1 code implementation • 21 Sep 2023 • Yuan-Hang Zhang, Massimiliano Di Ventra
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.
1 code implementation • 20 Jan 2023 • Daniel Primosch, Yuan-Hang Zhang, Massimiliano Di Ventra
Digital memcomputing machines (DMMs) are a new class of computing machines that employ non-quantum dynamical systems with memory to solve combinatorial optimization problems.
no code implementations • 17 Feb 2021 • Haik Manukian, Massimiliano Di Ventra
The deep extension of the restricted Boltzmann machine (RBM), known as the deep Boltzmann machine (DBM), is an expressive family of machine learning models which can serve as compact representations of complex probability distributions.
no code implementations • 6 Feb 2021 • Yuan-Hang Zhang, Massimiliano Di Ventra
To investigate the reasons behind the robustness and effectiveness of this method, we employ three explicit integration schemes (forward Euler, trapezoid and Runge-Kutta 4th order) with a constant time step, to solve 3-SAT instances with planted solutions.
no code implementations • 23 Dec 2020 • Pieter Gypens, Jonathan Leliaert, Massimiliano Di Ventra, Bartel Van Waeyenberge, Daniele Pinna
Despite the realization of several proofs of concepts of such nanomagnetic logic[13-15], it is still unclear what the advantages are compared to the widespread CMOS designs, due to their need for clocking[16, 17] and/or thermal annealing [18, 19] for which fast convergence to the ground state is not guaranteed.
Combinatorial Optimization Mesoscale and Nanoscale Physics Adaptation and Self-Organizing Systems
no code implementations • 15 Jan 2020 • Haik Manukian, Yan Ru Pei, Sean R. B. Bearden, Massimiliano Di Ventra
Restricted Boltzmann machines (RBMs) are a powerful class of generative models, but their training requires computing a gradient that, unlike supervised backpropagation on typical loss functions, is notoriously difficult even to approximate.
no code implementations • 16 Oct 2019 • Giovanni Finocchio, Massimiliano Di Ventra, Kerem Y. Camsari, Karin Everschor-Sitte, Pedram Khalili Amiri, Zhongming Zeng
Novel computational paradigms may provide the blueprint to help solving the time and energy limitations that we face with our modern computers, and provide solutions to complex problems more efficiently (with reduced time, power consumption and/or less device footprint) than is currently possible with standard approaches.
Applied Physics Mesoscale and Nanoscale Physics
1 code implementation • 14 May 2019 • Yan Ru Pei, Haik Manukian, Massimiliano Di Ventra
Many optimization problems can be cast into the maximum satisfiability (MAX-SAT) form, and many solvers have been developed for tackling such problems.
no code implementations • 20 Feb 2018 • Massimiliano Di Ventra, Fabio L. Traversa
In this perspective we discuss how to employ one such property, memory (time non-locality), in a novel physics-based approach to computation: Memcomputing.
no code implementations • 1 Jan 2018 • Haik Manukian, Fabio L. Traversa, Massimiliano Di Ventra
In fact, the acceleration of pretraining achieved by simulating DMMs is comparable to, in number of iterations, the recently reported hardware application of the quantum annealing method on the same network and data set.
no code implementations • 23 Dec 2017 • Yan Ru Pei, Fabio L. Traversa, Massimiliano Di Ventra
We show that UMMs can simulate both types of machines, hence they are both "liquid-" or "reservoir-complete" and "quantum-complete".
no code implementations • 23 Oct 2017 • Fabio L. Traversa, Pietro Cicotti, Forrest Sheldon, Massimiliano Di Ventra
However, despite numerous research efforts, in many cases even approximations to the optimal solution are hard to find, as the computational time for further refining a candidate solution grows exponentially with input size.
no code implementations • 13 Dec 2016 • Haik Manukian, Fabio L. Traversa, Massimiliano Di Ventra
We propose to use Digital Memcomputing Machines (DMMs), implemented with self-organizing logic gates (SOLGs), to solve the problem of numerical inversion.
no code implementations • 18 Nov 2014 • Fabio L. Traversa, Chiara Ramella, Fabrizio Bonani, Massimiliano Di Ventra
Even though the particular machine presented here is eventually limited by noise--and will thus require error-correcting codes to scale to an arbitrary number of memprocessors--it represents the first proof-of-concept of a machine capable of working with the collective state of interacting memory cells, unlike the present-day single-state machines built using the von Neumann architecture.
no code implementations • 14 Oct 2014 • Yuriy V. Pershin, Fabio L. Traversa, Massimiliano Di Ventra
We show theoretically that networks of membrane memcapacitive systems -- capacitors with memory made out of membrane materials -- can be used to perform a complete set of logic gates in a massively parallel way by simply changing the external input amplitudes, but not the topology of the network.
no code implementations • 5 May 2014 • Fabio L. Traversa, Massimiliano Di Ventra
We introduce the notion of universal memcomputing machines (UMMs): a class of brain-inspired general-purpose computing machines based on systems with memory, whereby processing and storing of information occur on the same physical location.