Stochastic magnetoelectric neuron for temporal information encoding

Yang, Kezhou; Sengupta, Abhronil

doi:10.1063/1.5138951

Cited by 8 publications

(2 citation statements)

References 39 publications

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“…The time-resolved measurements show very similar behavior for 10 devices with diameters ranging from 70 to 150 nm. The macrospin simulations shown in Figure b (parameters given in Supporting Information 1) and a previous study also demonstrate this behavior with applied voltage and rule out the hypothesis of a purely superparamagnetic stochastic switching response − to explain our device behavior.…”

supporting

confidence: 80%

Spiking Dynamics in Dual Free Layer Perpendicular Magnetic Tunnel Junctions

Farcis,

Teixeira,

Talatchian

et al. 2023

Nano Lett.

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Spintronic devices have recently attracted a lot of attention in the field of unconventional computing due to their non-volatility for short- and long-term memory, nonlinear fast response, and relatively small footprint. Here we demonstrate experimentally how voltage driven magnetization dynamics of dual free layer perpendicular magnetic tunnel junctions can emulate spiking neurons in hardware. The output spiking rate was controlled by varying the dc bias voltage across the device. The field-free operation of this two-terminal device and its robustness against an externally applied magnetic field make it a suitable candidate to mimic the neuron response in a dense neural network. The small energy consumption of the device (4–16 pJ/spike) and its scalability are important benefits for embedded applications. This compact perpendicular magnetic tunnel junction structure could finally bring spiking neural networks to sub-100 nm size elements.

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supporting

confidence: 80%

Spiking Dynamics in Dual Free Layer Perpendicular Magnetic Tunnel Junctions

Farcis,

Teixeira,

Talatchian

et al. 2023

Nano Lett.

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“…We also highlight that the work outperforms computationally expensive BPTT based fine-tuning approaches since temporal information may not be relevant in static image classification tasks. Future exploration into application drivers with temporal information (Mahapatra et al, 2020 ; Singh et al, 2021 ) or temporal spike encoding schemes (Petro et al, 2020 ; Yang and Sengupta, 2020 ) is expected to truly leverage the full potential of BPTT based SNN training strategies.…”

Section: Discussionmentioning

confidence: 99%

Neuroevolution Guided Hybrid Spiking Neural Network Training

Sengupta

2022

Front. Neurosci.

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Neuromorphic computing algorithms based on Spiking Neural Networks (SNNs) are evolving to be a disruptive technology driving machine learning research. The overarching goal of this work is to develop a structured algorithmic framework for SNN training that optimizes unique SNN-specific properties like neuron spiking threshold using neuroevolution as a feedback strategy. We provide extensive results for this hybrid bio-inspired training strategy and show that such a feedback-based learning approach leads to explainable neuromorphic systems that adapt to the specific underlying application. Our analysis reveals 53.8, 28.8, and 28.2% latency improvement for the neuroevolution-based SNN training strategy on CIFAR-10, CIFAR-100, and ImageNet datasets, respectively in contrast to state-of-the-art conversion based approaches. The proposed algorithm can be easily extended to other application domains like image classification in presence of adversarial attacks where 43.2 and 27.9% latency improvements were observed on CIFAR-10 and CIFAR-100 datasets, respectively.

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Exploiting random phenomena in magnetic materials for data security, logics, and neuromorphic computing: Challenges and prospects

Navau

Sort

2021

APL Materials

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Random phenomena are ubiquitous in magnetism. They include, for example: the random orientation of magnetization in an assembly of non-interacting isotropic magnets; arbitrary maze domain patterns in magnetic multilayers with out-of-plane anisotropy, random polarization, and chirality of an array of magnetic vortices; or Brownian skyrmion motion, among others. Usually, for memory applications, randomness needs to be avoided to reduce noise and enhance stability and endurance. However, these uncontrolled magnetic effects, especially when incorporated in magnetic random-access memories, offer a wide range of new opportunities in, e.g., stochastic computing, the generation of true random numbers, or physical unclonable functions for data security. Partial control of randomness leads to tunable probabilistic bits, which are of interest for neuromorphic computing and for new logic paradigms, as a first step toward quantum computing. In this Perspective, we present and analyze typical materials that exhibit stochastic magnetic phenomena and we show some examples of emerging applications. The current challenges in terms of material development, as well as new strategies to tune stochasticity, enhance energy efficiency, and improve operation speeds are discussed, aiming to provide new prospects and opportunities in this compelling research field.

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Stochastic magnetoelectric neuron for temporal information encoding

Cited by 8 publications

References 39 publications

Spiking Dynamics in Dual Free Layer Perpendicular Magnetic Tunnel Junctions

Spiking Dynamics in Dual Free Layer Perpendicular Magnetic Tunnel Junctions

Neuroevolution Guided Hybrid Spiking Neural Network Training

Exploiting random phenomena in magnetic materials for data security, logics, and neuromorphic computing: Challenges and prospects

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