colors of the pixels in the pattern.