Probabilistic-Bits Based on Ferroelectric Field-Effect Transistors for Probabilistic Computing

Luo, Sheng; He, Yihan; Cai, Baofang; Gong, Xiao; Liang, Gengchiau

doi:10.1109/led.2023.3285525

Cited by 6 publications

(3 citation statements)

References 35 publications

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“…To date, a variety of p-bit devices have demonstrated their functionality and applicability in the field of probabilistic computing. These devices encompass thermal noise-driven stochastic magnetic tunnel junctions 5 , 7 – 9 , 42 , 43 , programmed microcontrollers 44 , CMOS-based 20 , 32 FPGA-based 45 , and other emerging probabilistic devices 46 – 51 . Networks and systems constructed upon these devices have exhibited remarkable effectiveness in solving a diverse range of hard computational problems, including integer factorization 6 , 17 , 19 , 21 , combinatorial optimization problems 18 , 52 , 53 , Bayesian inference 8 , 54 – 58 , and machine learning 3 , 59 , 60 .…”

Section: Hardware Implementationsmentioning

confidence: 99%

Direct design of ground-state probabilistic logic using many-body interactions for probabilistic computing

He,

Luo,

Fang

et al. 2024

Sci Rep

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In this work, an innovative design model aimed at enhancing the efficacy of ground-state probabilistic logic with a binary energy landscape (GSPL-BEL) is presented. This model enables the direct conversion of conventional CMOS-based logic circuits into corresponding probabilistic graphical representations based on a given truth table. Compared to the conventional approach of solving the configuration of Ising model-basic probabilistic gates through linear programming, our model directly provides configuration parameters with embedded many-body interactions. For larger-scale probabilistic logic circuits, the GSPL-BEL model can fully utilize the dimensions of many-body interactions, achieving minimal node overhead while ensuring the simplest binary energy landscape and circumventing additional logic synthesis steps. To validate its effectiveness, hardware implementations of probabilistic logic gates were conducted. Probabilistic bits were introduced as Ising cells, and cascaded conventional XNOR gates along with passive resistor networks were precisely designed to realize many-body interactions. HSPICE circuit simulation results demonstrate that the probabilistic logic circuits designed based on this model can successfully operate in free, forward, and reverse modes, exhibiting the simplest binary probability distributions. For a 2-bit × 2-bit integer factorizer involving many-body interactions, compared to the logic synthesis approach, the GSPL-BEL model significantly reduces the number of consumed nodes, the solution space (in the free-run mode), and the number of energy levels from 12, 4096, and 9–8, 256, and 2, respectively. Our findings demonstrate the significant potential of the GSPL-BEL model in optimizing the structure and performance of probabilistic logic circuits, offering a new robust tool for the design and implementation of future probabilistic computing systems.

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Section: Hardware Implementationsmentioning

confidence: 99%

Direct design of ground-state probabilistic logic using many-body interactions for probabilistic computing

He,

Luo,

Fang

et al. 2024

Sci Rep

Self Cite

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“…In this work, we adopt a FeFET-based design due to its low hardware cost and compatibility with CMOS technology [35], where the stochasticity of the p-bit device arises from the thermal noise. The FeFET p-bit comprises a FeFET, a transistor, and two serially connected inverters shown in Fig.…”

Section: A Probabilistic-bit Devicementioning

confidence: 99%

Many-Body Effects-Based Invertible Logic With a Simple Energy Landscape and High Accuracy

He,

Fang,

Luo

et al. 2023

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

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Inspired by many-body effects, we propose a novel design for Boltzmann machine-based invertible logic using probabilistic bits. A CMOS-based XNOR gate is derived to serve as the hardware implementation of many-body interactions and an invertible logic family is built based on this design. Compared to the conventional two-body-based design framework, the many-body-based design enables compact configuration and provides the simplest binarized energy landscape for fundamental invertible logic gates. Furthermore, we demonstrate the composability of the many-body-based invertible logic circuit by merging modular building blocks into large-scale integer factorizers. To optimize the energy landscape of large-scale combinatorial invertible logic circuits, we introduce degeneracy in energy levels which enlarges the probabilities for the lowest states. Circuit simulations of our integer factorizers reveal a significant boost in factorization accuracy. An example of a 2-bit × 2-bit integer factorizer demonstrated an increment of factorization accuracy from 64.99% to 91.44% with a reduction in the number of energy levels from 32 to 9. Similarly, our 6-bit × 6-bit integer factorizer increases the accuracy from 4.430% to 83.65% with the many-body design. Overall, the many-body-based design scheme provides promising results for future invertible logic circuit designs.INDEX TERMS Probabilistic computing, many-body interactions, invertible logic, integer factorizer, probabilistic bit (p-bit).

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“…Several p-bit implementations that use the inherent stochasticity in different materials and devices have been proposed, based on diffusive memristors 9 , resistive RAM 10 , perovskite nickelates 11 , ferroelectric transistors 12 , single photon avalanche diodes 13 , optical parametric oscillators 14 and others. Among alternatives sMTJs built out of low-barrier nanomagnets have demonstrated significant potential due to their ability to amplify noise, converting millivolts of fluctuations to hundreds of millivolts over resistive networks 15 , unlike alternative approaches with amplifiers 16 .…”

Section: Introductionmentioning

confidence: 99%

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

Singh,

Kobayashi,

Cao

et al. 2024

Nat Commun

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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.

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Probabilistic-Bits Based on Ferroelectric Field-Effect Transistors for Probabilistic Computing

Cited by 6 publications

References 35 publications

Direct design of ground-state probabilistic logic using many-body interactions for probabilistic computing

Direct design of ground-state probabilistic logic using many-body interactions for probabilistic computing

Many-Body Effects-Based Invertible Logic With a Simple Energy Landscape and High Accuracy

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

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