油藏计算
计算机科学
神经形态工程学
任务(项目管理)
利用
物理系统
分布式计算
超参数
集合(抽象数据类型)
多样性(控制论)
计算科学
计算机工程
人工智能
人工神经网络
物理
循环神经网络
程序设计语言
管理
经济
计算机安全
量子力学
作者
Oscar Lee,Tianyi Wei,Kilian D. Stenning,Jack C. Gartside,Dan Prestwood,Shu Seki,Aisha Aqeel,Kosuke Karube,Naoya Kanazawa,Y. Taguchi,Christian Back,Y. Tokura,W. R. Branford,Hidekazu Kurebayashi
标识
DOI:10.1038/s41563-023-01698-8
摘要
Abstract Reservoir computing is a neuromorphic architecture that may offer viable solutions to the growing energy costs of machine learning. In software-based machine learning, computing performance can be readily reconfigured to suit different computational tasks by tuning hyperparameters. This critical functionality is missing in ‘physical’ reservoir computing schemes that exploit nonlinear and history-dependent responses of physical systems for data processing. Here we overcome this issue with a ‘task-adaptive’ approach to physical reservoir computing. By leveraging a thermodynamical phase space to reconfigure key reservoir properties, we optimize computational performance across a diverse task set. We use the spin-wave spectra of the chiral magnet Cu 2 OSeO 3 that hosts skyrmion, conical and helical magnetic phases, providing on-demand access to different computational reservoir responses. The task-adaptive approach is applicable to a wide variety of physical systems, which we show in other chiral magnets via above (and near) room-temperature demonstrations in Co 8.5 Zn 8.5 Mn 3 (and FeGe).
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