The Experts below are selected from a list of 306 Experts worldwide ranked by ideXlab platform
Joseph S Friedman - One of the best experts on this subject based on the ideXlab platform.
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Lateral Inhibition in magnetic domain wall racetrack arrays for neuromorphic computing
Spintronics XIII, 2020Co-Authors: Can Cui, Naimul Hassan, Otitoaleke G Akinola, Christopher H Bennett, Joseph S Friedman, Matthew J. Marinella, Jean Anne C IncorviaAbstract:Neuromorphic computing captures the quintessential neural behaviors of the brain and is a promising candidate for the beyond-von Neumann computer architectures, featuring low power consumption and high parallelism. The neuronal Lateral Inhibition feature, closely associated with the biological receptive field, is crucial to neuronal competition in the nervous system as well as its neuromorphic hardware counterpart. The domain wall - magnetic tunnel junction (DW-MTJ) neuron is an emerging spintronic artificial neuron device exhibiting intrinsic Lateral Inhibition. This work discusses Lateral Inhibition mechanism of the DW-MTJ neuron and shows by micromagnetic simulation that Lateral Inhibition is efficiently enhanced by the Dzyaloshinskii-Moriya interaction (DMI).
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Maximized Lateral Inhibition in paired magnetic domain wall racetracks for neuromorphic computing.
Nanotechnology, 2020Co-Authors: Can Cui, Naimul Hassan, Otitoaleke G Akinola, Christopher H Bennett, Joseph S Friedman, Matthew J. Marinella, Jean Anne C IncorviaAbstract:Lateral Inhibition is an important functionality in neuromorphic computing, modeled after the biological neuron behavior that a firing neuron deactivates its neighbors belonging to the same layer and prevents them from firing. In most neuromorphic hardware platforms Lateral Inhibition is implemented by external circuitry, thereby decreasing the energy efficiency and increasing the area overhead of such systems. Recently, the domain wall-magnetic tunnel junction (DW-MTJ) artificial neuron is demonstrated in modeling to be intrinsically inhibitory. Without peripheral circuitry, Lateral Inhibition in DW-MTJ neurons results from magnetostatic interaction between neighboring neuron cells. However, the Lateral Inhibition mechanism in DW-MTJ neurons has not been studied thoroughly, leading to weak Inhibition only in very closely-spaced devices. This work approaches these problems by modeling current- and field- driven DW motion in a pair of adjacent DW-MTJ neurons. We maximize the magnitude of Lateral Inhibition by tuning the magnetic interaction between the neurons. The results are explained by current-driven DW velocity characteristics in response to an external magnetic field and quantified by an analytical model. Dependence of Lateral Inhibition strength on device parameters is also studied. Finally, Lateral Inhibition behavior in an array of 1000 DW-MTJ neurons is demonstrated. Our results provide a guideline for the optimization of Lateral Inhibition implementation in DW-MTJ neurons. With strong Lateral Inhibition achieved, a path towards competitive learning algorithms such as the winner-take-all are made possible on such neuromorphic devices.
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Maximized Lateral Inhibition in Paired Magnetic Domain Wall Racetracks for Neuromorphic Computing
arXiv: Applied Physics, 2019Co-Authors: Can Cui, Naimul Hassan, Otitoaleke G Akinola, Christopher H Bennett, Joseph S Friedman, Matthew J. Marinella, Jean Anne C IncorviaAbstract:Lateral Inhibition is an important functionality in neuromorphic computing, modeled after the biological neuron behavior that a firing neuron deactivates its neighbors belonging to the same layer and prevents them from firing. In most neuromorphic hardware platforms Lateral Inhibition is implemented by external circuitry, thereby decreasing the energy efficiency and increasing the area overhead of such systems. Recently, the domain wall -- magnetic tunnel junction (DW-MTJ) artificial neuron is demonstrated in modeling to be inherently inhibitory. Without peripheral circuitry, Lateral Inhibition in DW-MTJ neurons results from magnetostatic interaction between neighboring neuron cells. However, the Lateral Inhibition mechanism in DW-MTJ neurons has not been studied thoroughly, leading to weak Inhibition only in very closely-spaced devices. This work approaches these problems by modeling current- and field- driven DW motion in a pair of adjacent DW-MTJ neurons. We maximize the magnitude of Lateral Inhibition by tuning the magnetic interaction between the neurons. The results are explained by current-driven DW velocity characteristics in response to external magnetic field and quantified by an analytical model. Finally, the dependence of Lateral Inhibition strength on device parameters is investigated. This provides a guideline for the optimization of Lateral Inhibition implementation in DW-MTJ neurons. With strong Lateral Inhibition achieved, a path towards competitive learning algorithms such as the winner-take-all are made possible on such neuromorphic devices.
