MARCH: Magnetic Architectures for Reservoir Computing Hardware

传统的数字计算是耗电的，脆弱的，很难与模拟现实世界接口。非常规计算机，如在材料库计算机(RCS)，可以帮助克服这些问题，特别是通过能够执行体现计算，可以直接利用其材料组成的自然动力学，从而显著降低功率需求。这样的设备在原则上已经得到了证明，但系统需要扩大规模，才能为现实世界的任务提供足够和适当的计算能力。此外，需要一系列设备配置来支持不同的计算任务。我们的目标是将多种材料RCS：1.与相似的实例配置相结合，以提供可伸缩性2。使用不同的实例配置，共同执行单个RC配置无法单独执行的任务。磁性材料为开发材料RC设计流程提供了极好的柔性试验台。这种材料具有RC操作所需的固有存储器和复杂的非线性动力学，并且还具有用于建立实际设备所需的数据输入/输出的良好接口方法。我们将利用磁性纳米线的图案化2D布局的特性，这种特性可以很容易地用现有技术制造出来。这个灵活的设计和实验平台将使我们能够开发适用于一系列智能材料RC的通用技术。我们将开发新的多RC设计工具，并将设计、测试和制造纳米磁性RC。该工具包和制造将提供一个强大而可靠的测试平台，我们将在此平台上开发和评估可配置用于一系列实际计算任务的可扩展RC架构。我们将在音频控制机器人中演示由此产生的多水库体系结构：一只遵循简单徽标般的命令的“乌龟”，完全由材料水库控制，没有机载数字计算机。其输出将是一种新的多水库设备设计方法和平台，可用于设计各种材料的低功耗、坚固、灵活和高效的智能传感和其他“边缘计算”设备。

Classical digital computing is power hungry, fragile, and hard to interface to the analogue real world.Unconventional computers such as in materio Reservoir Computers (RCs) can help overcome these issues, particularly by being able to perform embodied computation that can directly exploit the natural dynamics of their material composition, thereby dramatically reducing the power requirements. Such devices have been proven in principle, but systems need to be scaled up to provide sufficient and appropriate computational power for real world tasks. Furthermore, a range of device configurations are needed to support different computational tasks.Our aim is to combine multiple material RCs:1. with similar instance configurations, to provide scalability2. with diverse instance configurations, to perform together tasks that no single RC configuration can readily perform alone. Magnetic materials provide an excellent flexible testbed for developing a material RC design process. Such materials have the intrinsic memory and complex non-linear dynamics needed for RC operation,and also have well-established methods of interfacing for data input/output, needed to build a practical device. We will exploit the properties of patterned 2D layouts of magnetic nanoring wires, which can be readily manufactured with existing technologies. This flexible design and experimental platform will allow us to develop generic techniques that will apply across a range of smart material RCs.We will develop new multi-RC design tools, and will design, test and manufacture nanomagnetic RCs. This toolset and manufacture will provide a robust and reliable testbed on which we will develop and evaluate scalable RC architectures that can be configured for a range of practical computing tasks. We will demonstrate the resulting multi-reservoir architecture in an audio-controlled robot: a `turtle' following simple LOGO-like commands, controlled purely by in materio reservoirs, with no onboard digital computers.The output will be a new design methodology and platform for multi-reservoir devices, that can be exploited to design low-power, robust, flexible, and efficient smart sensing and other `edge-computing' devices in a diverse range of materials.