Robustness of Binary Stochastic Neurons Implemented With Low Barrier Nanomagnets Made of Dilute Magnetic Semiconductors

Rahman, Rahnuma; Bandyopadhyay, Supriyo

doi:10.1109/lmag.2022.3202135

Cited by 5 publications

(3 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another is to replace common ferromagnets used in MTJs with dilute magnetic semiconductors which have several orders of magnitude lower saturation magnetization. That makes the energy barriers in the nanomagnets much less sensitive to shape and size variations and suppresses device-to-device variations [10].…”

Section: Current and Future Challengesmentioning

confidence: 99%

Roadmap for unconventional computing with nanotechnology

Finocchio,

Incorvia,

Friedman

et al. 2024

Nano Futures

Self Cite

View full text Add to dashboard Cite

In the “Beyond Moore’s Law” era, with increasing edge intelligence, domain-specific computing embracing unconventional approaches will become increasingly prevalent. At the same time, the adoption of a wide variety of nanotechnologies will offer benefits in energy cost, computational speed, reduced footprint, cyber-resilience and processing prowess. The time is ripe to lay out a roadmap for unconventional computing with nanotechnologies to guide future research and this collection aims to fulfill that need. The authors provide a comprehensive roadmap for neuromorphic computing with electron spins, memristive devices, two-dimensional nanomaterials, nanomagnets and assorted dynamical systems. They also address other paradigms such as Ising machines, Bayesian inference engines, probabilistic computing with p-bits, processing in memory, quantum memories and algorithms, computing with skyrmions and spin waves, and brain inspired computing for incremental learning and solving problems in severely resource constrained environments. All of these approaches have advantages over conventional Boolean computing predicated on the von-Neumann architecture. With the computational need for artificial intelligence growing at a rate 50×faster than Moore’s law for electronics, more unconventional approaches to computing and signal processing will appear on the horizon and this roadmap will aid in identifying future needs and challenges.

show abstract

Section: Current and Future Challengesmentioning

confidence: 99%

Roadmap for unconventional computing with nanotechnology

Finocchio,

Incorvia,

Friedman

et al. 2024

Nano Futures

Self Cite

View full text Add to dashboard Cite

show abstract

“…On the other hand, using nanodevices such as CMOScompatible stochastic magnetic tunnel junctions (sMTJ), millions of p-bits can be accommodated in single cores due to the scalability achieved by the MRAM technology, exceeding 1Gbit MRAM chips [56,57]. However, before the stable MTJs can be controllably made stochastic, challenges at the material and device level must be addressed [58,59] with careful magnet design [60][61][62]. Different flavors of magnetic p-bits exist [63][64][65][66], for a recent review, see Ref.…”

Section: Hardware: Physical Implementation Of P-bits a P-bitmentioning

confidence: 99%

A Full-Stack View of Probabilistic Computing With p-Bits: Devices, Architectures, and Algorithms

Chowdhury

Grimaldi

Aadit

et al. 2023

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

View full text Add to dashboard Cite

The transistor celebrated its 75 th birthday in 2022. The continued scaling of the transistor defined by Moore's Law continues, albeit at a slower pace. Meanwhile, computing demands and energy consumption required by modern artificial intelligence (AI) algorithms have skyrocketed. As an alternative to scaling transistors for general-purpose computing, the integration of transistors with unconventional technologies has emerged as a promising path for domain-specific computing. In this article, we provide a full-stack review of probabilistic computing with p-bits as a representative example of the energy-efficient and domain-specific computing movement. We argue that p-bits could be used to build energy-efficient probabilistic systems, tailored for probabilistic algorithms and applications. From hardware, architecture, and algorithmic perspectives, we outline the main applications of probabilistic computers ranging from probabilistic machine learning and AI to combinatorial optimization and quantum simulation. Combining emerging nanodevices with the existing CMOS ecosystem will lead to probabilistic computers with orders of magnitude improvements in energy efficiency and probabilistic sampling, potentially unlocking previously unexplored regimes for powerful probabilistic algorithms.

show abstract

“…(ii) Additionally, it is difficult to fabricate a chip with a tight statistical distribution around a small energy barrier for all devices, further limiting the sampling rate and the number of p-bits achievable on a chip. Materials with small saturation magnetization, such as dilute magnetic semiconductors, have been proposed as a potential solution to the problem of device-to-device variation [24]. However, realizing this potential requires large room-temperature tunneling magnetoresistance in these material systems, which has not been experimentally realized to date [25,26].…”

Section: Introductionmentioning

confidence: 99%

Probabilistic computing with voltage-controlled dynamics in magnetic tunnel junctions

Shao,

Duffee,

Raimondo

et al. 2023

Nanotechnology

View full text Add to dashboard Cite

Probabilistic (p-) computing is a physics-based approach to addressing computational problems which are difficult to solve by conventional von Neumann computers. A key requirement for p-computing is the realization of fast, compact, and energy-efficient probabilistic bits. Stochastic magnetic tunnel junctions (MTJs) with low energy barriers, where the relative dwell time in each state is controlled by current, have been proposed as a candidate to implement p-bits. This approach presents challenges due to the need for precise control of a small energy barrier across large numbers of MTJs, and due to the need for an analog control signal. Here we demonstrate an alternative p-bit design based on perpendicular MTJs that uses the voltage-controlled magnetic anisotropy (VCMA) effect to create the random state of a p-bit on demand. The MTJs are stable (i.e., have large energy barriers) in the absence of voltage, and VCMA-induced dynamics are used to generate random numbers in less than 10 ns/bit. We then show a compact method of implementing p-bits by using VC-MTJs without a bias current. As a demonstration of the feasibility of the proposed p-bits and high quality of the generated random numbers, we solve up to 40-bit integer factorization problems using experimental bit-streams generated by VC-MTJs. Our proposal can impact the development of p-computers, both by supporting a fully spintronic implementation of a p-bit, and alternatively, by enabling true random number generation at low cost for ultralow-power and compact p-computers implemented in complementary metal-oxide semiconductor (CMOS) chips.

show abstract

Robustness of Binary Stochastic Neurons Implemented With Low Barrier Nanomagnets Made of Dilute Magnetic Semiconductors

Cited by 5 publications

References 18 publications

Roadmap for unconventional computing with nanotechnology

Roadmap for unconventional computing with nanotechnology

A Full-Stack View of Probabilistic Computing With p-Bits: Devices, Architectures, and Algorithms

Probabilistic computing with voltage-controlled dynamics in magnetic tunnel junctions

Contact Info

Product

Resources

About