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Magnetic domain wall neuron with intrinsic leaking and Lateral Inhibition capability
Spintronics XII, 2019Co-Authors: Wesley H Brigner, Naimul Hassan, Otitoaleke G Akinola, M Pasquale, Christopher H Bennett, Jean Anne C Incorvia, Lucian Jiang-wei, Felipe Garcia-sanchez, Joseph S FriedmanAbstract:The challenge of developing an efficient artificial neuron is impeded by the use of external CMOS circuits to perform leaking and Lateral Inhibition. The proposed leaky integrate-and-fire neuron based on the three terminal magnetic tunnel junction (3T-MTJ) performs integration by pushing its domain wall (DW) with spin-transfer or spin-orbit torque. The leaking capability is achieved by pushing the neurons’ DWs in the direction opposite of integration using a stray field from a hard ferromagnet or a non-uniform energy landscape resulting from shape or anisotropy variation. Firing is performed by the MTJ stack. Finally, analog Lateral Inhibition is achieved by dipolar field repulsive coupling from each neuron. An integrating neuron thus pushes slower neighboring neurons’ DWs in the direction opposite of integration. Applying this Lateral Inhibition to a ten-neuron output layer within a neuromorphic crossbar structure enables the identification of handwritten digits with 94% accuracy.
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magnetic domain wall neuron with Lateral Inhibition
Journal of Applied Physics, 2018Co-Authors: Naimul Hassan, Lucian Jiangwei, Wesley H Brigner, Otitoaleke G Akinola, Felipe Garciasanchez, M Pasquale, Christopher H Bennett, Jean Anne C Incorvia, Joseph S FriedmanAbstract:The development of an efficient neuromorphic computing system requires the use of nanodevices that intrinsically emulate the biological behavior of neurons and synapses. While numerous artificial synapses have been shown to store weights in a manner analogous to biological synapses, the challenge of developing an artificial neuron is impeded by the necessity to include leaking, integrating, firing, and Lateral Inhibition features. In particular, previous proposals for artificial neurons have required the use of external circuits to perform Lateral Inhibition, thereby decreasing the efficiency of the resulting neuromorphic computing system. This work therefore proposes a leaky integrate-and-fire neuron that intrinsically provides Lateral Inhibition, without requiring any additional circuitry. The proposed neuron is based on the previously proposed domain-wall magnetic tunnel junction devices, which have been proposed as artificial synapses and experimentally demonstrated for non-volatile logic. Single-neuron micromagnetic simulations are provided that demonstrate the ability of this neuron to implement the required leaking, integrating, and firing. These simulations are then extended to pairs of adjacent neurons to demonstrate, for the first time, Lateral Inhibition between neighboring artificial neurons. Finally, this intrinsic Lateral Inhibition is applied to a ten-neuron crossbar structure and trained to identify handwritten digits and shown via direct large-scale micromagnetic simulation for 100 digits to correctly identify the proper signal for 94% of the digits.The development of an efficient neuromorphic computing system requires the use of nanodevices that intrinsically emulate the biological behavior of neurons and synapses. While numerous artificial synapses have been shown to store weights in a manner analogous to biological synapses, the challenge of developing an artificial neuron is impeded by the necessity to include leaking, integrating, firing, and Lateral Inhibition features. In particular, previous proposals for artificial neurons have required the use of external circuits to perform Lateral Inhibition, thereby decreasing the efficiency of the resulting neuromorphic computing system. This work therefore proposes a leaky integrate-and-fire neuron that intrinsically provides Lateral Inhibition, without requiring any additional circuitry. The proposed neuron is based on the previously proposed domain-wall magnetic tunnel junction devices, which have been proposed as artificial synapses and experimentally demonstrated for non-volatile logic. Single-neur...
Norberto M. Grzywacz - One of the best experts on this subject based on the ideXlab platform.
