The Strong Sensitivity of the Characteristics of Binary Stochastic Neurons Employing Low Barrier Nanomagnets to Small Geometrical Variations

Rahman, Rahnuma; Bandyopadhyay, Supriyo

doi:10.1109/tnano.2023.3244139

Cited by 4 publications

(2 citation statements)

References 23 publications

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“…Deviation from the designed shape of a LBM due to lack of precision in the fabrication method can lead to significant variations in the natural barrier heights in LBM networks. This, in turn, can introduce large variability in the correlation times and pinning currents of LBMs used as BSNs, resulting in large variability in computational outputs of BSN networks [24]. Furthermore, it has been shown that in a network with large barrier height variability, significant error in computational results can be expected unless the compute process is exponentially long on the scale of the barrier height variability spread in the network [25].…”

Section: Control Over Device-to-device Variability and Data Retention...mentioning

confidence: 99%

Reconfigurable stochastic neurons based on strain engineered low barrier nanomagnets

Rahman,

Ganguly,

Bandyopadhyay

2024

Nanotechnology

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Stochastic neurons are efficient hardware accelerators for solving a large variety of combinatorial optimization problems. “Binary” stochastic neurons (BSN) are those whose states fluctuate randomly between two levels +1 and -1, with the probability of being in either level determined by an external bias. “Analog” stochastic neurons (ASNs), in contrast, can assume any state between the two levels randomly (hence “analog”) and can perform analog signal processing. They may be leveraged for such tasks as temporal sequence learning, processing and prediction. Both BSNs and ASNs can be used to build efficient and scalable neural networks. Both can be implemented with low (potential energy) barrier nanomagnets (LBMs) whose random magnetization orientations encode the binary or analog state variables. The difference between them is that the potential energy barrier in a BSN LBM, albeit low, is much higher than that in an ASN LBM. As a result, a BSN LBM has a clear double well potential profile, which makes its magnetization orientation assume one of two orientations at any time, resulting in the binary behavior. ASN nanomagnets, on the other hand, hardly have any energy barrier at all and hence lack the double well feature. That makes their magnetizations fluctuate in an analog fashion. Hence, one can reconfigure an ASN to a BSN, and vice-versa, by simply raising and lowering the energy barrier. If the LBM is magnetostrictive, then this can be done with local (electrically generated) strain. Such a reconfiguration capability heralds a powerful field programmable architecture for a p-computer whereby hardware for very different functionalities such as combinatorial optimization and temporal sequence learning can be integrated in the same substrate in the same processing run. This is somewhat reminiscent of heterogeneous integration, except this is integration of functionalities or computational fabrics rather than components. The energy cost of reconfiguration is miniscule. There are also other applications of strain mediated barrier control that do not involve reconfiguring a BSN to an ASN or vice versa, e.g., adaptive annealing in energy minimization computing (Boltzmann or Ising machines), emulating memory hierarchy in a dynamically reconfigurable fashion, and control over belief uncertainty in analog stochastic neurons. Here, we present a study of strain engineered barrier control in unconventional computing.

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Section: Control Over Device-to-device Variability and Data Retention...mentioning

confidence: 99%

Reconfigurable stochastic neurons based on strain engineered low barrier nanomagnets

Rahman,

Ganguly,

Bandyopadhyay

2024

Nanotechnology

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“…On the other hand, using nanodevices such as CMOScompatible stochastic magnetic tunnel junctions (sMTJ), millions of p-bits can be accommodated in single cores due to the scalability achieved by the MRAM technology, exceeding 1Gbit MRAM chips [56,57]. However, before the stable MTJs can be controllably made stochastic, challenges at the material and device level must be addressed [58,59] with careful magnet design [60][61][62]. Different flavors of magnetic p-bits exist [63][64][65][66], for a recent review, see Ref.…”

Section: Hardware: Physical Implementation Of P-bits a P-bitmentioning

confidence: 99%

A Full-Stack View of Probabilistic Computing With p-Bits: Devices, Architectures, and Algorithms

Chowdhury

Grimaldi

Aadit

et al. 2023

IEEE J. Explor. Solid-State Comput. Devices Circuits

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The transistor celebrated its 75 th birthday in 2022. The continued scaling of the transistor defined by Moore's Law continues, albeit at a slower pace. Meanwhile, computing demands and energy consumption required by modern artificial intelligence (AI) algorithms have skyrocketed. As an alternative to scaling transistors for general-purpose computing, the integration of transistors with unconventional technologies has emerged as a promising path for domain-specific computing. In this article, we provide a full-stack review of probabilistic computing with p-bits as a representative example of the energy-efficient and domain-specific computing movement. We argue that p-bits could be used to build energy-efficient probabilistic systems, tailored for probabilistic algorithms and applications. From hardware, architecture, and algorithmic perspectives, we outline the main applications of probabilistic computers ranging from probabilistic machine learning and AI to combinatorial optimization and quantum simulation. Combining emerging nanodevices with the existing CMOS ecosystem will lead to probabilistic computers with orders of magnitude improvements in energy efficiency and probabilistic sampling, potentially unlocking previously unexplored regimes for powerful probabilistic algorithms.

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Increasing Flips per Second and Speed of p-Computers by Using Dilute Magnetic Semiconductors

Rahman,

Bandyopadhyay

2023

IEEE Magn. Lett.

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The Strong Sensitivity of the Characteristics of Binary Stochastic Neurons Employing Low Barrier Nanomagnets to Small Geometrical Variations

Cited by 4 publications

References 23 publications

Reconfigurable stochastic neurons based on strain engineered low barrier nanomagnets

Reconfigurable stochastic neurons based on strain engineered low barrier nanomagnets

A Full-Stack View of Probabilistic Computing With p-Bits: Devices, Architectures, and Algorithms

Increasing Flips per Second and Speed of p-Computers by Using Dilute Magnetic Semiconductors

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