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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.
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This content will become publicly available on November 1, 2026
Memimpedance‐Based Biomolecular Device for Adaptive Physical Reservoir Computing
Artificial neural networks and recurrent neural networks are foundational to machine learning; although successful, their simplified components lack the intricate dynamics and high functional density of biological neural networks, limiting their efficiency. Physical reservoir computing (PRC) offers an alternative by leveraging the intrinsic dynamics of physical systems while restricting training to the readout layer. However, two significant shortcomings hinder the PRC's efficacy: the memory‐nonlinearity trade‐off, where increasing nonlinearity reduces memory capacity, and limited timescale adaptability, constraining it to contrived task‐device matching. This work presents a biomolecular PRC system that integrates heterogeneous nodes with complementary memimpedance properties using droplet interface bilayers. The system combines ion channel‐based memristive (nonlinear, fast‐switching) and biomembrane‐based memcapacitive (linear, slow‐switching) nodes within a single device, addressing the trade‐off and enabling multiscale temporal processing. Predicting a second‐order nonlinear system is demonstrated to address the trade‐off, achieving a normalized mean square error of 0.176 with the hybrid system outperforming memristive and memcapacitive reservoirs. Additionally, two real‐time classification tasks validate the system's ability to process signals across temporal scales, achieving 100% accuracy in both tasks. This approach expands PRC's applicability across diverse tasks without preoptimization, underscoring the potential of PRC and soft biomolecular materials for adaptive energy‐efficient computing.
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- PAR ID:
- 10672117
- Publisher / Repository:
- Wiley-VCH
- Date Published:
- Journal Name:
- Advanced Intelligent Systems
- Volume:
- 7
- Issue:
- 11
- ISSN:
- 2640-4567
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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