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The Minimal Local-Asperity Hypothesis of Early Retinal Lateral Inhibition
Neural computation, 2000Co-Authors: Rosario M. Balboa, Norberto M. GrzywaczAbstract:Recently we found that the theories related to information theory existent in the literature cannot explain the behavior of the extent of the Lateral Inhibition mediated by retinal horizontal cells as a function of background light intensity. These theories can explain the fall of the extent from intermediate to high intensities, but not its rise from dim to intermediate intensities. We propose an alternate hypothesis that accounts for the extent’s bell-shape behavior. This hypothesis proposes that the Lateral-Inhibition adaptation in the early retina is part of a system to extract several image attributes, such as occlusion borders and contrast. To do so, this system would use prior probabilistic knowledge about the biological processing and relevant statistics in natural images. A key novel statistic used here is the probability of the presence of an occlusion border as a function of local contrast. Using this probabilistic knowledge, the retina would optimize the spatial profile of Lateral Inhibition to minimize attribute-extraction error. The two significant errors that this minimization process must reduce are due to the quantal noise in photoreceptors and the straddling of occlusion borders by Lateral Inhibition.
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The role of early retinal Lateral Inhibition : More than maximizing luminance information
Visual Neuroscience, 2000Co-Authors: Rosario M. Balboa, Norberto M. GrzywaczAbstract:Lateral Inhibition is one of the first and most important stages of visual processing. There are at least four theories related to information theory in the literature for the role of early retinal Lateral Inhibition. They are based on the spatial redundancy in natural images and the advantage of removing this redundancy from the visual code. Here, we contrast these theories with data from the retina's outer plexiform layer. The horizontal cells' Lateral-Inhibition extent displays a bell-shape behavior as function of background luminance, whereas all the theories show a fall as luminance increases. It is remarkable that different theories predict the same luminance behavior, explaining "half" of the biological data. We argue that the main reason is how these theories deal with photon-absorption noise. At dim light levels, for which this noise is relatively large, large receptive fields would increase the signal-to-noise ratio through averaging. Unfortunately, such an increase at low luminance levels may smooth out basic visual information of natural images. To explain the biological behavior, we describe an alternate hypothesis, which proposes that the role of early visual Lateral Inhibition is to deal with noise without missing relevant clues from the visual world, most prominently, the occlusion boundaries between objects.
Jean Anne C Incorvia - One of the best experts on this subject based on the ideXlab platform.
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Lateral Inhibition in magnetic domain wall racetrack arrays for neuromorphic computing
Spintronics XIII, 2020Co-Authors: Can Cui, Naimul Hassan, Otitoaleke G Akinola, Christopher H Bennett, Joseph S Friedman, Matthew J. Marinella, Jean Anne C IncorviaAbstract:Neuromorphic computing captures the quintessential neural behaviors of the brain and is a promising candidate for the beyond-von Neumann computer architectures, featuring low power consumption and high parallelism. The neuronal Lateral Inhibition feature, closely associated with the biological receptive field, is crucial to neuronal competition in the nervous system as well as its neuromorphic hardware counterpart. The domain wall - magnetic tunnel junction (DW-MTJ) neuron is an emerging spintronic artificial neuron device exhibiting intrinsic Lateral Inhibition. This work discusses Lateral Inhibition mechanism of the DW-MTJ neuron and shows by micromagnetic simulation that Lateral Inhibition is efficiently enhanced by the Dzyaloshinskii-Moriya interaction (DMI).
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Maximized Lateral Inhibition in paired magnetic domain wall racetracks for neuromorphic computing.
