Probabilistic Neural Computing with Stochastic Devices

Misra, Shashank; Bland, L. C.; Cardwell, Suma; Incorvia, Jean Anne C.; James, Conrad D.; Kent, Andrew D.; Schuman, Catherine D.; Smith, J. Darby; Aimone, James B.

doi:10.1002/adma.202204569

Cited by 38 publications

(20 citation statements)

References 84 publications

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“…[ 434–436 ] Random number generation is also an important tool in probabilistic SNNs for simulating synaptic neurotransmitter release or membrane channel opening and closing. [ 437,438 ] Traditional approaches create seed‐dependent pseudo‐random numbers using software and hardware strategies. [ 439,440 ] A physical entropy source, e.g., variability or noise in memory elements, is required to construct a true random number generator (TRNG).…”

Section: Mm‐based In‐memory Computingmentioning

confidence: 99%

Nonvolatile Memristive Materials and Physical Modeling for In‐Memory and In‐Sensor Computing

Go,

Lim,

Lee

et al. 2024

Small Science

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Separate memory and processing units are utilized in conventional von Neumann computational architectures. However, regarding the energy and the time, it is costly to shuffle data between the memory and the processing entity, and for data‐intensive applications associated with artificial intelligence, the demand is ever increasing. A paradigm shift in traditional architectures is required, and in‐memory computing is one of the non‐von‐Neumann computing strategies. By harnessing physical signatures of the memory, computing workloads are administered in the same memory element. For in‐memory computing, a wide range of memristive material (MM) systems have been examined. Moreover, developing computing schemes that perform in the same sensory network and that minimize the data shuffle between the processing unit and the sensing element is a requirement, to process large volumes of data efficiently and decrease the energy consumption. In this review, an overview of the switching character and system signature harnessed in three archetypal MM systems is rendered, along with an integrated application survey for developing in‐sensor and in‐memory computing, viz., brain‐inspired or analogue computing, physical unclonable functions, and random number generators. The recent progress in theoretical studies that reveal the structural origin of the fast‐switching ability of the MM system is further summarized.

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Section: Mm‐based In‐memory Computingmentioning

confidence: 99%

Nonvolatile Memristive Materials and Physical Modeling for In‐Memory and In‐Sensor Computing

Go,

Lim,

Lee

et al. 2024

Small Science

View full text Add to dashboard Cite

show abstract

“…Merely designing perfect devices is not sufficient to bring about a paradigm shift. AI-guided codesign can further alleviate the challenges of interdisciplinary codesign and accelerate design. , AI-enhanced codesign techniques can use the constraints from emerging devices to develop algorithmic solutions for a given application. This can not only accelerate design but also enable interactions between experts from different areas of the microelectronics design stack including theory, algorithms, circuits, devices, and materials.…”

Section: Other Approaches Of Memristive Technologymentioning

confidence: 99%

“…Creativity in hardware design will dominate future computing systems that leverage increasingly heterogeneous components. Probabilistic computing is an avenue to leverage neuromorphic devices . Multiple applications can benefit from probabilistic computing including modeling complex problems such as nuclear and high-energy physics events, complex biological systems, precise climate models, large-scale neuromorphic applications, and AI algorithms.…”

Section: Other Approaches Of Memristive Technologymentioning

confidence: 99%

Recent Advances and Future Prospects for Memristive Materials, Devices, and Systems

et al. 2023

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Memristive technology has been rapidly emerging as a potential alternative to traditional CMOS technology, which is facing fundamental limitations in its development. Since oxide-based resistive switches were demonstrated as memristors in 2008, memristive devices have garnered significant attention due to their biomimetic memory properties, which promise to significantly improve power consumption in computing applications. Here, we provide a comprehensive overview of recent advances in memristive technology, including memristive devices, theory, algorithms, architectures, and systems. In addition, we discuss research directions for various applications of memristive technology including hardware accelerators for artificial intelligence, in-sensor computing, and probabilistic computing. Finally, we provide a forward-looking perspective on the future of memristive technology, outlining the challenges and opportunities for further research and innovation in this field. By providing an up-to-date overview of the state-of-the-art in memristive technology, this review aims to inform and inspire further research in this field.

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“…This full-stack research program covering hardware, architecture, algorithms, and applications is similar to the related field of quantum computation where a large degree of interdisciplinary expertise is required to move the field forward (see the related reviews Ref. [16,17]). The purpose of this paper is to serve as a consolidated summary of recent developments with new results in hardware, architectures, and algorithms.…”

Section: Full-stack View and Organizationmentioning

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

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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.

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Probabilistic Neural Computing with Stochastic Devices

Cited by 38 publications

References 84 publications

Nonvolatile Memristive Materials and Physical Modeling for In‐Memory and In‐Sensor Computing

Nonvolatile Memristive Materials and Physical Modeling for In‐Memory and In‐Sensor Computing

Recent Advances and Future Prospects for Memristive Materials, Devices, and Systems

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

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