Year: 2023
Type: preprint
Abstract: Topological textures in magnetic and electric materials are considered to be promising candidates for next-generation information technology and unconventional computing. Here, we discuss how the physical properties of topological nanoscale systems, such as skyrmions and domain walls, can be leveraged for reservoir computing, translating non-linear problems into linearly solvable ones. In addition to the necessary requirements of physical reservoirs, the topological textures give new opportunities for the downscaling of devices, enhanced complexity, and versatile input and readout options. Our perspective article presents topological magnetic and electric defects as an intriguing platform for non-linear signal conversion, giving a new dimension to reservoir computing and in-materio computing in general.
Source: arXiv (Cornell University)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4388926430