Maximized lateral inhibition in paired magnetic domain wall racetracks for neuromorphic computing

Cui, Can; Akinola, Otitoaleke G.; Hassan, Naimul; Bennett, Christopher H.; Marinella, Matthew; Friedman, Joseph S.; Incorvia, Jean Anne C.

doi:10.1088/1361-6528/ab86e8

Cited by 22 publications

(13 citation statements)

References 37 publications

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“…Many proposed approaches to these have been inhibited by the complex magnetization dynamics of domain walls, which ultimately lead to unreliable device operation 25 , 26 . Other studies have proposed non-volatile, DW-based neurons and synapses that could be integrated into CMOS devices to create hybrid neuromorphic computing platforms 27 – 29 . Applications in these areas may be more robust against stochasticity than those in conventional memory and logic, due to the intrinsic error tolerance of neuromorphic approaches to computation.…”

Section: Introductionmentioning

confidence: 99%

Neuromorphic computation with a single magnetic domain wall

Ababei

Ellis

Vidamour

et al. 2021

Sci Rep

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Machine learning techniques are commonly used to model complex relationships but implementations on digital hardware are relatively inefficient due to poor matching between conventional computer architectures and the structures of the algorithms they are required to simulate. Neuromorphic devices, and in particular reservoir computing architectures, utilize the inherent properties of physical systems to implement machine learning algorithms and so have the potential to be much more efficient. In this work, we demonstrate that the dynamics of individual domain walls in magnetic nanowires are suitable for implementing the reservoir computing paradigm in hardware. We modelled the dynamics of a domain wall placed between two anti-notches in a nickel nanowire using both a 1D collective coordinates model and micromagnetic simulations. When driven by an oscillating magnetic field, the domain exhibits non-linear dynamics within the potential well created by the anti-notches that are analogous to those of the Duffing oscillator. We exploit the domain wall dynamics for reservoir computing by modulating the amplitude of the applied magnetic field to inject time-multiplexed input signals into the reservoir, and show how this allows us to perform machine learning tasks including: the classification of (1) sine and square waves; (2) spoken digits; and (3) non-temporal 2D toy data and hand written digits. Our work lays the foundation for the creation of nanoscale neuromorphic devices in which individual magnetic domain walls are used to perform complex data analysis tasks.

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Section: Introductionmentioning

confidence: 99%

Neuromorphic computation with a single magnetic domain wall

Ababei

Ellis

Vidamour

et al. 2021

Sci Rep

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“…A serious research effort is currently underway to implement non-Boolean computing machinery with nanomagnets [38][39][40][41][42][43][44][45][46][47][48][49][50][51]. Their requirements are very different from those of Boolean logic and fortunately are well suited to the features of nanomagnetic switches, especially their attribute of non-volatility.…”

Section: Discussionmentioning

confidence: 99%

Nanomagnetic Boolean Logic—The Tempered (and Realistic) Vision

Bandyopadhyay

2021

IEEE Access

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The idea of nanomagnetic Boolean logic was advanced more than two decades ago. It envisaged the use of nanomagnets with two stable magnetization orientations as the primitive binary switch for implementing logic gates and ultimately combinational/sequential circuits. Enthusiastic proclamations of how nanomagnetic logic will eclipse traditional (transistor-based) logic circuits proliferated the applied physics literature. Two decades later there is not a single viable nanomagnetic logic chip in sight, let alone one that is a commercial success. In this perspective article, I offer my reasons on why this has come to pass. I present a realistic and tempered vision of nanomagnetic logic, pointing out many misconceptions about this paradigm, flaws in some proposals that appeared in the literature, shortcomings, and likely pitfalls that might stymie progress in this field.

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“…[ 458–460 ] Inhibition as an efficient mean for computing has been widely used in neural network algorithms [ 461–465 ] or implemented in neuromorphic electronic hardware. [ 466–481 ]…”

Section: Algorithmic Levelmentioning

confidence: 99%

A Marr's Three‐Level Analytical Framework for Neuromorphic Electronic Systems

Guo

Zou

et al. 2021

Advanced Intelligent Systems

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Neuromorphic electronics, an emerging field that aims for building electronic mimics of the biological brain, holds promise for reshaping the frontiers of information technology and enabling a more intelligent and efficient computing paradigm. As their biological brain counterpart, the neuromorphic electronic systems are complex, having multiple levels of organization. Inspired by David Marr's famous three‐level analytical framework developed for neuroscience, the advances in neuromorphic electronic systems are selectively surveyed and given significance to these research endeavors as appropriate from the computational level, algorithmic level, or implementation level. Under this framework, the problem of how to build a neuromorphic electronic system is defined in a tractable way. In conclusion, the development of neuromorphic electronic systems confronts a similar challenge to the one neuroscience confronts, that is, the limited constructability of the low‐level knowledge (implementations and algorithms) to achieve high‐level brain‐like (human‐level) computational functions. An opportunity arises from the communication among different levels and their codesign. Neuroscience lab‐on‐neuromorphic chip platforms offer additional opportunity for mutual benefit between the two disciplines.

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Maximized lateral inhibition in paired magnetic domain wall racetracks for neuromorphic computing

Cited by 22 publications

References 37 publications

Neuromorphic computation with a single magnetic domain wall

Neuromorphic computation with a single magnetic domain wall

Nanomagnetic Boolean Logic—The Tempered (and Realistic) Vision

A Marr's Three‐Level Analytical Framework for Neuromorphic Electronic Systems

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