Telegraphic switching signals by magnet tunnel junctions for neural spiking signals with high information capacity

Zink, Brandon R.; Lv, Yang; Wang, Jianping

doi:10.1063/1.5042444

Cited by 28 publications

(7 citation statements)

References 20 publications

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“…Efforts have been put into developing a suitable hardware for accelerating evaluation of this function, many of which are OH based on magnetoresistive random access memory (MRAM) technology which is a major contender in the field of nonvolatile memory using stable magnets to store information in the form of 0's and 1's. By contrast, BSN's can be built out of nanomagnets designed to have low energy barriers [11]- [18]. The performance of such BSN designs are largely dependent on the magnetization fluctuation rates of the LBM's, making it important to design the low barrier magnet to have a high fluctuation rate.…”

Section: Introductionmentioning

confidence: 99%

Low-Barrier Magnet Design for Efficient Hardware Binary Stochastic Neurons

et al. 2019

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Binary stochastic neurons (BSN's) form an integral part of many machine learning algorithms, motivating the development of hardware accelerators for this complex function. It has been recognized that hardware BSN's can be implemented using low barrier magnets (LBM's) by minimally modifying presentday magnetoresistive random access memory (MRAM) devices. A crucial parameter that determines the response of these LBM based BSN designs is the correlation time of magnetization, τc. In this letter, we show that for magnets with low energy barriers (∆ ≈ kBT and below), circular disk magnets with inplane magnetic anisotropy (IMA) lead to τc values that are two orders of magnitude smaller compared to τc for magnets having perpendicular magnetic anisotropy (PMA) and provide analytical descriptions. We show that this striking difference in τc is due to a precession-like fluctuation mechanism that is enabled by the large demagnetization field in IMA magnets. We provide a detailed energy-delay performance evaluation of previously proposed BSN designs based on Spin-Orbit-Torque (SOT) MRAM and Spin-Transfer-Torque (STT) MRAM employing low barrier circular IMA magnets by SPICE simulations. The designs exhibit sub-ns response times leading to energy requirements of ∼a few fJ to evaluate the BSN function, orders of magnitude lower than digital CMOS implementations with a much larger footprint. While modern MRAM technology is based on PMA magnets, results in this paper suggest that low barrier circular IMA magnets may be more suitable for this application.Index Terms-Binary stochastic neuron, hardware implementation, low barrier magnet, embedded MTJ, probabilistic computing

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Section: Introductionmentioning

confidence: 99%

Low-Barrier Magnet Design for Efficient Hardware Binary Stochastic Neurons

et al. 2019

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“…Hence, the magnitude of the external magnetic field and "write" current are independent control knobs that can be used to modulate the device lifetimes, τ P and τ AP , and hence the precise temporal code. Recent experiments on a CoFeB MTJ stack have demonstrated that such a control is indeed possible for a range of external fields and currents 23 . However, using an external tunable magnetic field to bias MTJ spiking neurons is not feasible from the scalability perspective for neuromorphic computing applications.…”

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confidence: 99%

Stochastic magnetoelectric neuron for temporal information encoding

Yang

Sengupta

2020

Applied Physics Letters

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Emulating various facets of computing principles of the brain can potentially lead to the development of neurocomputers that are able to exhibit brain-like cognitive capabilities. In this letter, we propose a magnetoelectronic neuron that utilizes noise as a computing resource and is able to encode information over time through the independent control of external voltage signals. We extensively characterize the device operation using simulations and demonstrate its suitability for neuromorphic computing platforms performing temporal information encoding.

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“…One is that it can be used to generate stochastic switching signals in thermally stable MTJs as well as sMTJs [79]. Second is that the average switching rate of the signals generated can be tuned by over four orders of magnitude and reach switching rates above 1 MHz [79], [80], [81]. The third, and most unique, feature of dual biasing is that the two biases can control the AP-and P-state dwell times separately, which we refer to as two degrees of tunability, and implies that the average output and average switching rate can be tuned independently.…”

Section: Dual-biased Mtjs For Asynchronous Methodsmentioning

confidence: 99%

Review of Magnetic Tunnel Junctions for Stochastic Computing

Zink

Wang

2022

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

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Modern computing schemes require large circuit areas and large energy consumption for neuromorphic computing applications, such as recognition, classification, and prediction. This is because these tasks require parallel processing on large datasets. Stochastic computing (SC) is a promising alternative to conventional binary computing schemes due to its low area cost, low processing power, and robustness to noise. However, the large area and energy costs for random number generation with CMOS-based circuits make SC impractical for most hardware implementations. For this reason, beyond-CMOS approaches to random number generation have been investigated in recent years. Spintronics is one of the most promising approaches due to the intrinsic stochasticity of the magnetic tunnel junction (MTJ). In this review article, we provide an overview of the literature published in recent years investigating the tunable, intrinsic stochasticity of MTJs and proposing practical methods for random number generation using spintronic hardware.INDEX TERMS Magnetic tunnel junctions (MTJs), random number generators (RNGs), spintronic devices, stochastic-bit generators (SBGs), stochastic computing (SC).

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Telegraphic switching signals by magnet tunnel junctions for neural spiking signals with high information capacity

Cited by 28 publications

References 20 publications

Low-Barrier Magnet Design for Efficient Hardware Binary Stochastic Neurons

Low-Barrier Magnet Design for Efficient Hardware Binary Stochastic Neurons

Stochastic magnetoelectric neuron for temporal information encoding

Review of Magnetic Tunnel Junctions for Stochastic Computing

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