Spin waves to the rescue: Development of a spintronic reservoir computing platform

随着我们接近3 nm晶体管沟道长度的理论极限，CMOS架构的制造挑战变得更加困难和昂贵。与此同时，计算工作负载正在发生巨变，从离线处理转向由物联网(IoT)、机器人和自主代理驱动的实时大数据应用。这种因素的结合导致了对替代计算方法的深入探索，这些方法跨越了从物理效果到材料、设备、体系结构和数据表示的整个布尔计算堆栈。它还包括新的非布尔计算方法，如量子、波和神经形态计算、玻尔兹曼机器和其他方法。从这种过多的选择中究竟会演变出哪种计算元素的组合还远不清楚。然而，未来的计算平台必须满足的一般要求是可能的。首先，任何新的计算方法都必须与现有的数万亿英镑的基础设施兼容，这些基础设施与当前基于cmos的计算相关。其次，它必须通过多代增量硬件和软件改进实现可伸缩性。第三，性能/成本指标必须远远超过布尔CMOS处理器，第四，新技术必须提供一个比现有技术更节能的替代方案。水库计算(RC)利用模拟物理系统中的快速非线性动力学来将系统的自发瞬时响应映射到传统困难问题的解决方案，如分类任务和信号预测。这项技术有效地将记忆和处理任务与材料的内在属性联系在一起。然而，实现RC的物理系统的具体细节是不相关的，只要满足以下关键标准：动力学非线性、高相空间维度、唯一可重现的初始状态、容易失衡扰动和动态状态的可读性。我们的主要任务是确定一种适合这项任务的系统，它不会受到与现实世界不兼容的要求的困扰。我们提出的解决方案是基于驱动的自旋波激发，它既保证了足够复杂的瞬时响应、受控的混沌，又提供了一个自然的自旋电子平台，用于直接驱动和读取动态磁态。我们提出的工作旨在展示自旋波干涉作为在现实世界设备中实现RC的关键候选的多功能性。我们认为，磁性纳米结构中的自旋波是开发电路元件的插入式替代品以及独立器件的理想候选者。这一努力的成功将证明是开发实时模式检测技术的突破性进展，有可能在从医疗监测到气候建模等领域进行高影响力的部署。复杂的模式识别任务可以在面积为平方微米、功耗为100微瓦、推理时间为10 ns的RC硬件上完成。与行业领军企业(谷歌、苹果、Facebook等)目前使用的服务器堆栈相比为了满足全球需求，这一行动的成功将为更具资源效率的大规模大数据解决方案铺平道路。

As we approach the theoretical limit of 3 nm transistor channel lengths, manufacturing challenges of CMOS architectures become exponentially more difficult and more expensive to overcome. Simultaneously, a seismic shift is occurring in the computational workload, away from offline processing to real-time big-data applications driven by the Internet of Things (IoT), robotics and autonomous agents. This combination of factors has led to an intensified exploration of alternative computing methodologies that span the entire Boolean computational stack from physical effects, to materials, devices, architectures, and data representations. It also includes novel, non-Boolean methods of computing such as quantum, wave and neuromorphic computation, Boltzmann machines and others. Exactly which combination of computational elements will evolve from this plethora of options is far from clear. However, it is possible to state general requirements future computing platforms must meet. First, any new computing methodology must be compatible with the existing multi-trillion-pound infrastructure associated with current CMOS based computing. Second, it must be scalable through multiple generations of incremental hardware and software improvements. Third, the performance/cost metric must greatly exceed that of Boolean CMOS processors, and, fourth, the new technology must provide a much more energy-efficient alternative to existing technology.Reservoir Computing (RC) leverages fast nonlinear dynamics in analogue physical systems to map a system's spontaneous transient response to solutions of traditionally hard problems such as classification tasks and signal prediction. This technique effectively ties memory and processing tasks to the intrinsic materials properties. The specific details of the physical system in which RC is implemented, however, are not relevant so long the following key criteria are met: dynamical non-linearity, high phase space dimensionality, uniquely reproducible initial state, easy out-of-equilibrium perturbation, and readability of dynamical state. The main quest is to identify a system suitable for the task, which is not plagued by real world-incompatible requirements. Our proposed solution is based on driven spin-wave excitations which guarantee both sufficiently complex transient responses, controlled chaoticity, as well as providing a natural spintronic platform for straightforward driving and reading of dynamical magnetic states. Our proposed work aims at demonstrating the versatility of spin-wave interference as the key candidate for the implementation of RC in a real-world device. We believe that spin-waves in magnetic nanostructures are ideal candidates for developing drop-in substitutes for circuit components, as well as stand-alone devices. Success in this endeavour would prove groundbreaking for the development of real-time pattern detection technologies with the potential for high-impact deployment in areas ranging from medical monitoring to climate modelling. Complex pattern recognition tasks could be performed on RC hardware with square-micrometre surface area, 100 micro-W power consumption and 10 ns inference time. Compared to the server stacks currently used by industry leaders (Google, Apple, Facebook, etc.) to satisfy global demand, success in this action will pave the way for massively more resource efficient big-data solutions.