Quantitative Evaluation of Hardware Binary Stochastic Neurons

Hassan, Orchi; Datta, Supriyo

doi:10.1103/physrevapplied.15.064046

Cited by 25 publications

(11 citation statements)

References 83 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Complex functions, such as full adder and multiplication/factorization, were also suggested, showing the methodology’s potential to be applied to more complex logic circuits. Finally, a comparison between other p-computing hardware and this work is shown in Table 2 18 , 46 , 52 , 53 . The average power consumption of the p-bit circuit was calculated using the pulse output of the device (Supplementary Fig.…”

Section: Discussionmentioning

confidence: 99%

Probabilistic computing using Cu0.1Te0.9/HfO2/Pt diffusive memristors

et al. 2022

View full text Add to dashboard Cite

A computing scheme that can solve complex tasks is necessary as the big data field proliferates. Probabilistic computing (p-computing) paves the way to efficiently handle problems based on stochastic units called probabilistic bits (p-bits). This study proposes p-computing based on the threshold switching (TS) behavior of a Cu0.1Te0.9/HfO2/Pt (CTHP) diffusive memristor. The theoretical background of the p-computing resembling the Hopfield network structure is introduced to explain the p-computing system. P-bits are realized by the stochastic TS behavior of CTHP diffusive memristors, and they are connected to form the p-computing network. The memristor-based p-bit is likely to be ‘0’ and ‘1’, of which probability is controlled by an input voltage. The memristor-based p-computing enables all 16 Boolean logic operations in both forward and inverted operations, showing the possibility of expanding its uses for complex operations, such as full adder and factorization.

show abstract

Section: Discussionmentioning

confidence: 99%

Probabilistic computing using Cu0.1Te0.9/HfO2/Pt diffusive memristors

et al. 2022

View full text Add to dashboard Cite

show abstract

“…It has been shown both theoretically 24,25 and experimentally 26,27 that s-MTJ-based p-bits can be designed to generate new random numbers in times ∼ nanoseconds. The same circuit could also be used with other fluctuating resistors 28 , but one advantage of s-MTJ's is that they can be built by modifying magnetoresistive random access memory (MRAM) technology that has already reached gigabit levels of integration 29 .…”

Section: Methodsmentioning

confidence: 99%

Probabilistic computing with p-bits

Kaiser,

Datta

2021

Preprint

Self Cite

View full text Add to dashboard Cite

Digital computers store information in the form of bits that can take on one of two values 0 and 1, while quantum computers are based on qubits that are described by a complex wavefunction whose squared magnitude gives the probability of measuring either a 0 or a 1. Here we make the case for a probabilistic computer based on p-bits which take on values 0 and 1 with controlled probabilities and can be implemented with specialized compact energy-efficient hardware. We propose a generic architecture for such p-computers and show that they can significantly accelerate randomized algorithms used in a wide variety of applications including but not limited to Bayesian networks, optimization, Ising models and quantum Monte Carlo.

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

Quantitative Evaluation of Hardware Binary Stochastic Neurons

Cited by 25 publications

References 83 publications

Probabilistic computing using Cu0.1Te0.9/HfO2/Pt diffusive memristors

Probabilistic computing using Cu0.1Te0.9/HfO2/Pt diffusive memristors

Probabilistic computing with p-bits

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

Contact Info

Product

Resources

About