Spin dice: A scalable truly random number generator based on spintronics

Fukushima, Akio; Seki, Takayuki; Yakushiji, Kay; Kubota, Hiroshi; Imamura, Hiroshi; Yuasa, Shinji; Ando, Koji

doi:10.7567/apex.7.083001

Cited by 193 publications

(92 citation statements)

References 24 publications

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“…While this stochastic switching is deleterious for applications such as memory cells, and necessitates the use of long programming pulses to ensure reliable switching [74], it can fortuitously be used to generate true random numbers, as their quality has already been verified with regards to true random number generator standards [75].…”

Section: A General Ideasmentioning

confidence: 99%

Bioinspired Programming of Memory Devices for Implementing an Inference Engine

Querlioz¹,

Bichler²,

Vincent³

et al. 2015

Proc. IEEE

121

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Emerging memory structures have several characteristics that are suitable for neuromorphic implementations. This paper connects the behavior of these ''new'' memory devices to achieving inference engines and provides a connection between the behavior of different devices and the learning algorithms.ABSTRACT | Cognitive tasks are essential for the modern applications of electronics, and rely on the capability to perform inference. The Von Neumann bottleneck is an important issue for such tasks, and emerging memory devices offer an opportunity to overcome this issue by fusing computing and memory, in nonvolatile instant ON/OFF systems. A vision for accomplishing this is to use brain-inspired architectures, which excel at inference and do not differentiate between computing and memory. In this work, we use a neuroscience-inspired model of learning, spike-timing-dependent plasticity, to develop a bioinspired approach for programming memory devices, which naturally gives rise to an inference engine. The method is then adapted to different memory devices, including multivalued memories (cumulative memristive device, phasechange memory) and stochastic binary memories (conductive bridge memory, spin transfer torque magnetic tunnel junction). By means of system-level simulations, we investigate several applications, including image recognition and pattern detection within video and auditory data. We compare the results of the different devices. Stochastic binary devices require the use of redundancy, the extent of which depends tremendously on the considered task. A theoretical analysis allows us to understand how the various devices differ, and ties the inference engine to the machine learning algorithm of expectation-maximization. Monte Carlo simulations demonstrate an exceptional robustness of the inference engine with respect to device variations and other issues. A theoretical analysis explains the roots of this robustness. These results highlight a possible new bioinspired paradigm for programming emerging memory devices, allowing the natural learning of a complex inference engine. The physics of the memory devices plays an active role. The results open the way for a reinvention of the role of memory, when solving inference tasks.

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Section: A General Ideasmentioning

confidence: 99%

Bioinspired Programming of Memory Devices for Implementing an Inference Engine

Querlioz¹,

Bichler²,

Vincent³

et al. 2015

Proc. IEEE

121

View full text Add to dashboard Cite

show abstract

“…If successive switching events do not follow each other too closely (with a frequency smaller than a few hundred megahertz), the switching probabilities are not correlated. By setting the switching probability close to 50%, spin-transfer torque has been used to generate true random numbers, using limited postprocessing [83]. …”

Section: Implementations Of Bioinspired Hardware Using Spintronicsmentioning

confidence: 99%

Spintronic Nanodevices for Bioinspired Computing

2016

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Bioinspired hardware holds the promise of low-energy, intelligent, and highly adaptable computing systems. Applications span from automatic classification for big data management, through unmanned vehicle control, to control for biomedical prosthesis. However, one of the major challenges of fabricating bioinspired hardware is building ultra-high-density networks out of complex processing units interlinked by tunable connections. Nanometer-scale devices exploiting spin electronics (or spintronics) can be a key technology in this context. In particular, magnetic tunnel junctions (MTJs) are well suited for this purpose because of their multiple tunable functionalities. One such functionality, non-volatile memory, can provide massive embedded memory in unconventional circuits, thus escaping the von-Neumann bottleneck arising when memory and processors are located separately. Other features of spintronic devices that could be beneficial for bioinspired computing include tunable fast nonlinear dynamics, controlled stochasticity, and the ability of single devices to change functions in different operating conditions. Large networks of interacting spintronic nanodevices can have their interactions tuned to induce complex dynamics such as synchronization, chaos, soliton diffusion, phase transitions, criticality, and convergence to multiple metastable states. A number of groups have recently proposed bioinspired architectures that include one or several types of spintronic nanodevices. In this paper, we show how spintronics can be used for bioinspired computing. We review the different approaches that have been proposed, the recent advances in this direction, and the challenges toward fully integrated spintronics complementary metal–oxide–semiconductor (CMOS) bioinspired hardware.

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“…This approach was investigated with oxide-based resistive memory devices [15][16][17][18] , phasechange memory devices 19,20 , magnetic memory devices [21][22][23] , as well as with straintronic memory devices 24 . However, these approaches are based on repeated, energy-intensive programming operations, and still require high energy for random bit generation.…”

Section: Introductionmentioning

confidence: 99%

Low-Energy Truly Random Number Generation with Superparamagnetic Tunnel Junctions for Unconventional Computing

et al. 2017

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Low-energy random number generation is critical for many emerging computing schemes proposed to complement or replace von Neumann architectures. However, current random number generators are always associated with an energy cost that is prohibitive for these computing schemes. In this paper, we introduce random number bit generation based on specific nanodevices: superparamagnetic tunnel junctions. We experimentally demonstrate high quality random bit generation that represents orders-of-magnitude improvements in energy efficiency compared to current solutions. We show that the random generation speed improves with nanodevice scaling, and investigate the impact of temperature, magnetic field and crosstalk. Finally, we show how alternative computing schemes can be implemented using superparamagentic tunnel junctions as random number generators. These results open the way for fabricating efficient hardware computing devices leveraging stochasticity, and highlight a novel use for emerging nanodevices.

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Spin dice: A scalable truly random number generator based on spintronics

Cited by 193 publications

References 24 publications

Bioinspired Programming of Memory Devices for Implementing an Inference Engine

Bioinspired Programming of Memory Devices for Implementing an Inference Engine

Spintronic Nanodevices for Bioinspired Computing

Low-Energy Truly Random Number Generation with Superparamagnetic Tunnel Junctions for Unconventional Computing

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