Nanotechnology, 2020Co-Authors: Can Cui, Naimul Hassan, Otitoaleke G Akinola, Christopher H Bennett, Joseph S Friedman, Matthew J. Marinella, Jean Anne C IncorviaAbstract:Lateral Inhibition is an important functionality in neuromorphic computing, modeled after the biological neuron behavior that a firing neuron deactivates its neighbors belonging to the same layer and prevents them from firing. In most neuromorphic hardware platforms Lateral Inhibition is implemented by external circuitry, thereby decreasing the energy efficiency and increasing the area overhead of such systems. Recently, the domain wall-magnetic tunnel junction (DW-MTJ) artificial neuron is demonstrated in modeling to be intrinsically inhibitory. Without peripheral circuitry, Lateral Inhibition in DW-MTJ neurons results from magnetostatic interaction between neighboring neuron cells. However, the Lateral Inhibition mechanism in DW-MTJ neurons has not been studied thoroughly, leading to weak Inhibition only in very closely-spaced devices. This work approaches these problems by modeling current- and field- driven DW motion in a pair of adjacent DW-MTJ neurons. We maximize the magnitude of Lateral Inhibition by tuning the magnetic interaction between the neurons. The results are explained by current-driven DW velocity characteristics in response to an external magnetic field and quantified by an analytical model. Dependence of Lateral Inhibition strength on device parameters is also studied. Finally, Lateral Inhibition behavior in an array of 1000 DW-MTJ neurons is demonstrated. Our results provide a guideline for the optimization of Lateral Inhibition implementation in DW-MTJ neurons. With strong Lateral Inhibition achieved, a path towards competitive learning algorithms such as the winner-take-all are made possible on such neuromorphic devices.
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Maximized Lateral Inhibition in Paired Magnetic Domain Wall Racetracks for Neuromorphic Computing
arXiv: Applied Physics, 2019Co-Authors: Can Cui, Naimul Hassan, Otitoaleke G Akinola, Christopher H Bennett, Joseph S Friedman, Matthew J. Marinella, Jean Anne C IncorviaAbstract:Lateral Inhibition is an important functionality in neuromorphic computing, modeled after the biological neuron behavior that a firing neuron deactivates its neighbors belonging to the same layer and prevents them from firing. In most neuromorphic hardware platforms Lateral Inhibition is implemented by external circuitry, thereby decreasing the energy efficiency and increasing the area overhead of such systems. Recently, the domain wall -- magnetic tunnel junction (DW-MTJ) artificial neuron is demonstrated in modeling to be inherently inhibitory. Without peripheral circuitry, Lateral Inhibition in DW-MTJ neurons results from magnetostatic interaction between neighboring neuron cells. However, the Lateral Inhibition mechanism in DW-MTJ neurons has not been studied thoroughly, leading to weak Inhibition only in very closely-spaced devices. This work approaches these problems by modeling current- and field- driven DW motion in a pair of adjacent DW-MTJ neurons. We maximize the magnitude of Lateral Inhibition by tuning the magnetic interaction between the neurons. The results are explained by current-driven DW velocity characteristics in response to external magnetic field and quantified by an analytical model. Finally, the dependence of Lateral Inhibition strength on device parameters is investigated. This provides a guideline for the optimization of Lateral Inhibition implementation in DW-MTJ neurons. With strong Lateral Inhibition achieved, a path towards competitive learning algorithms such as the winner-take-all are made possible on such neuromorphic devices.
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Magnetic domain wall neuron with intrinsic leaking and Lateral Inhibition capability
Spintronics XII, 2019Co-Authors: Wesley H Brigner, Naimul Hassan, Otitoaleke G Akinola, M Pasquale, Christopher H Bennett, Jean Anne C Incorvia, Lucian Jiang-wei, Felipe Garcia-sanchez, Joseph S FriedmanAbstract:The challenge of developing an efficient artificial neuron is impeded by the use of external CMOS circuits to perform leaking and Lateral Inhibition. The proposed leaky integrate-and-fire neuron based on the three terminal magnetic tunnel junction (3T-MTJ) performs integration by pushing its domain wall (DW) with spin-transfer or spin-orbit torque. The leaking capability is achieved by pushing the neurons’ DWs in the direction opposite of integration using a stray field from a hard ferromagnet or a non-uniform energy landscape resulting from shape or anisotropy variation. Firing is performed by the MTJ stack. Finally, analog Lateral Inhibition is achieved by dipolar field repulsive coupling from each neuron. An integrating neuron thus pushes slower neighboring neurons’ DWs in the direction opposite of integration. Applying this Lateral Inhibition to a ten-neuron output layer within a neuromorphic crossbar structure enables the identification of handwritten digits with 94% accuracy.
