Implementation of digital MemComputing using standard electronic components

Summary Digital MemComputing machines (DMMs), which employ nonlinear dynamical systems with memory (time non‐locality), have proven to be a robust and scalable unconventional computing approach for solving a wide variety of combinatorial optimization problems. However, most of the research so far has focused on the numerical simulations of the equations of motion of DMMs. This inevitably subjects time to discretization, which brings its own (numerical) issues that would be otherwise absent in actual physical systems operating in continuous time. Although hardware realizations of DMMs have been previously suggested, their implementation would require materials and devices that are not so easy to integrate with traditional electronics. Addressing this, our study introduces a novel hardware design for DMMs, utilizing readily available electronic components. This approach not only significantly boosts computational speed compared to current models but also exhibits remarkable robustness against additive noise. Crucially, it circumvents the limitations imposed by numerical noise, ensuring enhanced stability and reliability during extended operations. This paves a new path for tackling increasingly complex problems, leveraging the inherent advantages of DMMs in a more practical and accessible framework.

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Publication Details

Journal
International Journal of Circuit Theory and Applications
Published
2024-08-08
DOI
https://doi.org/10.1002/cta.4220
Citations
5
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
1.74

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article

Implementation of digital MemComputing using standard electronic components

Massimiliano Di Ventra, Yuanhang Zhang
5 citations
International Journal of Circuit Theory and Applications
Neural Networks and Reservoir Computing
1.74
article

Implementation of digital MemComputing using standard electronic components

Massimiliano Di Ventra, Yuanhang Zhang
article en
5 citations

Abstract

Summary Digital MemComputing machines (DMMs), which employ nonlinear dynamical systems with memory (time non‐locality), have proven to be a robust and scalable unconventional computing approach for solving a wide variety of combinatorial optimization problems. However, most of the research so far has focused on the numerical simulations of the equations of motion of DMMs. This inevitably subjects time to discretization, which brings its own (numerical) issues that would be otherwise absent in actual physical systems operating in continuous time. Although hardware realizations of DMMs have been previously suggested, their implementation would require materials and devices that are not so easy to integrate with traditional electronics. Addressing this, our study introduces a novel hardware design for DMMs, utilizing readily available electronic components. This approach not only significantly boosts computational speed compared to current models but also exhibits remarkable robustness against additive noise. Crucially, it circumvents the limitations imposed by numerical noise, ensuring enhanced stability and reliability during extended operations. This paves a new path for tackling increasingly complex problems, leveraging the inherent advantages of DMMs in a more practical and accessible framework.

International Journal of Circuit Theory and ApplicationsVol. 53(4)
University of California San Diego (US)
National Science Foundation
Openalex Percentile: Top 13%
Neural Networks and Reservoir Computing
1.74
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Implementation of digital MemComputing using standard electronic components — Massimiliano Di Ventra, Yuanhang Zhang · International Journal of Circuit Theory and Applications (2024) | TGRS Research Map | TGRS