An Overview of Spintronic True Random Number Generator

Fu, Zhenxiao; Tang, Yi; Zhao, Xiaohu; Lü, Kai; Dong, Yong; Shukla, Aditya; Zhu, Zhifeng; Yang, Yumeng

doi:10.3389/fphy.2021.638207

Cited by 15 publications

(2 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Harabi's prototype system demonstrated the potential for orders of magnitude improvements in energy efficiency compared to microprocessors. However, further optimizations in power could be made by using emerging devices like SOT-MRAMs for true random number generation rather than the LFSRs [53]. The binary architecture may also limit model accuracy compared to systems storing probabilities using multi-level memristors.…”

Section: Simulation Of Chaotic Spiking In a Memristive Nanowire That ...mentioning

confidence: 99%

Neuromorphic Computing with Resistive Memory and Bayesian Machines

Frick

2023

Memristors - The Fourth Fundamental Circuit Element - Theory, Device, and Applications

View full text Add to dashboard Cite

Bio-inspired computing with memristors and neuristors offers promising pathways to energy-efficient intelligence. This work reviews toolkits for implementing spiking neural networks and Bayesian machine learning directly in hardware using these emerging devices. We first demonstrate that normally passive memristors can exhibit neuristor-like oscillatory behavior when heating and cooling is taken into account. Such oscillations enable spike-based neural computing. We then summarize recent works on leveraging intrinsic switching stochasticity in memristive devices to physically embed Bayesian models and perform in-situ probabilistic inference. While still facing challenges in endurance, variation tolerance, and peripheral circuitry, this co-design approach combining tailored algorithms and nanodevices could enable a new class of ultra-low power brain-inspired intelligence tolerant to uncertainty and capable to learn with small datasets. Longer-term, hybrid CMOS-memristor systems with sensing/actuation may provide fully adaptive Bayesian edge intelligence. Overall, the confluence of probabilistic algorithms and memristive hardware holds promise for future electronics combining efficiency, adaptability, and human-like reasoning. Academic innovations exploring this algorithm-hardware co-design can lay the foundation for this emerging paradigm of probabilistic cognitive computing.

show abstract

Section: Simulation Of Chaotic Spiking In a Memristive Nanowire That ...mentioning

confidence: 99%

Neuromorphic Computing with Resistive Memory and Bayesian Machines

Frick

2023

Memristors - The Fourth Fundamental Circuit Element - Theory, Device, and Applications

View full text Add to dashboard Cite

show abstract

“…Second, we evaluate the quality of randomness directly at the application level through probabilistic inference and deep Boltzmann learning. This approach contrasts with the majority of related work, which typically conducts statistical tests at the single device level to evaluate the quality of randomness 21 , 34 – 38 (see Supplementary Notes VIII , XI , and XII for more randomness experiments). As an important new result, we find that the quality of randomness matters in machine learning tasks as opposed to optimization tasks that have been explored previously.…”

Section: Introductionmentioning

confidence: 99%

CMOS plus stochastic nanomagnets enabling heterogeneous computers for probabilistic inference and learning

Singh,

Kobayashi,

Cao

et al. 2024

Nat Commun

View full text Add to dashboard Cite

Extending Moore’s law by augmenting complementary-metal-oxide semiconductor (CMOS) transistors with emerging nanotechnologies (X) has become increasingly important. One important class of problems involve sampling-based Monte Carlo algorithms used in probabilistic machine learning, optimization, and quantum simulation. Here, we combine stochastic magnetic tunnel junction (sMTJ)-based probabilistic bits (p-bits) with Field Programmable Gate Arrays (FPGA) to create an energy-efficient CMOS + X (X = sMTJ) prototype. This setup shows how asynchronously driven CMOS circuits controlled by sMTJs can perform probabilistic inference and learning by leveraging the algorithmic update-order-invariance of Gibbs sampling. We show how the stochasticity of sMTJs can augment low-quality random number generators (RNG). Detailed transistor-level comparisons reveal that sMTJ-based p-bits can replace up to 10,000 CMOS transistors while dissipating two orders of magnitude less energy. Integrated versions of our approach can advance probabilistic computing involving deep Boltzmann machines and other energy-based learning algorithms with extremely high throughput and energy efficiency.

show abstract

Ferroelectric Stochasticity in 2D CuInP₂S₆ and Its Application for True Random Number Generator

Kang,

Hong,

Das

et al. 2024

Advanced Materials

View full text Add to dashboard Cite

True random number generators (TRNGs), which create cryptographically secure random bitstreams, hold great promise in addressing security concerns regarding hardware, communication, and authentication in the Internet of Things (IoT) realm. Recently, TRNGs based on nanoscale materials have gained considerable attention for avoiding conventional and predictable hardware circuitry designs that can be vulnerable to machine learning (ML) attacks. In this article, a low‐power and low‐cost TRNG developed by exploiting stochastic ferroelectric polarization switching in 2D ferroelectric CuInP2S6 (CIPS)‐based capacitive structures, is reported. The stochasticity arises from the probabilistic switching of independent electrical dipoles. The TRNG exhibits enhanced stochastic variability with near‐ideal entropy, uniformity, uniqueness, Hamming distance, and independence from autocorrelation variations. Its unclonability is systematically examined using device‐to‐device variations. The generated cryptographic bitstreams pass the National Institute of Standards and Technology (NIST) randomness tests. This nanoscale CIPS‐based TRNG is circuit‐integrable and exhibits potential for hardware security in edge devices with advanced data encryption.

show abstract

An Overview of Spintronic True Random Number Generator

Cited by 15 publications

References 51 publications

Neuromorphic Computing with Resistive Memory and Bayesian Machines

Neuromorphic Computing with Resistive Memory and Bayesian Machines

CMOS plus stochastic nanomagnets enabling heterogeneous computers for probabilistic inference and learning

Ferroelectric Stochasticity in 2D CuInP₂S₆ and Its Application for True Random Number Generator

Contact Info

Product

Resources

About

An Overview of Spintronic True Random Number Generator

Cited by 15 publications

References 51 publications

Neuromorphic Computing with Resistive Memory and Bayesian Machines

Neuromorphic Computing with Resistive Memory and Bayesian Machines

CMOS plus stochastic nanomagnets enabling heterogeneous computers for probabilistic inference and learning

Ferroelectric Stochasticity in 2D CuInP2S6 and Its Application for True Random Number Generator

Contact Info

Product

Resources

About

Ferroelectric Stochasticity in 2D CuInP₂S₆ and Its Application for True Random Number Generator