Skyrmion based energy-efficient straintronic physical reservoir computing. Issue 4 (1st December 2022)

• Record Type: Journal Article
• Authors: Rajib, Md Mahadi; Misba, Walid Al; Chowdhury, Md Fahim F; Alam, Muhammad Sabbir; Atulasimha, Jayasimha
• Is Part Of: Neuromorphic computing and engineering. Volume 2:Issue 4(2022)
• Publication Date: 2022-12-01
• Subjects: reservoir computing -- magnetic skyrmion -- STM -- PC -- energy-efficient -- nano-devices; Neural networks (Computer science) -- Periodicals; Neural computers -- Periodicals; Neuromorphics -- Periodicals; 006.3
• DOI: 10.1088/2634-4386/aca178
• Languages: English
• ISSNs: 2634-4386

Abstract

Abstract: Physical Reservoir Computing (PRC) is an unconventional computing paradigm that exploits the nonlinear dynamics of reservoir blocks to perform temporal data classification and prediction tasks. Here, we show with simulations that patterned thin films hosting skyrmion can implement energy-efficient straintronic reservoir computing (RC) in the presence of room-temperature thermal perturbation. This RC block is based on strain-induced nonlinear breathing dynamics of skyrmions, which are coupled to each other through dipole and spin-wave interaction. The nonlinear and coupled magnetization dynamics were exploited to perform temporal data classification and prediction. Two performance metrics, namely Short-Term Memory (STM) and Parity Check (PC) capacity are studied and shown to be promising (4.39 and 4.62 respectively), in addition to showing it can classify sine and square waves with 100% accuracy. These demonstrate the potential of such skyrmion based PRC. Furthermore, our study shows that nonlinear magnetization dynamics and interaction through spin-wave and dipole coupling have a strong influence on STM and PC capacity, thus explaining the role of physical interaction in a dynamical system on its ability to perform RC.