تقرير
Task-adaptive physical reservoir computing
| العنوان: | Task-adaptive physical reservoir computing |
|---|---|
| المؤلفون: | Lee, Oscar, Wei, Tianyi, Stenning, Kilian D., Gartside, Jack C., Prestwood, Dan, Seki, Shinichiro, Aqeel, Aisha, Karube, Kosuke, Kanazawa, Naoya, Taguchi, Yasujiro, Back, Christian, Tokura, Yoshinori, Branford, Will R., Kurebayashi, Hidekazu |
| سنة النشر: | 2022 |
| المجموعة: | Condensed Matter |
| مصطلحات موضوعية: | Condensed Matter - Materials Science, Condensed Matter - Mesoscale and Nanoscale Physics |
| الوصف: | Reservoir computing is a neuromorphic architecture that potentially offers viable solutions to the growing energy costs of machine learning. In software-based machine learning, neural network properties and performance can be readily reconfigured to suit different computational tasks by changing hyperparameters. This critical functionality is missing in ``physical" reservoir computing schemes that exploit nonlinear and history-dependent memory responses of physical systems for data processing. Here, we experimentally present a `task-adaptive' approach to physical reservoir computing, capable of reconfiguring key reservoir properties (nonlinearity, memory-capacity and complexity) to optimise computational performance across a broad range of tasks. As a model case of this, we use the temperature and magnetic-field controlled spin-wave response of Cu$_2$OSeO$_3$ that hosts skyrmion, conical and helical magnetic phases, providing on-demand access to a host of different physical reservoir responses. We quantify phase-tunable reservoir performance, characterise their properties and discuss the correlation between these in physical reservoirs. This task-adaptive approach overcomes key prior limitations of physical reservoirs, opening opportunities to apply thermodynamically stable and metastable phase control across a wide variety of physical reservoir systems, as we show its transferable nature using above(near)-room-temperature demonstration with Co$_{8.5}$Zn$_{8.5}$Mn$_{3}$ (FeGe). Comment: Main manuscript: 14 pages, 5 figures. Supplementary materials: 13 pages, 10 figures |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2209.06962 |
| رقم الأكسشن: | edsarx.2209.06962 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |