Verma Ravi Shankar, Raj Ravish Kumar, Verma Gaurav, Kaushik Brajesh Kumar
Department of Electronics and Communication Engineering, Indian Institute of Technology, Roorkee 247667, India.
Nanotechnology. 2024 Aug 12;35(43). doi: 10.1088/1361-6528/ad6997.
Magnetic skyrmions offer unique characteristics such as nanoscale size, particle-like behavior, topological stability, and low depinning current density. These properties make them promising candidates for next-generation spintronics-based memory and neuromorphic computing. However, one of their distinctive features is their tendency to deviate from the direction of the applied driving force that may lead to the skyrmion annihilation at the edge of nanotrack during skyrmion motion, known as the skyrmion Hall effect (SkHE). To overcome this problem, synthetic antiferromagnetic (SAF) skyrmions that having bilayer coupling effect allows them to follow a straight path by nullifying SkHE making them alternative for ferromagnetic (FM) counterpart. This study proposes an integrate-and-fire (IF) artificial neuron model based on SAF skyrmions with asymmetric wedge-shaped nanotrack having self-sustainability of skyrmion numbers at the device window. The model leverages inter-skyrmion repulsion to replicate the IF mechanism of biological neuron. The device threshold, determined by the maximum number of pinned skyrmions at the device window, can be adjusted by tuning the current density applied to the nanotrack. Neuronal spikes occur when initial skyrmion reaches the detection unit after surpassing the device window by the accumulation of repulsive force that result in reduction of the device's contriving current results to design of high energy efficient for neuromorphic computing. Furthermore, work implements a binarized neuronal network accelerator using proposed IF neuron and SAF-SOT-MRAM based synaptic devices for national institute of standards and technology database image classification. The presented approach achieves significantly higher energy efficiency compared to existing technologies like SRAM and STT-MRAM, with improvements of 2.31x and 1.36x, respectively. The presented accelerator achieves 1.42x and 1.07x higher throughput efficiency per Watt as compared to conventional SRAM and STT-MRAM based designs.
磁斯格明子具有独特的特性，如纳米级尺寸、类粒子行为、拓扑稳定性和低脱钉电流密度。这些特性使其成为下一代基于自旋电子学的存储器和神经形态计算的有前途的候选者。然而，它们的一个显著特征是倾向于偏离所施加驱动力的方向，这可能导致斯格明子在纳米轨道边缘运动时湮灭，即所谓的斯格明子霍尔效应（SkHE）。为了克服这个问题，具有双层耦合效应的合成反铁磁（SAF）斯格明子能够通过抵消SkHE来使其沿直线路径运动，从而成为铁磁（FM）斯格明子的替代方案。本研究提出了一种基于SAF斯格明子的积分发放（IF）人工神经元模型，该模型具有不对称楔形纳米轨道，在器件窗口处具有斯格明子数量的自我维持能力。该模型利用斯格明子间的排斥力来复制生物神经元的IF机制。由器件窗口处钉扎斯格明子的最大数量决定的器件阈值，可以通过调整施加到纳米轨道的电流密度来调节。当初始斯格明子通过排斥力的积累超过器件窗口到达检测单元时，就会发生神经元尖峰，这会导致器件驱动电流减小，从而实现神经形态计算的高能效设计。此外，该工作使用所提出的IF神经元和基于SAF-SOT-MRAM的突触器件实现了一个二值化神经网络加速器，用于美国国家标准与技术研究院数据库图像分类。与现有技术如SRAM和STT-MRAM相比，所提出的方法实现了显著更高的能量效率，分别提高了2.31倍和1.36倍。与基于传统SRAM和STT-MRAM的设计相比，所提出的加速器每瓦实现了1.42倍和1.07倍更高的吞吐效率。
