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.
翻译:储油层计算(RC)最近引起了人们的兴趣,因为储油层重量不需要经过培训,使得极低的资源消耗执行量极低,这可能会对边缘计算和资源严重受限的现场学习产生变革性影响。理想的情况是,天然硬件储油层应该是被动的、最低限度的、直观的和可行的;迄今为止,拟议的硬件储油层很难满足所有这些标准。因此,我们提议建立一个储油层,利用浮油和破碎的纳米磁网的被动相互作用,满足所有这些标准。这种挫折大大增加了稳定的储油层国家的数量,丰富储油层的动态,因此这些破灭的纳米磁网可以满足天然硬件储油层的所有标准。我们同样建议建立一个完全受挫的纳米岩层储油层计算系统,其低功率的金属氧化半导电路(CMOS)系统与储油层连接起来,初步实验结果显示储油层的可行性。水库经过微磁模拟,显示有三种不同的任务。提议的系统与CMOS回流网络(ES-NNN)相比,因此满足了所有天然的低能潜能,因为资源在10000年是最低的。