任务自适应的物理储备池计算
Task-adaptive physical reservoir computing
AI总结：
提出一种任务自适应的物理储备池计算方法，通过调控材料的温度和磁场以改变其非线性、记忆容量等关键属性，克服了传统物理储备池难以适配不同任务的局限，并在多种磁相材料中验证了其优化计算性能和可迁移性的能力。
摘要：
储备池计算是一种神经形态架构，有望为机器学习日益增长的能耗提供可行的解决方案。在基于软件的机器学习中，可以通过改变超参数，轻松重新配置神经网络的属性和性能，以适应不同的计算任务。然而，利用物理系统的非线性和依赖历史的记忆响应来进行数据处理的“物理”储备池计算方案却缺失这一关键功能。在此，我们通过实验提出了一种物理储备池计算的“任务自适应”方法，能够重新配置储备池的关键属性（非线性、记忆容量和复杂度），以优化在广泛任务中的计算性能。作为模型案例，我们利用Cu$_2$OSeO$_3$的温度和磁场控制的自旋波响应，该材料具有斯格明子、圆锥和螺旋磁相，提供了对多种不同物理储备池响应的按需访问。我们量化了相位可调的储备池性能，表征了它们的属性，并讨论了物理储备池中这些属性之间的相关性。这种任务自适应方法克服了物理储备池的关键先验限制，为在各种物理储备池系统中应用热力学稳定和亚稳态相控制开辟了机会，正如我们使用Co$_{8.5}$Zn$_{8.5}$Mn$_{3}$（FeGe）在室温（近室温）下的演示所展示的其可转移性。
英文摘要：
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).
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