Low-Energy Implementation of Feed-Forward Neural Network With Back-Propagation Algorithm Using a Spin-Orbit Torque Driven Skyrmionic Device

Saxena, Utkarsh; Kaushik, Divya; Bansal, Mudit; Sahu, Upasana; Bhowmik, Debjit

doi:10.1109/tmag.2018.2853082

Cited by 19 publications

(19 citation statements)

References 20 publications

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“…PMA in rest of the layer = 8 × 10 5 J/m 3 . These notch regions mimic defects, which pin the domain wall for in-plane charge current lower than a certain threshold value [15,22,23,24,31]. Hence, our micro-magnetic simulation Fig.…”

Section: Device Level Comparisonmentioning

confidence: 78%

“…1. The operating physics of the device has been discussed extensively in [3,14,15,17]. The core physics is that of spin orbit torque driven DW motion, which has been extensively studied through simulations and experiments in the past [26,27,28,29].…”

Section: Device Level Comparisonmentioning

confidence: 99%

“…Spin orbit torque driven Domain Wall (DW) device based on heavy metal-ferromagnet hetero-structure has been recently proposed and experimentally demonstrated to exhibit synaptic behaviour [3,14,15,16,17,18,19,20]. In Section II of this paper, we simulate such DW synapse based on experimentally calibrated micromagnetic model.…”

Section: Introductionmentioning

confidence: 99%

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Comparing domain wall synapse with other non volatile memory devices for on-chip learning in analog hardware neural network

et al. 2020

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Resistive Random Access Memory (RRAM) and Phase Change Memory (PCM) devices have been popularly used as synapses in crossbar array based analog Neural Network (NN) circuit to achieve more energy and time efficient data classification compared to conventional computers. Here we demonstrate the advantages of recently proposed spin orbit torque driven Domain Wall (DW) device as synapse compared to the RRAM and PCM devices with respect to on-chip learning (training in hardware) in such NN. Synaptic characteristic of DW synapse, obtained by us from micromagnetic modeling, turns out to be much more linear and symmetric (between positive and negative update) than that of RRAM and PCM synapse. This makes design of peripheral analog circuits for on-chip learning much easier in DW synapse based NN compared to that for RRAM and PCM synapses. We next incorporate the DW synapse as a Verilog-A model in the crossbar array based NN circuit we design on SPICE circuit simulator. Successful on-chip learning is demonstrated through SPICE simulations on the popular Fisher's Iris dataset. Time and energy required for learning turn out to be orders of magnitude lower for DW synapse based NN circuit compared to that for RRAM and PCM synapse based NN circuits.

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Section: Device Level Comparisonmentioning

confidence: 78%

Section: Device Level Comparisonmentioning

confidence: 99%

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Comparing domain wall synapse with other non volatile memory devices for on-chip learning in analog hardware neural network

et al. 2020

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“…Current driven domain wall motion in heavy metal/ ferromagnetic metal heterostructure based spintronic device has been utilized to propose and experimentally demonstrate synaptic behavior in such a device [34], [35], [44], [45]. In-plane current, also known as "write" current, flowing through the device moves the ferromagnetic domain wall in the ferromagnetic layer even in the absence of magnetic field as observed in experiments and simulations [46]- [50] (Fig.…”

Section: A Domain Wall Synapsementioning

confidence: 99%

Training of a Spiking Neural Network on spintronics based analog hardware for handwritten digit recognition

Sahu¹,

Goyal²,

Bhowmik³

2021

Preprint

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We trained Spiking neural network (SNN) using spike time dependent plasticity (STDP)-enabled learning under two different learning schemes in MNIST data set(hand written digit recognition). We showed how the post-neurons need to be far more in number than the output classes for larger data sets in the case of SNN for reasonably high accuracy number. We have also reported the net energy consumed for learning in the spintronic devices and associated transistor-based circuits that enable synaptic functionality for this SNN.

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“…Including material defects, they compared this skyrmion-based synapse with a domain wall-based one, as they were described above. In a simulation of a standard digit recognition problem, the skyrmion-based artificial neural network necessitated two orders or magnitude less energy than the domain wall-based one [43].…”

Section: Skyrmionsmentioning

confidence: 99%

Magnetic Elements for Neuromorphic Computing

Błachowicz

Ehrmann

2020

Molecules

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Neuromorphic computing is assumed to be significantly more energy efficient than, and at the same time expected to outperform, conventional computers in several applications, such as data classification, since it overcomes the so-called von Neumann bottleneck. Artificial synapses and neurons can be implemented into conventional hardware using new software, but also be created by diverse spintronic devices and other elements to completely avoid the disadvantages of recent hardware architecture. Here, we report on diverse approaches to implement neuromorphic functionalities in novel hardware using magnetic elements, published during the last years. Magnetic elements play an important role in neuromorphic computing. While other approaches, such as optical and conductive elements, are also under investigation in many groups, magnetic nanostructures and generally magnetic materials offer large advantages, especially in terms of data storage, but they can also unambiguously be used for data transport, e.g., by propagation of skyrmions or domain walls. This review underlines the possible applications of magnetic materials and nanostructures in neuromorphic systems.

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Low-Energy Implementation of Feed-Forward Neural Network With Back-Propagation Algorithm Using a Spin-Orbit Torque Driven Skyrmionic Device

Cited by 19 publications

References 20 publications

Comparing domain wall synapse with other non volatile memory devices for on-chip learning in analog hardware neural network

Comparing domain wall synapse with other non volatile memory devices for on-chip learning in analog hardware neural network

Training of a Spiking Neural Network on spintronics based analog hardware for handwritten digit recognition

Magnetic Elements for Neuromorphic Computing

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