Noise resilient leaky integrate-and-fire neurons based on multi-domain spintronic devices.
Noise resilient leaky integrate-and-fire neurons based on multi-domain spintronic devices.
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DOI：
10.1038/s41598-022-12555-0
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发表时间：
2022-05-19
影响因子：
4.6
通讯作者：
Roy, Kaushik
中科院分区：
文献类型：
--
作者：
Wang, Cheng;Lee, Chankyu;Roy, Kaushik
在硬件中有效仿真神经功能的能力对于构建神经形态计算系统至关重要。虽然已经研究了各种类型的神经模拟装置，但是提供可以模拟尖峰神经元的紧凑装置仍然具有挑战性。在这项工作中，我们提出了一种非易失性自旋为基础的设备，有效地模拟泄漏的积分和消防神经元。通过在自旋轨道转矩磁性隧道结中引入交换耦合复合自由层，利用多畴磁化切换动力学来实现具有紧凑足迹的泄漏积分和激发神经元的膜电位的逐渐积累。与先前提出的基于自旋的神经模拟实现相比，所提出的设备提供了显着改善的可扩展性，同时表现出高能效和良好的可控性。此外，所提出的神经元器件表现出变化的泄漏常数和变化的膜电阻，这两者都取决于膜电位的大小。有趣的是，我们证明了这种设备启发的动态行为可以用来构建更强大的尖峰神经网络模型，并找到针对各种类型的噪声注入场景的改进弹性。拟议的自旋电子神经模拟设备可能会为开发高效且强大的神经启发计算硬件开辟令人兴奋的机会。
The capability of emulating neural functionalities efficiently in hardware is crucial for building neuromorphic computing systems. While various types of neuro-mimetic devices have been investigated, it remains challenging to provide a compact device that can emulate spiking neurons. In this work, we propose a non-volatile spin-based device for efficiently emulating a leaky integrate-and-fire neuron. By incorporating an exchange-coupled composite free layer in spin-orbit torque magnetic tunnel junctions, multi-domain magnetization switching dynamics is exploited to realize gradual accumulation of membrane potential for a leaky integrate-and-fire neuron with compact footprints. The proposed device offers significantly improved scalability compared with previously proposed spin-based neuro-mimetic implementations while exhibiting high energy efficiency and good controllability. Moreover, the proposed neuron device exhibits a varying leak constant and a varying membrane resistance that are both dependent on the magnitude of the membrane potential. Interestingly, we demonstrate that such device-inspired dynamic behaviors can be incorporated to construct more robust spiking neural network models, and find improved resiliency against various types of noise injection scenarios. The proposed spintronic neuro-mimetic devices may potentially open up exciting opportunities for the development of efficient and robust neuro-inspired computational hardware.
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