Implementation of digital MemComputing using standard electronic components

• Published in: International journal of circuit theory and applications Vol. 53; no. 4; pp. 2447 - 2465
• Main Authors: Zhang, Yuan‐Hang, Di Ventra, Massimiliano
• Format: Journal Article
• Language: English
• Published: Bognor Regis Wiley Subscription Services, Inc 01.04.2025
• Subjects: circuit simulations; Combinatorial analysis; combinatorial optimization; Dynamical systems; Electronic components; Equations of motion; Hardware; hardware acceleration; MemComputing; nonlinear dynamical systems; Nonlinear systems; self‐organization
• ISSN: 0098-9886; 1097-007X
• DOI: 10.1002/cta.4220

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. This paper presents a scalable and efficient hardware design for Digital MemComputing Machines (DMMs) using standard electronic components, to solve combinatorial optimization problems. The design circumvents the limitations of numerical noise, while minimizing the impact of physical noise. This enhances system stability and reliability, broadening the potential for tackling increasingly complex problems with MemComputing.