Image segmentation with stochastic magnetic tunnel junctions and spiking neurons

Liyanagedera, Chamika; Wijesinghe, Parami; Jaiswal, Akhilesh; Roy, Kaushik

doi:10.1109/ijcnn.2017.7966155

Cited by 4 publications

(3 citation statements)

References 24 publications

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“…This duration is called the refractory period, and the neuron stays idle within this period. Reprinted with the permission from Liyanagedera et al (2017). (B) The structure of the liquid state machine.…”

Section: Methodsmentioning

confidence: 99%

Analysis of Liquid Ensembles for Enhancing the Performance and Accuracy of Liquid State Machines

et al. 2019

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Liquid state machine (LSM), a bio-inspired computing model consisting of the input sparsely connected to a randomly interlinked reservoir (or liquid) of spiking neurons followed by a readout layer, finds utility in a range of applications varying from robot control and sequence generation to action, speech, and image recognition. LSMs stand out among other Recurrent Neural Network (RNN) architectures due to their simplistic structure and lower training complexity. Plethora of recent efforts have been focused toward mimicking certain characteristics of biological systems to enhance the performance of modern artificial neural networks. It has been shown that biological neurons are more likely to be connected to other neurons in the close proximity, and tend to be disconnected as the neurons are spatially far apart. Inspired by this, we propose a group of locally connected neuron reservoirs, or an ensemble of liquids approach, for LSMs. We analyze how the segmentation of a single large liquid to create an ensemble of multiple smaller liquids affects the latency and accuracy of an LSM. In our analysis, we quantify the ability of the proposed ensemble approach to provide an improved representation of the input using the Separation Property (SP) and Approximation Property (AP). Our results illustrate that the ensemble approach enhances class discrimination (quantified as the ratio between the SP and AP), leading to better accuracy in speech and image recognition tasks, when compared to a single large liquid. Furthermore, we obtain performance benefits in terms of improved inference time and reduced memory requirements, due to lowered number of connections and the freedom to parallelize the liquid evaluation process.

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Section: Methodsmentioning

confidence: 99%

Analysis of Liquid Ensembles for Enhancing the Performance and Accuracy of Liquid State Machines

et al. 2019

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“…Probabilistic behavior is becoming increasingly important as stochastic systems are being used for unconventional computing (Lv and Wang, 2017;Hayakawa et al, 2021). Stochastic MTJs have, for example, been proposed for integer factorization as an alternative to qubits (Borders et al, 2019) and image segmentation (Liyanagedera et al, 2017). Sourcing a write current will result in probabilistic switching of the free magnetization, where the probability is determined by the current magnitude (Srinivasan et al, 2016;Shim et al, 2017).…”

Section: Spintronic Memristormentioning

confidence: 99%

Anisotropy and Current Control of Magnetization in SrRuO3/SrTiO3 Heterostructures for Spin-Memristors

Goossens

Leiviskä

Banerjee

2021

Front. Nanotechnol.

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Spintronics-based nonvolatile components in neuromorphic circuits offer the possibility of realizing novel functionalities at low power. Current-controlled electrical switching of magnetization is actively researched in this context. Complex oxide heterostructures with perpendicular magnetic anisotropy (PMA), consisting of SrRuO3 (SRO) grown on SrTiO3 (STO) are strong material contenders. Utilizing the crystal orientation, magnetic anisotropy in such simple heterostructures can be tuned to either exhibit a perfect or slightly tilted PMA. Here, we investigate current induced magnetization modulation in such tailored ferromagnetic layers with a material with strong spin-orbit coupling (Pt), exploiting the spin Hall effect. We find significant differences in the magnetic anisotropy between the SRO/STO heterostructures, as manifested in the first and second harmonic magnetoresistance measurements. Current-induced magnetization switching can be realized with spin-orbit torques, but for systems with perfect PMA this switching is probabilistic as a result of the high symmetry. Slight tilting of the PMA can break this symmetry and allow the realization of deterministic switching. Control over the magnetic anisotropy of our heterostructures therefore provides control over the manner of switching. Based on our findings, we propose a three-terminal spintronic memristor, with a magnetic tunnel junction design, that shows several resistive states controlled by electric charge. Non-volatile states can be written through SOT by applying an in-plane current, and read out as a tunnel current by applying a small out-of-plane current. Depending on the anisotropy of the SRO layer, the writing mechanism is either deterministic or probabilistic allowing for different functionalities to emerge. We envisage that the probabilistic MTJs could be used as synapses while the deterministic devices can emulate neurons.

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“…Probabilistic behavior is becoming increasingly important as stochastic systems are being used for unconventional computing [49,50]. Stochastic MTJs have, for example, been proposed for integer factorization as an alternative to qubits ( [51]) and image segmentation [52]. Sourcing a write current will result in probabilistic switching of the free magnetization, where the probability is determined by the current magnitude [53,54].…”

Section: Spintronic Memristormentioning

confidence: 99%

Anisotropy and Current Control of Magnetization in SrRuO3 SrTiO3 Heterostructures for Spin-Memristors

Goossens¹,

Leiviskä²,

Banerjee³

2021

Preprint

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Spintronics-based nonvolatile components in neuromorphic circuits offer the possibility of realizing novel functionalities at low power. Current-controlled electrical switching of magnetization is actively researched in this context. Complex oxide heterostructures with perpendicular magnetic anisotropy (PMA), consisting of SrRuO 3 (SRO) grown on SrTiO 3 (STO) are strong material contenders. Utilizing the crystal orientation, magnetic anisotropy in such simple heterostructures can be tuned to either exhibit a perfect or slightly tilted PMA. Here, we investigate current induced magnetization modulation in such tailored ferromagnetic layers with a material with strong spin-orbit coupling (Pt), exploiting the spin Hall effect. We find significant differences in the magnetic anisotropy between the SRO/STO heterostructures, as manifested in the first and second harmonic magnetoresistance measurements. Current-induced magnetization switching can be realized with spin-orbit torques, but for systems with perfect PMA this switching is probabilistic as a result of the high symmetry. Slight tilting of the PMA can break this symmetry and allow the realization of deterministic switching. Control over the magnetic anisotropy of our heterostructures therefore provides control over the manner of switching. Based on our findings, we propose a three-terminal spintronic memristor, with a magnetic tunnel junction design, that shows several resistive states controlled by electric charge. Non-volatile states can be written through SOT by applying an in-plane current, and read out as a tunnel current by applying a small out-of-plane current. Depending on the anisotropy of the SRO layer, the writing mechanism is either deterministic or probabilistic allowing for different functionalities to emerge. We envisage that the probabilistic MTJs could be used as synapses while the deterministic devices can emulate neurons.

show abstract

Image segmentation with stochastic magnetic tunnel junctions and spiking neurons

Cited by 4 publications

References 24 publications

Analysis of Liquid Ensembles for Enhancing the Performance and Accuracy of Liquid State Machines

Analysis of Liquid Ensembles for Enhancing the Performance and Accuracy of Liquid State Machines

Anisotropy and Current Control of Magnetization in SrRuO3/SrTiO3 Heterostructures for Spin-Memristors

Anisotropy and Current Control of Magnetization in SrRuO3 SrTiO3 Heterostructures for Spin-Memristors

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