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Stochastic Memristive Synapses from Spin-Transfer Torque Magnetic Tunnel Junctions

机译：自旋传递转矩磁隧道结的随机忆阻突触

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Recent progress lead spin-transfer torque magnetic tunnel junctions (STT-MTJs) to emerge as a breakthrough for embedded and standalone non-volatile memory. A peculiarity of this technology, however, is its stochastic switching nature. In memory applications, the randomness of the delay to program from a memory state to another requires designing programming times with high safety margins, to ensure reliable programming. However, considering this MTJs behavior as an intrinsic feature and not as a drawback can benefit unconventional applications. In particular, here we propose to use MTJs as “stochastic binary memristive synapses”. We show how MTJs may then be used in a brain-inspired (neuromorphic) system for practical applications, where MTJs implement at the same time massive nonvolatile memory and a stochastic learning rule. We perform simulations of our system using analytical equations to describe MTJs' probabilistic behavior, in different programming regimes. These simulations highlight the potential of the technology for learning systems that are also robust to device variations. We study the impact of the different programming regimes on the operation and on the energy consumption of the system. These results open the way for unexplored applications of MTJs in robust, low power, cognitive-type systems.
机译：最近的进展导致自旋转移扭矩磁性隧道结（STT-MTJ）成为嵌入式和独立非易失性存储器的突破。但是，这种技术的独特之处在于它的随机切换特性。在存储器应用中，从存储器状态到另一状态的编程延迟的随机性要求设计具有高安全裕度的编程时间，以确保可靠的编程。但是，将此MTJ的行为视为固有特征而不是缺点可以使非常规应用程序受益。特别是，在这里，我们建议使用MTJ作为“随机二进制忆阻突触”。我们展示了如何将MTJs应用于实际应用的灵感来自大脑的（神经形态）系统，其中MTJs同时实现大量的非易失性存储器和随机学习规则。我们使用解析方程对系统进行仿真，以描述不同编程方案下MTJ的概率行为。这些仿真凸显了该技术在学习系统中的潜力，该学习系统对于设备的变化也很稳定。我们研究了不同编程方案对系统的运行和能耗的影响。这些结果为未开发的MTJ在健壮，低功耗，认知型系统中的应用开辟了道路。

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