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magnetic domain wall neuron with Lateral Inhibition
Journal of Applied Physics, 2018Co-Authors: Naimul Hassan, Lucian Jiangwei, Wesley H Brigner, Otitoaleke G Akinola, Felipe Garciasanchez, M Pasquale, Christopher H Bennett, Jean Anne C Incorvia, Joseph S FriedmanAbstract:The development of an efficient neuromorphic computing system requires the use of nanodevices that intrinsically emulate the biological behavior of neurons and synapses. While numerous artificial synapses have been shown to store weights in a manner analogous to biological synapses, the challenge of developing an artificial neuron is impeded by the necessity to include leaking, integrating, firing, and Lateral Inhibition features. In particular, previous proposals for artificial neurons have required the use of external circuits to perform Lateral Inhibition, thereby decreasing the efficiency of the resulting neuromorphic computing system. This work therefore proposes a leaky integrate-and-fire neuron that intrinsically provides Lateral Inhibition, without requiring any additional circuitry. The proposed neuron is based on the previously proposed domain-wall magnetic tunnel junction devices, which have been proposed as artificial synapses and experimentally demonstrated for non-volatile logic. Single-neuron micromagnetic simulations are provided that demonstrate the ability of this neuron to implement the required leaking, integrating, and firing. These simulations are then extended to pairs of adjacent neurons to demonstrate, for the first time, Lateral Inhibition between neighboring artificial neurons. Finally, this intrinsic Lateral Inhibition is applied to a ten-neuron crossbar structure and trained to identify handwritten digits and shown via direct large-scale micromagnetic simulation for 100 digits to correctly identify the proper signal for 94% of the digits.The development of an efficient neuromorphic computing system requires the use of nanodevices that intrinsically emulate the biological behavior of neurons and synapses. While numerous artificial synapses have been shown to store weights in a manner analogous to biological synapses, the challenge of developing an artificial neuron is impeded by the necessity to include leaking, integrating, firing, and Lateral Inhibition features. In particular, previous proposals for artificial neurons have required the use of external circuits to perform Lateral Inhibition, thereby decreasing the efficiency of the resulting neuromorphic computing system. This work therefore proposes a leaky integrate-and-fire neuron that intrinsically provides Lateral Inhibition, without requiring any additional circuitry. The proposed neuron is based on the previously proposed domain-wall magnetic tunnel junction devices, which have been proposed as artificial synapses and experimentally demonstrated for non-volatile logic. Single-neur...
Veronica A. Alvarez - One of the best experts on this subject based on the ideXlab platform.
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Striatal Local Circuitry: A New Framework for Lateral Inhibition.
Neuron, 2017Co-Authors: Dennis A. Burke, Horacio G. Rotstein, Veronica A. AlvarezAbstract:This Perspective will examine the organization of intrastriatal circuitry, review recent findings in this area, and discuss how the pattern of connectivity between striatal neurons might give rise to the behaviorally observed synergism between the direct/indirect pathway neurons. The emphasis of this Perspective is on the underappreciated role of Lateral Inhibition between striatal projection cells in controlling neuronal firing and shaping the output of this circuit. We review some classic studies in combination with more recent anatomical and functional findings to lay out a framework for an updated model of the intrastriatal Lateral Inhibition, where we explore its contribution to the formation of functional units of processing and the integration and filtering of inputs to generate motor patterns and learned behaviors.
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dopamine regulation of Lateral Inhibition between striatal neurons gates the stimulant actions of cocaine
Neuron, 2016Co-Authors: Lauren K Dobbs, Alanna R Kaplan, Julia C Lemos, Aya Matsui, Marcelo Rubinstein, Veronica A. AlvarezAbstract:Striatal medium spiny neurons (MSNs) form inhibitory synapses on neighboring striatal neurons through axon colLaterals. The functional relevance of this Lateral Inhibition and its regulation by dopamine remains elusive. We show that synchronized stimulation of colLateral transmission from multiple indirect-pathway MSNs (iMSNs) potently inhibits action potentials in direct-pathway MSNs (dMSNs) in the nucleus accumbens. Dopamine D2 receptors (D2Rs) suppress Lateral Inhibition from iMSNs to disinhibit dMSNs, which are known to facilitate locomotion. Surprisingly, D2R Inhibition of synaptic transmission was larger at axon colLaterals from iMSNs than their projections to the ventral pallidum. Targeted deletion of D2Rs from iMSNs impaired cocaine's ability to suppress Lateral Inhibition and increase locomotion. These impairments were rescued by chemogenetic activation of Gi-signaling in iMSNs. These findings shed light on the functional significance of Lateral Inhibition between MSNs and offer a novel synaptic mechanism by which dopamine gates locomotion and cocaine exerts its canonical stimulant response. VIDEO ABSTRACT.
