From Charge to Spin and Spin to Charge: Stochastic Magnets for Probabilistic Switching

Debashis, Punyashloka; Ostwal, Vaibhav; Pervaiz, Ahmed Zeeshan; Shen, Tingting; Chen, Zhihong; Datta, Supriyo; Appenzeller, Joerg

doi:10.1109/jproc.2020.2966925

Cited by 26 publications

(16 citation statements)

References 96 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…New technologies, including probabilistic computing technologies, promise to considerably raise the standard on the amount of information that can be processed concurrently and, thus, are increasing the potential to identify suspicious communication on the internet (Camsari et al, 2017 ; Behin-Aein et al, 2016 ). While standard computers use stable magnets to hold their bits as stable ones or zeros, probabilistic computers replace the stable magnets with unstable magnets to allow their bits, known as p-bits, to fluctuate back and forth between ones and zeros (Camsari et al, 2020 ). This type of computing makes p-bits suitable for solving problems of probability, machine learning, and problems that have recently been addressed by quantum computing (Camsari et al, 2020 ).…”

mentioning

confidence: 99%

“…While standard computers use stable magnets to hold their bits as stable ones or zeros, probabilistic computers replace the stable magnets with unstable magnets to allow their bits, known as p-bits, to fluctuate back and forth between ones and zeros (Camsari et al, 2020 ). This type of computing makes p-bits suitable for solving problems of probability, machine learning, and problems that have recently been addressed by quantum computing (Camsari et al, 2020 ). Advancements and applications of probabilistic computing technologies in deciphering and cybersecurity promise to increase the probability of detecting attacks like the described October 31 st attack in advance and increase the chances for government agencies to thwart attempts and save lives.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Public support for counterterrorism efforts using probabilistic computing technologies to decipher terrorist communication on the internet

Johnson

2022

Curr Psychol

View full text Add to dashboard Cite

Advancements in big data analytics offer new avenues for the analysis and deciphering of suspicious activities on the internet. One promising new technology to increase the identification of terrorism threats is based on probabilistic computing. The technology promises to provide more efficient problem solutions in encryption and cybersecurity. Probabilistic computing technologies use large amounts of data, though, which raises potential privacy concerns. A study ( N = 1,023) was conducted to survey public support for using probabilistic computing technologies to increase counterterrorism efforts. Overall, strong support was found for the use of publicly available personal information (e.g., personal websites). Regarding private personal information (e.g., online conversations), respondents perceived it to be more appropriate to use information from out-group members (non-American citizens) than from in-group members (American citizens). In line with a social-identity account, this form of in-group favoritism was strongest among respondents displaying a combination of strong national identities and strong privacy concerns.

show abstract

mentioning

confidence: 99%

mentioning

confidence: 99%

Public support for counterterrorism efforts using probabilistic computing technologies to decipher terrorist communication on the internet

Johnson

2022

Curr Psychol

View full text Add to dashboard Cite

show abstract

“…Particularly, due to its tuneable energy barrier the magnetic tunnel junction (MTJ) is being used in solving problems in deterministic and stochastic neuromorphic computing [13,14]. In this work, we propose a MTJ-based device-circuit co-design to implement the spike time dependent plasticity (STDP) learning for pattern recognition applications [15]. The Hebbian learning algorithm implementation by the devices and the circuit is discussed.…”

Section: Introductionmentioning

confidence: 99%

Magnetic tunnel junction based implementation of spike time dependent plasticity learning for pattern recognition

Lone

Amara

Fariborzi

2022

Neuromorph. Comput. Eng.

View full text Add to dashboard Cite

We present a magnetic tunnel junction (MTJ) based implementation of the spike time-dependent (STDP) learning for pattern recognition applications. The proposed hybrid scheme utilizes the spin-orbit torque (SOT) driven neuromorphic device-circuit co-design to demonstrate the Hebbian learning algorithm. The circuit implementation involves the (MTJ) device structure, with the domain wall motion in the free layer, acting as an artificial synapse. The post-spiking neuron behaviour is implemented using a low barrier MTJ. In both synapse and neuron, the switching is driven by the spin-orbit torques generated by the spin Hall effect in the heavy metal. A coupled model for the spin transport and switching characteristics in both devices is developed by adopting a modular approach to spintronics. The thermal effects in the synapse and neuron result in a stochastic but tuneable domain wall motion in the synapse and a superparamagnetic behaviour of in neuron MTJ. Using the device model, we study the dimensional parameter dependence of the switching delay and current to optimize the device dimensions. The optimized parameters corresponding to synapse and neuron are considered for the implementation of the Hebbian learning algorithm. Furthermore, cross-point architecture and spike time-dependent plasticity-based weight modulation scheme is used to demonstrate the pattern recognition capabilities by the proposed neuromorphic circuit.

show abstract

“…where h i is the on-site bias and J ij is the weight of the coupling from j th p-bit to i th p-bit, I 0 parameterizes the coupling strength between p-bits, and τ S is the synpase evaluation time. Several hardware designs of p-bits based on low barrier nanomagnet (LBM) physics have been proposed and also experimentally demonstrated (Ostwal et al, 2018 ; Borders et al, 2019 ; Ostwal and Appenzeller, 2019 ; Camsari et al, 2020 ; Debashis, 2020 ). The thermal energy barrier of the LBM is of the order of a few k B T instead of 40–60 k B T used in the memory technology to retain stability.…”

Section: Introductionmentioning

confidence: 99%

Hardware Design for Autonomous Bayesian Networks

Faria

Kaiser

Datta

2021

Front. Comput. Neurosci.

Self Cite

View full text Add to dashboard Cite

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.

show abstract

From Charge to Spin and Spin to Charge: Stochastic Magnets for Probabilistic Switching

Cited by 26 publications

References 96 publications

Public support for counterterrorism efforts using probabilistic computing technologies to decipher terrorist communication on the internet

Public support for counterterrorism efforts using probabilistic computing technologies to decipher terrorist communication on the internet

Magnetic tunnel junction based implementation of spike time dependent plasticity learning for pattern recognition

Hardware Design for Autonomous Bayesian Networks

Contact Info

Product

Resources

About