Stochastic computation with Spin Torque Transfer Magnetic Tunnel Junction

Naviner, Lírida Alves de Barros; Cai, Hao; Wang, You; Zhao, Weisheng; Dhia, Arwa Ben

doi:10.1109/newcas.2015.7182031

Cited by 18 publications

(14 citation statements)

References 24 publications

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“…Sun's model, the equation conventionally used to describe the high-current regime, actually diverges in this intermediate current regime. For this reason, the current models usually ignore the intermediate regime [8]- [10], although it is, particularly, relevant for applications [16].…”

Section: Introductionmentioning

confidence: 99%

Analytical Macrospin Modeling of the Stochastic Switching Time of Spin-Transfer Torque Devices

Vincent

Locatelli

Klein

et al. 2015

IEEE Trans. Electron Devices

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Owing to their nonvolatility, outstanding endurance, high write and read speeds, and CMOS process compatibility, spin-transfer torque magnetoresistive memories (MRAMs) are prime candidates for innovative memory applications. However, the switching delay of their core components-the magnetic tunnel junctions (MTJs)-is a stochastic quantity. To account for this in electronic design, only partial models (working in extreme regimes) are available. In this paper, we propose an analytical model for the stochastic switching delay of a current-driven MTJ, with in-plane magnetization, that agrees with physical simulations, from low-to high-current regimes through intermediate regime. We performed physical macrospin simulations of MTJs for a wide range of current. We developed an analytical model for the mean switching delay that fits those simulations results, and smoothly connects well-accepted models for the extreme low and extreme high currents limits. In addition, a probability distribution in agreement with our simulations results is proposed, leading to a full model of the stochastic switching delay. An example for the application of the model is proposed. Our analytical model can help to evaluate the error rate in MRAM designs, and allow designing innovative electronic circuits that exploit the intrinsic stochastic behavior of MTJs as a beneficial feature.

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Section: Introductionmentioning

confidence: 99%

Analytical Macrospin Modeling of the Stochastic Switching Time of Spin-Transfer Torque Devices

Vincent

Locatelli

Klein

et al. 2015

IEEE Trans. Electron Devices

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“…Significant efforts have been made to provide compact models of STT-MTJs [4]- [12]. Some compact models [5], [6], [11], [12] directly solve the exact magnetic equation of the dynamic of the MTJ. Unfortunately, this approach is, particularly, time consuming compared with other analytical models and does not suit simulations with a large number of devices.…”

mentioning

confidence: 99%

SPINBIS: Spintronics-Based Bayesian Inference System With Stochastic Computing

Jia

Yang

Dai

et al. 2020

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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Bayesian inference is an effective approach for solving statistical learning problems, especially with uncertainty and incompleteness. However, Bayesian inference is a computingintensive task whose efficiency is physically limited by the bottlenecks of conventional computing platforms. In this work, a spintronics based stochastic computing approach is proposed for efficient Bayesian inference. The inherent stochastic switching behaviors of spintronic devices are exploited to build stochastic bitstream generator (SBG) for stochastic computing with hybrid CMOS/MTJ circuits design. Aiming to improve the inference efficiency, an SBG sharing strategy is leveraged to reduce the required SBG array scale by integrating a switch network between SBG array and stochastic computing logic. A deviceto-architecture level framework is proposed to evaluate the performance of spintronics based Bayesian inference system (SPINBIS). Experimental results on data fusion applications have shown that SPINBIS could improve the energy efficiency about 12× than MTJ-based approach with 45% design area overhead and about 26× than FPGA-based approach.

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“…Voltage-controlled MTJs are claimed to be more energy efficient than conventional MTJs [89] and others have indicted the scope to use switching mechanisms such as STT to further enhance the scope and energy benefits of taking this approach [90]. Stochastic computing-based logic operations offer a number of potential advantages, including not just the evident speed and energy advantages of in-memory computing but also improved accuracy and fault tolerance [91].…”

Section: Potential For Logic Operationsmentioning

confidence: 99%

“…The feasibility of achieving a full set of 1-and 2-input Boolean logic functions (such as NOT, AND, and OR) based on such a device was demonstrated by configuring the applied keys [97]. Some recent work has explored the potential use of MTJs for stochastic computing [89][90][91]. Zhang et al [89], for instance, have explored the use of voltage-controlled MTJs as both stochastic number generators and in-memory logic devices, with changes in voltage and current controlling the operating mode and the assignation of different Boolean logic inputs [89], as demonstrated in Figure 7c.…”

Section: Potential For Logic Operationsmentioning

confidence: 99%

“…Some recent work has explored the potential use of MTJs for stochastic computing [ 89 , 90 , 91 ]. Zhang et al [ 89 ], for instance, have explored the use of voltage-controlled MTJs as both stochastic number generators and in-memory logic devices, with changes in voltage and current controlling the operating mode and the assignation of different Boolean logic inputs [ 89 ], as demonstrated in Figure 7 c. Lv and Wang [ 90 ] have shown how the physical properties of a single MTJ, such as pulse amplitude, bias field, bias current, pulse width and switching probability, can be used for a variety of logic operations.…”

Section: Logic In Magnetic Rammentioning

confidence: 99%

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In-Memory Logic Operations and Neuromorphic Computing in Non-Volatile Random Access Memory

Xiong

et al. 2020

Materials

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Recent progress in the development of artificial intelligence technologies, aided by deep learning algorithms, has led to an unprecedented revolution in neuromorphic circuits, bringing us ever closer to brain-like computers. However, the vast majority of advanced algorithms still have to run on conventional computers. Thus, their capacities are limited by what is known as the von-Neumann bottleneck, where the central processing unit for data computation and the main memory for data storage are separated. Emerging forms of non-volatile random access memory, such as ferroelectric random access memory, phase-change random access memory, magnetic random access memory, and resistive random access memory, are widely considered to offer the best prospect of circumventing the von-Neumann bottleneck. This is due to their ability to merge storage and computational operations, such as Boolean logic. This paper reviews the most common kinds of non-volatile random access memory and their physical principles, together with their relative pros and cons when compared with conventional CMOS-based circuits (Complementary Metal Oxide Semiconductor). Their potential application to Boolean logic computation is then considered in terms of their working mechanism, circuit design and performance metrics. The paper concludes by envisaging the prospects offered by non-volatile devices for future brain-inspired and neuromorphic computation.

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Stochastic computation with Spin Torque Transfer Magnetic Tunnel Junction

Cited by 18 publications

References 24 publications

Analytical Macrospin Modeling of the Stochastic Switching Time of Spin-Transfer Torque Devices

Analytical Macrospin Modeling of the Stochastic Switching Time of Spin-Transfer Torque Devices

SPINBIS: Spintronics-Based Bayesian Inference System With Stochastic Computing

In-Memory Logic Operations and Neuromorphic Computing in Non-Volatile Random Access Memory

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