Naimul Hassan - One of the best experts on this subject based on the ideXlab platform.
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Lateral Inhibition in magnetic domain wall racetrack arrays for neuromorphic computing
Spintronics XIII, 2020Co-Authors: Can Cui, Naimul Hassan, Otitoaleke G Akinola, Christopher H Bennett, Joseph S Friedman, Matthew J. Marinella, Jean Anne C IncorviaAbstract:Neuromorphic computing captures the quintessential neural behaviors of the brain and is a promising candidate for the beyond-von Neumann computer architectures, featuring low power consumption and high parallelism. The neuronal Lateral Inhibition feature, closely associated with the biological receptive field, is crucial to neuronal competition in the nervous system as well as its neuromorphic hardware counterpart. The domain wall - magnetic tunnel junction (DW-MTJ) neuron is an emerging spintronic artificial neuron device exhibiting intrinsic Lateral Inhibition. This work discusses Lateral Inhibition mechanism of the DW-MTJ neuron and shows by micromagnetic simulation that Lateral Inhibition is efficiently enhanced by the Dzyaloshinskii-Moriya interaction (DMI).
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Maximized Lateral Inhibition in paired magnetic domain wall racetracks for neuromorphic computing.
Nanotechnology, 2020Co-Authors: Can Cui, Naimul Hassan, Otitoaleke G Akinola, Christopher H Bennett, Joseph S Friedman, Matthew J. Marinella, Jean Anne C IncorviaAbstract:Lateral Inhibition is an important functionality in neuromorphic computing, modeled after the biological neuron behavior that a firing neuron deactivates its neighbors belonging to the same layer and prevents them from firing. In most neuromorphic hardware platforms Lateral Inhibition is implemented by external circuitry, thereby decreasing the energy efficiency and increasing the area overhead of such systems. Recently, the domain wall-magnetic tunnel junction (DW-MTJ) artificial neuron is demonstrated in modeling to be intrinsically inhibitory. Without peripheral circuitry, Lateral Inhibition in DW-MTJ neurons results from magnetostatic interaction between neighboring neuron cells. However, the Lateral Inhibition mechanism in DW-MTJ neurons has not been studied thoroughly, leading to weak Inhibition only in very closely-spaced devices. This work approaches these problems by modeling current- and field- driven DW motion in a pair of adjacent DW-MTJ neurons. We maximize the magnitude of Lateral Inhibition by tuning the magnetic interaction between the neurons. The results are explained by current-driven DW velocity characteristics in response to an external magnetic field and quantified by an analytical model. Dependence of Lateral Inhibition strength on device parameters is also studied. Finally, Lateral Inhibition behavior in an array of 1000 DW-MTJ neurons is demonstrated. Our results provide a guideline for the optimization of Lateral Inhibition implementation in DW-MTJ neurons. With strong Lateral Inhibition achieved, a path towards competitive learning algorithms such as the winner-take-all are made possible on such neuromorphic devices.
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Maximized Lateral Inhibition in Paired Magnetic Domain Wall Racetracks for Neuromorphic Computing
arXiv: Applied Physics, 2019Co-Authors: Can Cui, Naimul Hassan, Otitoaleke G Akinola, Christopher H Bennett, Joseph S Friedman, Matthew J. Marinella, Jean Anne C IncorviaAbstract:Lateral Inhibition is an important functionality in neuromorphic computing, modeled after the biological neuron behavior that a firing neuron deactivates its neighbors belonging to the same layer and prevents them from firing. In most neuromorphic hardware platforms Lateral Inhibition is implemented by external circuitry, thereby decreasing the energy efficiency and increasing the area overhead of such systems. Recently, the domain wall -- magnetic tunnel junction (DW-MTJ) artificial neuron is demonstrated in modeling to be inherently inhibitory. Without peripheral circuitry, Lateral Inhibition in DW-MTJ neurons results from magnetostatic interaction between neighboring neuron cells. However, the Lateral Inhibition mechanism in DW-MTJ neurons has not been studied thoroughly, leading to weak Inhibition only in very closely-spaced devices. This work approaches these problems by modeling current- and field- driven DW motion in a pair of adjacent DW-MTJ neurons. We maximize the magnitude of Lateral Inhibition by tuning the magnetic interaction between the neurons. The results are explained by current-driven DW velocity characteristics in response to external magnetic field and quantified by an analytical model. Finally, the dependence of Lateral Inhibition strength on device parameters is investigated. This provides a guideline for the optimization of Lateral Inhibition implementation in DW-MTJ neurons. With strong Lateral Inhibition achieved, a path towards competitive learning algorithms such as the winner-take-all are made possible on such neuromorphic devices.
