Multi-value Probabilistic Computing with current-controlled Skyrmion Diffusion

TL;DR

This paper demonstrates multi-value probabilistic computing using thermally diffusing magnetic skyrmions, enabling scalable, invertible logic and core AI functions with electrical control and measurement.

Contribution

It introduces a novel approach to multi-value probabilistic computing by leveraging skyrmion diffusion in a non-flat energy landscape, enabling scalable and invertible logic operations.

Findings

Successfully realized multi-value probabilistic computing with skyrmion diffusion.

Demonstrated softmax computation using current-controlled skyrmion distributions.

Achieved invertible logic without complex probabilistic device networks.

Abstract

Magnetic systems are highly promising for implementing probabilistic computing paradigms because of the fitting energy scales and conspicuous non-linearities. While conventional binary probabilistic computing has been realized, implementing more advantageous multi-value probabilistic computing (MPC) remains a challenge. Here, we report the realization of MPC by leveraging the thermally activated diffusion of magnetic skyrmions through an effectively non-flat energy landscape defined by a discrete number of pinning sites. The time-averaged spatial distribution of the diffusing skyrmions directly realizes a discrete probability distribution, which is tunable by current-generated spin-orbit torques, and can be quantified by non-perturbative electrical measurements. Even a very straightforward implementation with global tuning, already allows us to demonstrate the softmax computation - a core function in artificial intelligence. As a key advance, we demonstrate invertible logic without the need to create a network of probabilistic devices, offering major scalability advantages. Our proof of concept can be generalized to multiple skyrmions and can accommodate multiple locally tunable inputs and outputs using magnetic tunnel junctions, potentially enabling the representation of highly complex distribution functions.