Intrinsic optimization using stochastic nanomagnets

Sutton, Brian M.; Çamsarı, Kerem Y.; Behin‐Aein, Behtash; Datta, Supriyo

doi:10.1038/srep44370

Cited by 204 publications

(149 citation statements)

References 64 publications

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“…This implies that EPM and AFM nanomagnets can be utilized to increase the operating speeds of applications that rely on large amounts of random numbers. This includes probabilistic computing, stochastic optimization, statistical sampling, cryptography and machine learning [12,13,15,[32][33][34].…”

Section: Discussionmentioning

confidence: 99%

“…Consequently, both fundamental and technological studies have primarily focused on the regime when the energy barrier between the stable states of the magnet is much larger than the thermal energy, referred to as the nonvolatile regime. More recently, it has been realized that the order-parameter dynamics even in the other extreme, namely the low-barrier volatile regime, can be utilized to engender useful technological functionality, including true random-number generation [11], probabilistic computing [12][13][14], optimization [15], machine learning [16] and quantum emulation [17].…”

Section: Introductionmentioning

confidence: 99%

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Subnanosecond Fluctuations in Low-Barrier Nanomagnets

et al. 2019

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Fast magnetic fluctuations due to thermal torques have useful technological functionality ranging from cryptography to probabilistic computing. The characteristic time of fluctuations in typical uniaxial anisotropy magnets studied so far is bounded from below by the well-known energy relaxation mechanism. This time scales as α −1 , where α parameterizes the strength of dissipative processes. Here, we theoretically analyze the fluctuating dynamics in easy-plane and antiferromagnetically coupled nanomagnets. We find in such magnets, the dynamics are strongly influenced by fluctuating intrinsic fields, which give rise to an additional dephasing-type mechanism for washing out correlations. In particular, we establish two time scales for characterizing fluctuations (i) the average time for a nanomagnet to reverse-which for the experimentally relevant regime of low damping is governed primarily by dephasing and becomes independent of α, (ii) the time scale for memory loss of a single nanomagnet-which scales as α −1/3 and is governed by a combination of energy dissipation and dephasing mechanism. For typical experimentally accessible values of intrinsic fields, the resultant thermal-fluctuation rate is increased by multiple orders of magnitude when compared with the bound set solely by the energy relaxation mechanism in uniaxial magnets. This could lead to higher operating speeds of emerging devices exploiting magnetic fluctuations.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Subnanosecond Fluctuations in Low-Barrier Nanomagnets

et al. 2019

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“…Probabilistic computing with p-bits encoded in the magnetization states of low energy barrier nanomagnets (LBMs) is extremely energy-efficient and far more error-resilient than energy-efficient Boolean computing with nanomagnets, which is normally very errorprone [2]. Computing with p-bits has also been shown to excel in certain tasks such as combinatorial optimization [3], invertible logic [4] and integer factorization [5].…”

Section: Introductionmentioning

confidence: 99%

Reliability and Scalability of p-Bits Implemented With Low Energy Barrier Nanomagnets

Drobitch

Bandyopadhyay

2019

IEEE Magn. Lett.

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Probabilistic (p-) bits implemented with low energy barrier nanomagnets (LBMs) have recently gained attention because they can be leveraged to perform some computational tasks very efficiently. Although more error-resilient than Boolean computing, p-bit based computing employing LBMs is, however, not completely immune to defects and device-todevice variations. In some tasks (e.g. binary stochastic neurons for machine learning and p-bits for population coding), extended defects, such as variation of the LBM thickness over a significant fraction of the surface, can impair functionality. In this paper, we have examined if unavoidable geometric device-to-device variations can have a significant effect on one of the most critical requirements for probabilistic computing, namely the ability to "program" probability with an external agent, such as a spin-polarized current injected into the LBM. We found that the programming ability is fortunately not lost due to reasonable device-to-device variations. The little variation in the probability versus current characteristic that reasonable device variability causes can be suppressed further by increasing the spin polarization of the current. This shows that probabilistic computing with LBMs is robust against small geometric variations, and hence will be "scalable" to a large number of p-bits.

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“…They are implemented with low energy barrier nanomagnets (LBMs) where the energy barrier separating the two stable magnetization states is purposely made small enough that thermal noise can make the magnetization fluctuate randomly with time. This random magnetization distribution is utilized for computation [24,27,28]. One way to realize LBMs is to fashion them out of nearly circular ultrathin disks of small cross-section, resulting in low shape anisotropy energy barrier on the order of the thermal energy kT.…”

Section: Introductionmentioning

confidence: 99%

Low Energy Barrier Nanomagnet Design for Binary Stochastic Neurons: Design Challenges for Real Nanomagnets With Fabrication Defects

Abeed

Bandyopadhyay

2019

IEEE Magn. Lett.

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Much attention has been focused on the design of low barrier nanomagnets (LBM), whose magnetizations vary randomly in time owing to thermal noise, for use in binary stochastic neurons (BSN) which are hardware accelerators for machine learning. The performance of BSNs depend on two important parameters: the correlation time c associated with the random magnetization dynamics in a LBM, and the spin-polarized pinning current Ip which stabilizes the magnetization of a LBM in a chosen direction within a chosen time. Here, we show that common fabrication defects in LBMs make these two parameters unpredictable since they are strongly sensitive to the defects. That makes the design of BSNs with real LBMs very challenging. Unless the LBMs are fabricated with extremely tight control, the BSNs which use them could be unreliable or suffer from poor yield.Index Terms-Low barrier magnets, binary stochastic neurons, correlation time, pinning currents, effect of defects.

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Intrinsic optimization using stochastic nanomagnets

Cited by 204 publications

References 64 publications

Subnanosecond Fluctuations in Low-Barrier Nanomagnets

Subnanosecond Fluctuations in Low-Barrier Nanomagnets

Reliability and Scalability of p-Bits Implemented With Low Energy Barrier Nanomagnets

Low Energy Barrier Nanomagnet Design for Binary Stochastic Neurons: Design Challenges for Real Nanomagnets With Fabrication Defects

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