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Magnetic domain wall neuron with intrinsic leaking and Lateral Inhibition capability
Spintronics XII, 2019Co-Authors: Wesley H Brigner, Naimul Hassan, Otitoaleke G Akinola, M Pasquale, Christopher H Bennett, Jean Anne C Incorvia, Lucian Jiang-wei, Felipe Garcia-sanchez, Joseph S FriedmanAbstract:The challenge of developing an efficient artificial neuron is impeded by the use of external CMOS circuits to perform leaking and Lateral Inhibition. The proposed leaky integrate-and-fire neuron based on the three terminal magnetic tunnel junction (3T-MTJ) performs integration by pushing its domain wall (DW) with spin-transfer or spin-orbit torque. The leaking capability is achieved by pushing the neurons’ DWs in the direction opposite of integration using a stray field from a hard ferromagnet or a non-uniform energy landscape resulting from shape or anisotropy variation. Firing is performed by the MTJ stack. Finally, analog Lateral Inhibition is achieved by dipolar field repulsive coupling from each neuron. An integrating neuron thus pushes slower neighboring neurons’ DWs in the direction opposite of integration. Applying this Lateral Inhibition to a ten-neuron output layer within a neuromorphic crossbar structure enables the identification of handwritten digits with 94% accuracy.
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magnetic domain wall neuron with Lateral Inhibition
Journal of Applied Physics, 2018Co-Authors: Naimul Hassan, Lucian Jiangwei, Wesley H Brigner, Otitoaleke G Akinola, Felipe Garciasanchez, M Pasquale, Christopher H Bennett, Jean Anne C Incorvia, Joseph S FriedmanAbstract:The development of an efficient neuromorphic computing system requires the use of nanodevices that intrinsically emulate the biological behavior of neurons and synapses. While numerous artificial synapses have been shown to store weights in a manner analogous to biological synapses, the challenge of developing an artificial neuron is impeded by the necessity to include leaking, integrating, firing, and Lateral Inhibition features. In particular, previous proposals for artificial neurons have required the use of external circuits to perform Lateral Inhibition, thereby decreasing the efficiency of the resulting neuromorphic computing system. This work therefore proposes a leaky integrate-and-fire neuron that intrinsically provides Lateral Inhibition, without requiring any additional circuitry. The proposed neuron is based on the previously proposed domain-wall magnetic tunnel junction devices, which have been proposed as artificial synapses and experimentally demonstrated for non-volatile logic. Single-neuron micromagnetic simulations are provided that demonstrate the ability of this neuron to implement the required leaking, integrating, and firing. These simulations are then extended to pairs of adjacent neurons to demonstrate, for the first time, Lateral Inhibition between neighboring artificial neurons. Finally, this intrinsic Lateral Inhibition is applied to a ten-neuron crossbar structure and trained to identify handwritten digits and shown via direct large-scale micromagnetic simulation for 100 digits to correctly identify the proper signal for 94% of the digits.The development of an efficient neuromorphic computing system requires the use of nanodevices that intrinsically emulate the biological behavior of neurons and synapses. While numerous artificial synapses have been shown to store weights in a manner analogous to biological synapses, the challenge of developing an artificial neuron is impeded by the necessity to include leaking, integrating, firing, and Lateral Inhibition features. In particular, previous proposals for artificial neurons have required the use of external circuits to perform Lateral Inhibition, thereby decreasing the efficiency of the resulting neuromorphic computing system. This work therefore proposes a leaky integrate-and-fire neuron that intrinsically provides Lateral Inhibition, without requiring any additional circuitry. The proposed neuron is based on the previously proposed domain-wall magnetic tunnel junction devices, which have been proposed as artificial synapses and experimentally demonstrated for non-volatile logic. Single-neur...
