Skyrmion based energy-efficient straintronic physical reservoir computing

Mahadi, Rajib, Md; Misba, Walid Al; Chowdhury, Md. Fahim F.; Alam, M. S.; Atulasimha, Jayasimha

doi:10.1088/2634-4386/aca178

Cited by 10 publications

(10 citation statements)

References 51 publications

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“…The input energy (including the ferroelectric hysteresis loss) and the output read energy are taken into account (see supplementary information S8 ). The total power dissipation is less than 1 fJ, which is 2 orders of magnitude lower than that of other spintronic reservoirs 13 , 26 .…”

Section: Discussionmentioning

confidence: 87%

“…In this part, we use a representative sequential waveform classification task 21 , 24 , 26 to test the performance of our skyrmion-enhanced strain-mediated RC system. The input signal is a random waveform sequence comprising square and sine waveforms (labelled as ‘0’ and ‘1’, respectively) encoded as the voltage V (t) applied to the RC system, and the output signal is the AHE voltage V xy .…”

Section: Resultsmentioning

confidence: 99%

“…The parity check capacity ( C PC ) and short-term memory content ( C STM ) at the skyrmion state are both around 2.31, which are restricted by the slow operation of the present configuration. However, they could be significantly enhanced through involving the fast magnetization dynamics 26 .…”

Section: Resultsmentioning

confidence: 99%

“…However, either the electrical currents or magnetic fields are served as inputs, which is not advantageous to lower energy costs. Accordingly, a strain-mediated voltage-controlled skyrmion RC block employing the nonlinear breathing dynamics has been proposed, with an estimated energy dissipation of 50 fJ per single input, which is three-four orders of magnitude lower than that of the CMOS-based reservoir 26 . Moreover, strain is a universal way to control various characteristics, which have been widely studied, including magnetization 43 – 48 , resistance 49 , 50 , Dzyaloshinskii–Moriya interaction (DMI) 51 , phase transitions 52 , luminescence 53 , etc.…”

Section: Introductionmentioning

confidence: 99%

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Experimental demonstration of a skyrmion-enhanced strain-mediated physical reservoir computing system

et al. 2023

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Physical reservoirs holding intrinsic nonlinearity, high dimensionality, and memory effects have attracted considerable interest regarding solving complex tasks efficiently. Particularly, spintronic and strain-mediated electronic physical reservoirs are appealing due to their high speed, multi-parameter fusion and low power consumption. Here, we experimentally realize a skyrmion-enhanced strain-mediated physical reservoir in a multiferroic heterostructure of Pt/Co/Gd multilayers on (001)-oriented 0.7PbMg1/3Nb2/3O3−0.3PbTiO3 (PMN-PT). The enhancement is coming from the fusion of magnetic skyrmions and electro resistivity tuned by strain simultaneously. The functionality of the strain-mediated RC system is successfully achieved via a sequential waveform classification task with the recognition rate of 99.3% for the last waveform, and a Mackey-Glass time series prediction task with normalized root mean square error (NRMSE) of 0.2 for a 20-step prediction. Our work lays the foundations for low-power neuromorphic computing systems with magneto-electro-ferroelastic tunability, representing a further step towards developing future strain-mediated spintronic applications.

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Section: Discussionmentioning

confidence: 87%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Experimental demonstration of a skyrmion-enhanced strain-mediated physical reservoir computing system

et al. 2023

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“…Various devices concepts are proposed for spintronic reservoir including nanoscale magnetic structures such as spin torque nano-oscillators [18], planner ensemble of nanomagnets interacting in dipole coupled [26,27] and spin wave mediated [20,28] systems and artificial spin ice (ASI) [29]. Chiral magnetic textures such as domain walls (DWs) [21,30], skyrmions [22,31] and skyrmion lattice [19,32] are also proposed. Individually accessing the dense arrays of ASI and planar nanomagnets [26] with addressable magnetic tunnel junction (MTJ) remains a fabrication challenge with modern technology.…”

Section: Introductionmentioning

confidence: 99%

Spintronic Physical Reservoir for Autonomous Prediction and Long-Term Household Energy Load Forecasting

Misba,

Mavikumbure,

Rajib

et al. 2023

IEEE Access

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With the growing use of artificial neural networks (ANNs) in temporal data processing tasks, the cost of training for complex ANNs is an escalating concern. Physical reservoir computing (RC), a variation of recurrent neural networks (RNNs), obviate the need for most data intensive matrix vector multiplication in the recurrent layer by evolving the RC's internal states with the inherent nonlinear dynamics and short-term memory. In this study, we show that magnetic skyrmion confined in a fixed geometry forming the soft layer of a magnetic tunnel junction (MTJ) can work as a RC and perform autonomous long-term prediction of temporal data. Our proposed skyrmion reservoir allows for manipulation of spin dynamics with ultra energy efficient voltage controlled magnetic anisotropy modulation (VCMA) method. Furthermore, the boundary effect on the skyrmion from the geometric edges provides necessary consistency property of the reservoir. We employ our proposed reservoir for the modeling and prediction of the chaotic time series such as Mackey-Glass and dynamic time-series data, such as household building energy loads. For autonomous run, the predicted output is fed to the input of the reservoir. By comparing our spintronic physical RC approach with energy load forecasting algorithms, such as LSTMs and RNNs, we conclude that the proposed framework presents good performance in achieving high predictions accuracy, while also requiring low memory and energy both of which are at a premium in hardware resource and power constrained edge applications. Further, the proposed approach is shown to require very small training datasets and at the same time being at least 16× energy efficient compared to the sequence to sequence LSTM for accurate household load predictions. Higher endurance, fast processing and well-established technology (i.e., MTJ) to integrate with CMOS technology makes such spintronic reservoir attractive over other emergent memory device-based reservoirs.

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