Energy-efficient synthetic antiferromagnetic skyrmion-based artificial neuronal device

被引:3
作者
Verma, Ravi Shankar [1 ]
Raj, Ravish Kumar [1 ]
Verma, Gaurav [1 ]
Kaushik, Brajesh Kumar [1 ]
机构
[1] Indian Inst Technol, Dept Elect & Commun Engn, Roorkee 247667, India
关键词
skyrmion; ferromagnetic; synthetic antiferromagnetic; neuron; neuromorphic computing; image classification; CURRENT-INDUCED MOTION; COMPUTING SYSTEM; NUCLEATION; LATTICE;
D O I
10.1088/1361-6528/ad6997
中图分类号
TB3 [工程材料学];
学科分类号
0805 ; 080502 ;
摘要
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.
引用
收藏
页数:13
相关论文
共 51 条
  • [1] Bilayer skyrmion dynamics on a magnetic anisotropy gradient
    Ang, Calvin Ching Ian
    Gan, Weiliang
    Lew, Wen Siang
    [J]. NEW JOURNAL OF PHYSICS, 2019, 21 (04)
  • [2] Spintronic devices: a promising alternative to CMOS devices
    Barla, Prashanth
    Joshi, Vinod Kumar
    Bhat, Somashekara
    [J]. JOURNAL OF COMPUTATIONAL ELECTRONICS, 2021, 20 (02) : 805 - 837
  • [3] Chen X, 2018, NANOSCALE, V10, P6139, DOI [10.1039/c7nr09722k, 10.1039/C7NR09722K]
  • [4] Formation and current-induced motion of synthetic antiferromagnetic skyrmion bubbles
    Dohi, Takaaki
    DuttaGupta, Samik
    Fukami, Shunsuke
    Ohno, Hideo
    [J]. NATURE COMMUNICATIONS, 2019, 10 (1)
  • [5] Neuromorphic extreme learning machines with bimodal memristive synapses
    Dong, Zhekang
    Lai, Chun Sing
    Zhang, Zhaowei
    Qi, Donglian
    Gao, Mingyu
    Duan, Shukai
    [J]. NEUROCOMPUTING, 2021, 453 : 38 - 49
  • [6] Synthetic antiferromagnetic spintronics
    Duine, R. A.
    Lee, Kyung-Jin
    Parkin, Stuart S. P.
    Stiles, M. D.
    [J]. NATURE PHYSICS, 2018, 14 (03) : 217 - 219
  • [7] A Low-Power High-Speed Spintronics-Based Neuromorphic Computing System Using Real-Time Tracking Method
    Farkhani, Hooman
    Tohidi, Mohammad
    Farkhani, Sadaf
    Madsen, Jens Kargaard
    Moradi, Farshad
    [J]. IEEE JOURNAL ON EMERGING AND SELECTED TOPICS IN CIRCUITS AND SYSTEMS, 2018, 8 (03) : 627 - 638
  • [8] Gebregiorgis A., 2023, Memories-Materials, Devices, Circuits and Systems, V4
  • [9] Neuromorphic spintronics
    Grollier, J.
    Querlioz, D.
    Camsari, K. Y.
    Everschor-Sitte, K.
    Fukami, S.
    Stiles, M. D.
    [J]. NATURE ELECTRONICS, 2020, 3 (07) : 360 - 370
  • [10] Low Power, CMOS-MoS2 Memtransistor based Neuromorphic Hybrid Architecture for Wake-Up Systems
    Gupta, Sarthak
    Kumar, Pratik
    Paul, Tathagata
    van Schaik, Andre
    Ghosh, Arindam
    Thakur, Chetan Singh
    [J]. SCIENTIFIC REPORTS, 2019, 9 (1)