Grollier Julie, Querlioz Damien, Stiles Mark D
Unité Mixte de Physique CNRS, Thales, Univ. Paris-Sud, Université Paris-Saclay, 91767 Palaiseau, France.
Centre de Nanosciences et de Nanotechnologies, CNRS, Université Paris-Saclay, 91405 Orsay, France.
Proc IEEE Inst Electr Electron Eng. 2016 Oct;104(10):2024-2039. doi: 10.1109/JPROC.2016.2597152. Epub 2016 Sep 8.
Bioinspired hardware holds the promise of low-energy, intelligent, and highly adaptable computing systems. Applications span from automatic classification for big data management, through unmanned vehicle control, to control for biomedical prosthesis. However, one of the major challenges of fabricating bioinspired hardware is building ultra-high-density networks out of complex processing units interlinked by tunable connections. Nanometer-scale devices exploiting spin electronics (or spintronics) can be a key technology in this context. In particular, magnetic tunnel junctions (MTJs) are well suited for this purpose because of their multiple tunable functionalities. One such functionality, non-volatile memory, can provide massive embedded memory in unconventional circuits, thus escaping the von-Neumann bottleneck arising when memory and processors are located separately. Other features of spintronic devices that could be beneficial for bioinspired computing include tunable fast nonlinear dynamics, controlled stochasticity, and the ability of single devices to change functions in different operating conditions. Large networks of interacting spintronic nanodevices can have their interactions tuned to induce complex dynamics such as synchronization, chaos, soliton diffusion, phase transitions, criticality, and convergence to multiple metastable states. A number of groups have recently proposed bioinspired architectures that include one or several types of spintronic nanodevices. In this paper, we show how spintronics can be used for bioinspired computing. We review the different approaches that have been proposed, the recent advances in this direction, and the challenges toward fully integrated spintronics complementary metal-oxide-semiconductor (CMOS) bioinspired hardware.
受生物启发的硬件有望实现低能耗、智能且高度自适应的计算系统。其应用范围广泛,涵盖大数据管理的自动分类、无人驾驶车辆控制以及生物医学假肢控制等领域。然而,制造受生物启发的硬件面临的主要挑战之一是,如何利用可调节连接将复杂的处理单元构建成超高密度网络。在这种情况下,利用自旋电子学(或自旋电子技术)的纳米级器件可能成为一项关键技术。特别是,磁性隧道结(MTJ)因其具有多种可调节功能而非常适合这一目的。其中一种功能,即非易失性存储器,可以在非常规电路中提供大容量嵌入式存储器,从而避免了因存储器和处理器分离而产生的冯·诺依曼瓶颈。自旋电子器件的其他特性,如可调节的快速非线性动力学、可控的随机性以及单个器件在不同工作条件下改变功能的能力,也可能对受生物启发的计算有益。相互作用的自旋电子纳米器件的大型网络可以通过调节其相互作用来诱导复杂的动力学,如同步、混沌、孤子扩散、相变、临界性以及收敛到多个亚稳态。最近有一些研究小组提出了包含一种或几种自旋电子纳米器件的受生物启发的架构。在本文中,我们展示了自旋电子学如何用于受生物启发的计算。我们回顾了已提出的不同方法、这一方向的最新进展以及实现完全集成的自旋电子学互补金属氧化物半导体(CMOS)受生物启发硬件所面临的挑战。