Magnetic Pattern Recognition Using Injection-Locked Spin-Torque Nano-Oscillators

Yogendra, Karthik; Fan, Deliang; Jung, Byunghoo; Roy, Kaushik

doi:10.1109/ted.2016.2523423

Cited by 26 publications

(23 citation statements)

References 25 publications

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“…Spin transfer torque nano-oscillators (STNOs) are one of the most promising candidates addressing these requirements (23,24). Free running STNOs can interact with other STNOs to synchronize via coupling mechanisms that can be electrical (25,26), exchange based (27), dipolar (28), or due to spinwave propagation (29)(30)(31)(32)(33)(34), and this way deliver higher power and more coherent microwave signals.…”

mentioning

confidence: 99%

Two-dimensional mutually synchronized spin Hall nano-oscillator arrays for neuromorphic computing

et al. 2019

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Spin Hall nano-oscillators (SHNOs) utilize pure spin currents to drive local regions of magnetic films and nanostructures into auto-oscillating precession. If such regions are placed in close proximity to each other they can interact and sometimes mutually synchronize, in pairs or in short linear chains. Here we demonstrate robust mutual synchronization of two-dimensional SHNO arrays ranging from 2 x 2 to 8 x 8 nano-constrictions, observed both electrically and using micro-Brillouin Light Scattering microscopy. The signal quality factor, Q = f /∆f , increases linearly with number of mutually synchronized nanoconstrictions (N ), reaching 170,000 in the largest arrays. While the microwave peak power first increases as N 2 , it eventually levels off, indicating a non-zero relative phase shift between nano-constrictions. Our demonstration will enable the use of SHNO arrays in two-dimensional oscillator networks for highquality microwave signal generation and neuromorphic computing. arXiv:1812.09630v1 [cond-mat.mes-hall]

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confidence: 99%

Two-dimensional mutually synchronized spin Hall nano-oscillator arrays for neuromorphic computing

et al. 2019

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“…Figure 1B shows the magnetization direction of the free layer (m) and different torques acting on it (Yogendra et al, 2015). T P describes the precession torque that leads to the oscillation of m. T D is the damping torque that aligns m with H eff and T STT is the spintransfer torque caused by a bias current (Yogendra et al, 2016). The interaction of T STT and T D determines the oscillatory orbit of m. As T STT increases, m will be placed in an orbit farther than H eff , which will lead to a lower frequency of oscillation of m as shown in Figure 1B (Csaba and Porod, 2013).…”

Section: Spin Torque Nano-oscillators Basicsmentioning

confidence: 99%

“…The spinbased devices integrated with electronics (i.e., spintronics) have opened a door for designers to implement low-power highdensity NCSs. In spintronic-based NCSs, magnetic switching in magnetic tunnel junction (MTJ) (Fong et al, 2016) or magnetic oscillation in spin-torque nano-oscillator (STNO) (Yogendra et al, 2015(Yogendra et al, , 2016Kurenkov et al, 2019) is used to mimic neuron firing. While using oscillation of magnetic moment decreases the power consumption by an order of magnitude compared with the magnetic moment switching [critical current: ∼10 6 Acm −2 (Costa et al, 2017) vs. ∼10 −7 Acm −2 (Fukami et al, 2016)], still there is a huge gap between spintronic-based NCSs and the brain in terms of power consumption and speed.…”

Section: Introductionmentioning

confidence: 99%

LAO-NCS: Laser Assisted Spin Torque Nano Oscillator-Based Neuromorphic Computing System

et al. 2020

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Dealing with big data, especially the videos and images, is the biggest challenge of existing Von-Neumann machines while the human brain, benefiting from its massive parallel structure, is capable of processing the images and videos in a fraction of second. The most promising solution, which has been recently researched widely, is brain-inspired computers, so-called neuromorphic computing systems (NCS). The NCS overcomes the limitation of the word-at-a-time thinking of conventional computers benefiting from massive parallelism for data processing, similar to the brain. Recently, spintronic-based NCSs have shown the potential of implementation of low-power highdensity NCSs, where neurons are implemented using magnetic tunnel junctions (MTJs) or spin torque nano-oscillators (STNOs) and memristors are used to mimic synaptic functionality. Although using STNOs as neuron requires lower energy in comparison to the MTJs, still there is a huge gap between the power consumption of spintronic-based NCSs and the brain due to high bias current needed for starting the oscillation with a detectable output power. In this manuscript, we propose a spintronic-based NCS (196 × 10) proof-of-concept where the power consumption of the NCS is reduced by assisting the STNO oscillation through a microwatt nanosecond laser pulse. The experimental results show the power consumption of the STNOs in the designed NCS is reduced by 55.3% by heating up the STNOs to 100 • C. Moreover, the average power consumption of spintronic layer (STNOs and memristor array) is decreased by 54.9% at 100 • C compared with room temperature. The total power consumption of the proposed laser assisted STNO-based NCS (LAO-NCS) at 100 • C is improved by 40% in comparison to a typical STNO-based NCS at room temperature. Finally, the energy consumption of the LAO-NCA at 100 • C is expected to reduce by 86% compared with a typical STNO-based NCS at the room temperature.

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“…Brain-inspired neuromorphic computing provides a strong platform to implement computationally intensive operations such as associative memory, recognition, and classification, in which traditional von-Neumann paradigms lack computational efficiency due to higher power consumption, big area, reduced accuracy, and poor parallelism [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. A variety of computing strategies have been demonstrated in this regard to implement such non-Boolean computing systems.…”

Section: Introductionmentioning

confidence: 99%

“…Coupled-oscillator neurons have become critical parts of ONNs with the possibility of performing low-power computations and offering interesting features like synchronization dynamics. To date, modern oscillatory neurons have been physically implemented using the emerging nano-scale technologies like spin-torque (ST), memristors, and metal-insulator-transitions (MIT) oxides because of their unique properties where even the most advanced CMOS nodes are left behind [1][2][3][4][5][6][7][8][9][10][11][12][13][14]22,23]. Both ST and MIT technologies show hysteretic behaviour, synchronization capabilities, and acceptable sensitivity to image contrast compared with the CMOS-based implementation.…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient Ferroelectric Field-Effect Transistor-Based Oscillators for Neuromorphic System Design

Eslahi

Hamilton

Khandelwal

2020

IEEE J. Explor. Solid-State Comput. Devices Circuits

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Neuromorphic or bioinspired computational platforms, as an alternative for von-Neumann structures, have benefitted from the excellent features of emerging technologies in order to emulate the behaviour of biological brain in an accurate and energy efficient way. Integrability with CMOS technology and low power consumption make Ferroelectric FET (FEFET) an attractive candidate to perform such paradigms, particularly for image processing. In this paper, we use FEFET device to make energy-efficient oscillatory neurons as main parts of neural networks for image processing applications, especially for edge-detection. Based on our simulation results, we estimated a significant energy efficiency compared to other technologies which shows roughly − × reduction, depended on the design.

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Magnetic Pattern Recognition Using Injection-Locked Spin-Torque Nano-Oscillators

Cited by 26 publications

References 25 publications

Two-dimensional mutually synchronized spin Hall nano-oscillator arrays for neuromorphic computing

Two-dimensional mutually synchronized spin Hall nano-oscillator arrays for neuromorphic computing

LAO-NCS: Laser Assisted Spin Torque Nano Oscillator-Based Neuromorphic Computing System

Energy-Efficient Ferroelectric Field-Effect Transistor-Based Oscillators for Neuromorphic System Design

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