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

Abeed, Md. Ahsanul; Bandyopadhyay, Supriyo

doi:10.1109/lmag.2019.2929484

Cited by 23 publications

(10 citation statements)

References 34 publications

(41 reference statements)

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“…In the past, it was found that in magnetostrictive nanomagnets, strain-induced magnetization reversal (switching) probability is dramatically affected by the presence of defects 29,30 . Defects are also known to have a serious deleterious effect on the stochastic behavior of low energy barrier nanomagnets that have been proposed for use in stochastic computing 31 . Here, we have found that defects have a dramatic effect on spin wave modes as well.…”

Section: Resultsmentioning

confidence: 99%

The effect of material defects on resonant spin wave modes in a nanomagnet

Abeed

Sahoo

Winters

et al. 2019

Sci Rep

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We have theoretically studied how resonant spin wave modes in an elliptical nanomagnet are affected by fabrication defects, such as small local thickness variations. Our results indicate that defects of this nature, which can easily result from the fabrication process, or are sometimes deliberately introduced during the fabrication process, will significantly alter the frequencies, magnetic field dependence of the frequencies, and the power and phase profiles of the resonant spin wave modes. They can also spawn new resonant modes and quench existing ones. All this has important ramifications for multi-device circuits based on spin waves, such as phase locked oscillators for neuromorphic computing, where the device-to-device variability caused by defects can be inhibitory.

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Section: Resultsmentioning

confidence: 99%

The effect of material defects on resonant spin wave modes in a nanomagnet

Abeed

Sahoo

Winters

et al. 2019

Sci Rep

Self Cite

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“…All the non-ideal effects listed above are supposed to have minimal effects on different probability distributions shown in this article. Real LBMs may suffer from common fabrication defects, resulting in variations in average magnet fluctuation time τ N (Abeed and Bandyopadhyay, 2019 ). The autonomous BN is also quite tolerant to such variations in τ N as long as τ T ≪ min (τ N ).…”

Section: Discussionmentioning

confidence: 99%

“…It is important to note that, for design 1 (Transistor-controlled) to function as a p-bit that has a step response time (τ step ) much smaller than its average fluctuation time (τ N ), the LBM fluctuation needs to be continuous and not bipolar. It is important to note that while most experimental implementations of low barrier magnetic tunnel junctions or spin-valves exhibit telegraphic (binary) fluctuations (Pufall et al, 2004 ; Locatelli et al, 2014 ; Parks et al, 2018 ; Debashis et al, 2020 ), theoretical results (Abeed and Bandyopadhyay, 2019 ; Hassan et al, 2019 ; Kaiser et al, 2019 ) indicate that it should be possible to design low barrier magnets with continuous fluctuations. Preliminary experimental results for such circular disk nanomagnets have been presented in Debashis et al ( 2016 ).…”

Section: Discussionmentioning

confidence: 99%

Hardware Design for Autonomous Bayesian Networks

Faria

Kaiser

Datta

2021

Front. Comput. Neurosci.

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Directed acyclic graphs or Bayesian networks that are popular in many AI-related sectors for probabilistic inference and causal reasoning can be mapped to probabilistic circuits built out of probabilistic bits (p-bits), analogous to binary stochastic neurons of stochastic artificial neural networks. In order to satisfy standard statistical results, individual p-bits not only need to be updated sequentially but also in order from the parent to the child nodes, necessitating the use of sequencers in software implementations. In this article, we first use SPICE simulations to show that an autonomous hardware Bayesian network can operate correctly without any clocks or sequencers, but only if the individual p-bits are appropriately designed. We then present a simple behavioral model of the autonomous hardware illustrating the essential characteristics needed for correct sequencer-free operation. This model is also benchmarked against SPICE simulations and can be used to simulate large-scale networks. Our results could be useful in the design of hardware accelerators that use energy-efficient building blocks suited for low-level implementations of Bayesian networks. The autonomous massively parallel operation of our proposed stochastic hardware has biological relevance since neural dynamics in brain is also stochastic and autonomous by nature.

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“…Binary stochastic neurons (BSNs) are well suited to function as a 'spin' in Ising machines for solving combinatorial optimization problems 10,32 . A compact and efficient hardware realization of the BSN leveraging the natural physics of stochastic nanomagnets can be made by using unstable magnetic tunnel junctions (MTJs) [33][34][35][36][37] as shown in Fig. 1.…”

Section: General Approach To Design Of Bsnmentioning

confidence: 99%

Quantitative Evaluation of Hardware Binary Stochastic Neurons

Hassan

Datta

2021

Phys. Rev. Applied

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Recently there has been increasing activity to build dedicated Ising Machines to accelerate the solution of combinatorial optimization problems by expressing these problems as a ground-state search of the Ising model. A common theme of such Ising Machines is to tailor the physics of underlying hardware to the mathematics of the Ising model to improve some aspect of performance that is measured in speed to solution, energy consumption per solution or area footprint of the adopted hardware. One such approach to build an Ising spin, or a binary stochastic neuron (BSN), is a compact mixed-signal unit based on a low-barrier nanomagnet based design that uses a single magnetic tunnel junction (MTJ) and three transistors (3T-1MTJ) where the MTJ functions as a stochastic resistor (1SR). Such a compact unit can drastically reduce the area footprint of BSNs while promising massive scalability by leveraging the existing Magnetic RAM (MRAM) technology that has integrated 1T-1MTJ cells in ∼ Gbit densities. The 3T-1SR design however can be realized using different materials or devices that provide naturally fluctuating resistances. Extending previous work, we evaluate hardware BSNs from this general perspective by classifying necessary and sufficient conditions to design a fast and energy-efficient BSN that can be used in scaled Ising Machine implementations. We connect our device analysis to systems-level metrics by emphasizing hardware-independent figures-of-merit such as flips per second and dissipated energy per random bit that can be used to classify any Ising Machine.

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

Cited by 23 publications

References 34 publications

The effect of material defects on resonant spin wave modes in a nanomagnet

The effect of material defects on resonant spin wave modes in a nanomagnet

Hardware Design for Autonomous Bayesian Networks

Quantitative Evaluation of Hardware Binary Stochastic Neurons

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