无源阻挫纳米磁体储备池计算
Passive frustrated nanomagnet reservoir computing
AI总结：
针对现有硬件储备池难以同时满足无源、极简等要求的问题，提出基于偶极耦合阻挫纳米磁体的无源储备池计算系统，搭配低功耗CMOS接口电路，经微磁模拟验证性能，资源消耗较CMOS ESN降低超千万倍，为资源受限场景提供高效方案。
摘要：
储备池计算（Reservoir Computing, RC）近年来受到关注，因为其无需训练储备池权重，可实现资源消耗极低的部署，这对资源严重受限的边缘计算和原位学习领域可能产生变革性影响。理想的天然硬件储备池应具备无源、极简、表达性强且可实现的特性；截至目前，已提出的硬件储备池难以同时满足所有这些标准。因此，我们提出一种满足全部标准的储备池，其利用偶极耦合的阻挫纳米磁体的无源相互作用实现。阻挫效应大幅增加了储备池稳定状态的数量，丰富了储备池的动力学特性，因此这类阻挫纳米磁体符合天然硬件储备池的全部标准。我们还提出了一套完整的阻挫纳米磁体储备池计算（nanomagnet reservoir computing, NMRC）系统，采用低功耗互补金属氧化物半导体（complementary metal-oxide semiconductor, CMOS）电路与储备池连接，初步实验结果验证了该储备池的可行性。通过微磁模拟在三项独立任务上对储备池进行了验证，证明其具备良好的表达性。将所提出的系统与CMOS回声状态网络（echo-state-network, ESN）进行对比，结果显示整体资源消耗降低了超过10,000,000倍，表明由于NMRC天然具备无源和极简的特性，它具备实现极高资源效率的潜力。
英文摘要：
Reservoir computing (RC) has received recent interest because reservoir weights do not need to be trained, enabling extremely low-resource consumption implementations, which could have a transformative impact on edge computing and in-situ learning where resources are severely constrained. Ideally, a natural hardware reservoir should be passive, minimal, expressive, and feasible; to date, proposed hardware reservoirs have had difficulty meeting all of these criteria. We therefore propose a reservoir that meets all of these criteria by leveraging the passive interactions of dipole-coupled, frustrated nanomagnets. The frustration significantly increases the number of stable reservoir states, enriching reservoir dynamics, and as such these frustrated nanomagnets fulfill all of the criteria for a natural hardware reservoir. We likewise propose a complete frustrated nanomagnet reservoir computing (NMRC) system with low-power complementary metal-oxide semiconductor (CMOS) circuitry to interface with the reservoir, and initial experimental results demonstrate the reservoir's feasibility. The reservoir is verified with micromagnetic simulations on three separate tasks demonstrating expressivity. The proposed system is compared with a CMOS echo-state-network (ESN), demonstrating an overall resource decrease by a factor of over 10,000,000, demonstrating that because NMRC is naturally passive and minimal it has the potential to be extremely resource efficient.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
