论文标题
基于磁性域壁神经元的自我分析方案
Self-reset schemes for Magnetic domain wall-based neuron
论文作者
论文摘要
Spintronic人工尖峰神经元由于能够密切模仿生物LIF峰神经元的泄漏的整合和火力（LIF）动力学，因此很有希望。但是，射击后需要重置神经元。文献中很少有人提出的自旋神经元详细讨论复位过程。在本文中，我们讨论了在基于磁性域壁（DW）的旋转神经元中实现此重置的各种方案，其中DW的位置代表膜电位。在研究的所有自旋神经元中，神经元进入难治性周期，当DW达到特定位置时，则重置。我们表明，神经元设备中的自我分析操作消耗的能量可以从几个PJ到几个FJ，这突出了重置策略在提高自旋人工尖峰神经元能源效率方面的重要性。
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 of several pJ to a few fJ, which highlights the importance of the reset strategy in improving the energy efficiency of spintronic artificial spiking neurons.
