基于磁畴壁的神经元自复位方案

IF 2 Q3 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE IEEE Journal on Exploratory Solid-State Computational Devices and Circuits Pub Date : 2022-12-08 DOI:10.1109/JXCDC.2022.3227774
Debasis Das;Xuanyao Fong
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引用次数: 1

摘要

自旋电子人工尖峰神经元由于其能够密切模拟生物LIF尖峰神经元的泄漏积分和激发(LIF)动力学而具有前景。然而,神经元在放电后需要重置。文献中提出的自旋电子神经元很少详细讨论重置过程。在本文中,我们讨论了在基于磁畴壁(DW)的自旋电子神经元中实现这种重置的各种方案,其中DW的位置表示膜电位。在所有研究的自旋电子神经元中,神经元进入不应期,当DW到达特定位置时重置。我们表明,神经元设备中的自复位操作消耗的能量可能从几个pJ到几个fJ不等,这突出了复位策略在提高自旋电子人工尖峰神经元能效方面的重要性。
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Self-Reset Schemes for Magnetic Domain Wall-Based Neuron
Spintronic artificial spiking neurons are promising due to their ability to closely mimic the leaky integrate-and-fire (LIF) dynamics of the biological LIF spiking neuron. However, the neuron needs to be reset after firing. Few of the spintronic neurons that have been proposed in the literature discuss the reset process in detail. In this article, we discuss the various schemes to achieve this reset in a magnetic domain wall (DW)-based spintronic neuron in which the position of the DW represents the membrane potential. In all the spintronic neurons studied, the neuron enters a refractory period and is reset when the DW reaches a particular position. We show that the self-reset operation in the neuron devices consumes energy that can vary from several pJ to a few fJ, which highlights the importance of the reset strategy in improving the energy efficiency of spintronic artificial spiking neurons.
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来源期刊
CiteScore
5.00
自引率
4.20%
发文量
11
审稿时长
13 weeks
期刊最新文献
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