From Stochasticity to Functionality: Probabilistic Computation with Magnetic Nanowires

机器学习，或计算机智能分析复杂数据集的能力，可能是我们这个时代的决定性技术。社会在经济、天气、医学、科学和社交媒体等不同领域产生了大量数据，对这些数据的分析可以大大提高我们的决策能力。然而，目前的计算机不适合分析大型和复杂的数据集，特别是与动物和人类大脑相比。例如，虽然一台典型的现代计算机可以比大脑更快地执行简单的数值计算，但在执行更复杂的数据分析（如识别人脸）时，其效率几乎要低100万倍。这种低效率是由于试图通过在不适合该任务的硬件上使用蛮力来模拟“神经形态”或类似大脑的计算过程。例如，在传统的计算机中，记忆和处理本质上是分离的，而它们在大脑中共享相同的介质。为了克服这些限制，需要专门为神经形态计算设计的定制计算机。除了纯粹的计算能力，“大数据”革命已经被我们存储信息的能力所驱动。这是以硬盘驱动器形式出现的纳米级磁性技术的结果。然而，纳米磁性器件受到一个不断侵犯的限制，因为它们的行为变得越来越不可靠或随机，因为它们被进一步放大以提高性能。虽然这对传统的数字计算机来说是毁灭性的，但有强有力的证据表明，随机行为在神经形态技术中是可以容忍的，甚至可以增强其性能。这提出了纳米磁技术的诱人前景，非常适合开发新的计算机形式的计算机硬件进行数据分析。在这个项目中，纳米磁技术和计算机科学专家在谢菲尔德大学的合作，我们将进行一项试验性研究，以调查“磁畴壁器件”中的随机行为是否磁纳米技术的一种有前途的形式，其中磁信息通过纳米线导管流动，可以用于实现新的神经形态计算机架构。我们将与机器学习技术的学术和行业专家组成的咨询委员会合作，展示新的数据分析设备，然后创建路线图，将设备开发成真实的技术。开发这些技术的最终成功可能会产生强大的硬件平台，即使是最小的设备也能智能分析其环境，从而做出明智的决策。

Machine learning, or the ability of computers to intelligently analyse complex data sets, may be the defining technology of our age. Society produces huge amounts of data in areas as diverse as economics, weather, medicine, science and social media, analysis of which can greatly enhance our decision making. However, current computers are poorly suited to analysing large and complex datasets, particularly when compared to animal and human brains. For example, while a typical contemporary computer can perform simple numerical calculations much more quickly than the brain, its efficiency is almost one million times lower when performing more complex data analysis such as recognising human faces. This inefficiency results from attempts to emulate "neuromorphic", or brain-like, computational processes by brute force on hardware which is ill-suited to the task. For example, in a conventional computer memory and processing are inherently separated, whereas they share the same medium in the brain. To overcome these limitations, bespoke computers that are specifically designed for neuromorphic computation are required.Away from pure computational power, the "big data" revolution has been driven by our ability to store information. This is the result of nano-scale magnetic technology in the form of hard-disk drives. However, nanomagnetic devices suffer from an encroaching limitation, in that their behaviour becomes increasingly unreliable, or stochastic, as they are further miniaturised to increase performance. While this is devastating for conventional digital computers, there is strong evidence that stochastic behaviour can be tolerated in, or even enhance, the performance of neuromorphic technologies. This raises the tantalising prospect of nanomagnetic technology being ideally suited for developing new computer forms of computer hardware for data analysis.In this project, a collaboration between experts in nanomagnetic technology and computer science at the University of Sheffield, we will perform a pilot study to investigate whether stochastic behaviour in "magnetic domain wall devices" a promising form of magnetic nanotechnology where magnetic information is flowed through nanowire conduits, can be used to realise new, neuromorphic computer architectures. Working with an advisory board of academic and industry experts in machine learning technology we will demonstrate new data analysis devices, before creating a roadmap to develop the devices into real technology. Eventual success in developing these technologies could result in powerful hardware platforms that can provide even the smallest device the ability to intelligently analyse its environment to make informed decisions.