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Found 16 papers, 3 papers with code
Implementation of digital MemComputing using standard electronic components
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.
Self-averaging of digital memcomputing machines
Digital memcomputing machines (DMMs) are a new class of computing machines that employ non-quantum dynamical systems with memory to solve combinatorial optimization problems.
Mode-Assisted Joint Training of Deep Boltzmann Machines
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.
Directed percolation and numerical stability of simulations of digital memcomputing machines
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.
Nanomagnetic Self-Organizing Logic Gates
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
Mode-Assisted Unsupervised Learning of Restricted Boltzmann Machines
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.
The promise of spintronics for unconventional computing
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
Generating Weighted MAX-2-SAT Instances of Tunable Difficulty with Frustrated Loops
Many optimization problems can be cast into the maximum satisfiability (MAX-SAT) form, and many solvers have been developed for tackling such problems.
Memcomputing: Leveraging memory and physics to compute efficiently
In this perspective we discuss how to employ one such property, memory (time non-locality), in a novel physics-based approach to computation: Memcomputing.
Accelerating Deep Learning with Memcomputing
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.
On the Universality of Memcomputing Machines
We show that UMMs can simulate both types of machines, hence they are both "liquid-" or "reservoir-complete" and "quantum-complete".
Evidence of an exponential speed-up in the solution of hard optimization problems
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.
Memcomputing Numerical Inversion with Self-Organizing Logic Gates
We propose to use Digital Memcomputing Machines (DMMs), implemented with self-organizing logic gates (SOLGs), to solve the problem of numerical inversion.
Memcomputing NP-complete problems in polynomial time using polynomial resources and collective states
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.
Memcomputing with membrane memcapacitive systems
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.
Universal Memcomputing Machines
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.
