arXiv subjects

Fabio Lorenzo Traversa

Publications and source records attributed to Fabio Lorenzo Traversa.

7 recordsLinked to original sources

arXiv2023

Scaling up prime factorization with self-organizing gates: A memcomputing approach

We report preliminary results on using the MEMCPU\texttrademark{} Platform to compute the prime factorization of large biprimes. The first approach, the direct model, directly returns the factors of a given biprime. The second approach, the congruence model, returns smooth congruences to address the bottleneck of standard sieve methods. The models have size-dependent structure, and the MEMCPU Platform requires structure-dependent tuning for optimal performance. Therefore, for both models, we tuned the platform on sample problems up to a given size according to available resources. Then we generated RSA-like benchmark biprimes to perform rigorous scaling analysis. The MEMCPU timings over the tuned range followed low degree polynomials in the number of bits, markedly different than other tested methods including general number field sieve. MEMCPU's congruence model was the most promising, which was scaled up to 300-bit factorization problems while following a $2^{nd}$ degree polynomial fit. We also discuss the approach to tuning the MEMCPU Platform for problems beyond the reach of today's most advanced methods. Finally, basic analysis of the acceleration expected from an ASIC implementation is provided and suggests the possibility of real time factorization of large biprimes.

cs.ET↗

arXiv2020

Coupled oscillator networks for von Neumann and non von Neumann computing

The frenetic growth of the need for computation performance and efficiency, along with the intrinsic limitations of the current main solutions, is pushing the scientific community towards unconventional, and sometimes even exotic, alternatives to the standard computing architectures. In this work we provide a panorama of the most relevant alternatives, both according and not the von Neumann architecture, highlighting which of the classical challenges, such as energy efficiency and/or computational complexity, they are trying to tackle. We focus on the alternatives based on networks of weakly coupled oscillators. This unconventional approach, already introduced by Goto and Von Neumann in the 50s, is recently regaining interest with potential applications to both von Neumann and non von Neumann type of computing. In this contribution, we present a general framework based on the phase equation we derive from the description of nonlinear weakly coupled oscillators especially useful for computing applications. We then use this formalism to design and prove the working principle and stability assessment of Boolean gates such as NOT and MAJORITY, that can be potentially employed as building blocks for both von Neumann and non-von Neumann architectures.

cs.ET↗

arXiv2016

The complex dynamics of memristive circuits: analytical results and universal slow relaxation

Networks with memristive elements (resistors with memory) are being explored for a variety of applications ranging from unconventional computing to models of the brain. However, analytical results that highlight the role of the graph connectivity on the memory dynamics are still a few, thus limiting our understanding of these important dynamical systems. In this paper, we derive an exact matrix equation of motion that takes into account all the network constraints of a purely memristive circuit, and we employ it to derive analytical results regarding its relaxation properties. We are able to describe the memory evolution in terms of orthogonal projection operators onto the subspace of fundamental loop space of the underlying circuit. This orthogonal projection explicitly reveals the coupling between the spatial and temporal sectors of the memristive circuits and compactly describes the circuit topology. For the case of disordered graphs, we are able to explain the emergence of a power law relaxation as a superposition of exponential relaxation times with a broad range of scales using random matrices. This power law is also {\it universal}, namely independent of the topology of the underlying graph but dependent only on the density of loops. In the case of circuits subject to alternating voltage instead, we are able to obtain an approximate solution of the dynamics, which is tested against a specific network topology. These result suggest a much richer dynamics of memristive networks than previously considered.

cond-mat.dis-nn↗

arXiv2016

Sequential measurement of displacement and conduction currents in electronic devices

The extension of the Ramo-Schockley-Pellegrini theorem for quantum systems allows to define a positive-operator valued measure (POVM) for the total conduction plus displacement electrical current. The resulting current operator is characterized by two parameters, viz. the width of the associated Gaussian functions and the lapse of time between consecutive measurements. For large Gaussian dispersions and small time intervals, the operator obeys to a continuous weak-measurement scheme. Contrarily, in the limit of very narrow Gaussian widths and a single-shot measurement, the operator corresponds to a standard von Neumann (projective) measurement. We have implemented the resulting measurement protocol into a quantum electron transport simulator. Numerical results for a resonant tunneling diode show the great sensibility of current-voltage characteristics to different parameter configurations of the total current operator.

quant-ph↗

arXiv2016

Local energy and power for many-particle quantum systems driven by an external electrical field

We derive expressions for the expectation values of the local energy and the local power transferred by an external electrical field to a many-particle system of interacting spinless electrons. In analogy with the definition of the (local) presence and current probability densities, we construct a local energy operator such that the time-rate of change of its expectation value provides information on the spatial distribution of power. Results are presented as functions of an arbitrarily small volume $Ω$, and physical insights are discussed by means of the quantum hydrodynamical representation of the wavefunction, which is proven to allow for a clear-cut separation into contributions with and without classical correspondence. Quantum features of the local power are mainly manifested through the presence of non-local sources/sinks of power and through the action of forces with no classical counterpart. Many-particle classical-like effects arise in the form of current-force correlations and through the inflow/outflow of energy across the boundaries of the volume $Ω$. Interestingly, such intriguing features are only reflected in the expression for the local power when the volume $Ω$ is finite. Otherwise, for closed systems with $Ω\to \infty$, we recover a classical-like single-particle expression.

quant-ph↗

arXiv2014

Dynamic Computing Random Access Memory

The present von Neumann computing paradigm involves a significant amount of information transfer between a central processing unit (CPU) and memory, with concomitant limitations in the actual execution speed. However, it has been recently argued that a different form of computation, dubbed memcomputing [Nature Physics, 9, 200-202 (2013)] and inspired by the operation of our brain, can resolve the intrinsic limitations of present day architectures by allowing for computing and storing of information on the same physical platform. Here we show a simple and practical realization of memcomputing that utilizes easy-to-build memcapacitive systems. We name this architecture Dynamic Computing Random Access Memory (DCRAM). We show that DCRAM provides massively-parallel and polymorphic digital logic, namely it allows for different logic operations with the same architecture, by varying only the control signals. In addition, by taking into account realistic parameters, its energy expenditures can be as low as a few fJ per operation. DCRAM is fully compatible with CMOS technology, can be realized with current fabrication facilities, and therefore can really serve as an alternative to the present computing technology.

cs.ET↗

arXiv2013

Memory models of adaptive behaviour

Adaptive response to a varying environment is a common feature of biological organisms. Reproducing such features in electronic systems and circuits is of great importance for a variety of applications. Here, we consider memory models inspired by an intriguing ability of slime molds to both memorize the period of temperature and humidity variations, and anticipate the next variations to come, when appropriately trained. Effective circuit models of such behavior are designed using i) a set of LC-contours with memristive damping, and ii) a single memcapacitive system-based adaptive contour with memristive damping. We consider these two approaches in detail by comparing their results and predictions. Finally, possible biological experiments that would discriminate between the models are discussed. In this work, we also introduce an effective description of certain memory circuit elements.

q-bio.CB↗