1,721,002 research outputs found
Memristor-based binary synapses for deep neural networks
No full textThe development of biologically–oriented mathematical models has allowed recent advances in neuromorphic computing architectures and in the understanding of the mechanisms behind the complex dynamics of living systems. Deep Neural Networks are among the most computational efficient architectures used in machine learning. The simplest structure is represented by multiple–layers perceptrons with binary synapses (i.e. the synaptic weights assume binary values). The manuscript introduces a memristor–based circuit to implement an artificial binary synapse. In the paper it will be shown how the binary output is obtained with respect to the internal state of the memristor and how this kind of sub–system could be a more efficient implementation of synapses inside networks such as a perceptron
Blinking Networks of Memristor Oscillatory Circuits in the Flux-Charge Domain
Full text linkMultistability phenomena and complex nonlinear dynamics in memristor oscillators pave the way to obtain efficient solutions to optimization problems by means of novel computational architectures based on the interconnection of single–device oscillators. It is well-known that topological properties of interconnections permit to control synchronization and spatio–temporal patterns in oscillatory networks. When the interconnections can change in time with a given probability to connect two oscillators, the whole network acts as a complex network with blinking couplings. The work of has shown that a particular class of blinking complex networks are able to completely synchronize in a faster fashion with respect to other coupling strategies. This work focuses on the specific class of blinking complex networks made of Memristor–based Oscillatory Circuits (MOCs). By exploiting the recent Flux–Charge Analysis Method, we make clear that synchronization phenomena in blinking networks of memristor oscillators having stochastic couplings, i.e., Blinking Memristor Oscillatory Networks (BMONs), correspond to global periodic oscillations on invariant manifolds and the effect of a blinking link is to shift the nonlinear dynamics through the infinite (invariant) manifolds. Numerical simulations performed on MOCs prove that synchronization phenomena can be controlled just by changing the coupling amongst them
Spice e PSpice esercitazioni di elettrotecnica
Full text linkQuesto testo si propone di introdurre lo studente all'apprendimento dell'Elettrotecnica, avvalendosi di una serie di esercizi da risolvere usando un moderno programma di analisi circuitale. Contemporaneamente, oltre alle soluzioni, per cosi' dire sperimentali, sono presentate le soluzioni analitiche ottenute per via teorica, applicando le leggi e i metodi dell'Elettrotecnica. Come dice il titolo stesso, il programma prescelto e' il ben noto Spice, sviluppato all'Universita' di Berkeley. Esso ha segnato l'inizio di un'epoca nel campo della simulazione dei circuiti ed e' stato universalmente adottato, nelle sue innumerevoli versioni commerciali, per la sua riconosciuta efficienza. PSpice, usato nello svolgere gli esercizi qui proposti, ne rappresenta una delle prime evoluzioni commerciali ed e' distribuito gratuitamente in una versione dimostrativa per PC, rendendo possibile una piu' ampia diffusione delle tecniche di simulazione. Il presente testo cerca anche di colmare, almeno in parte, la grave lacuna nella formazione universitaria degli studenti della Laurea triennale, rappresentata dalla difficolta' ad accedere a laboratori sperimentali, a causa dell'elevata numerosita' dei corsi di base, quali l'Elettrotecnica. Grazie agli esercizi proposti e all'uso di PSpice, gli studenti potranno compiere esperimenti come se avessero costruito un circuito reale in laboratorio, anche se e' opportuno ricordare che una reale verifica sperimentale rimane insostituibile per una buona formazione scientifica. Il libro e' diviso in due parti: la prima presenta i testi degli esercizi, con un breve richiamo, ove necessario, ai fondamenti utili per la loro soluzione; la seconda mostra le soluzioni dei problemi ricavate con PSpice e le verifiche analitiche effettuate analizzando i circuiti proposti. Gli argomenti trattati riguardano: l'analisi di circuiti resistivi lineari, contenenti resistori, generatori indipendenti e dipendenti e amplificatori operazionali ideali; l'analisi nel dominio del tempo di circuiti dinamici; l'analisi di circuiti in regime sinusoidale; l'analisi di circuiti resistivi non lineari, contenenti diodi. Infine, un grazie molto sentito all'Editore, per aver seguito con la consueta cura l'edizione del presente volume
Memristor-based cellular nonlinear networks with belief propagation inspired algorithm
No full textNeural Networks trained with the Belief Propagation Inspired (BPI) algorithm are able to learn a number of associations close to the theoretical limit in time that is sublinear in the number of input. Using binary synapses, implemented by a memristor, a single layer perceptron with BPI has been proposed. It well know that perceptrons with step function type nonlinearity can be implemented by a suitable class of Cellular Neural/Nonlinear Networks. This paper aims to present a statistical analysis on the learning efficiency of Memristor-based Cellular Nonlinear Networks (M-CNNs) with Belief Propagation Inspired (BPI) algorithm. Monte Carlo simulations permit to assess that the learning efficiency of M-CNNs with BPI is not regardless of the input signals given to train the perceptron
State equations of memristor circuits with nonlinear lossless elements in the Flux-charge domain
No full textRecent works have introduced an effective technique to analyze nonlinear dynamics of a class LM of circuits containing ideal flux or charge controlled memristors and linear lossless elements (i.e. ideal capacitors and inductors). The technique, named Flux Charge Analysis Method (FCAM), is based on analyzing the circuits in the flux charge domain instead of the traditional voltage current domain. Goal of this paper is to extend the FCAM to a larger class N of circuits containing also nonlinear capacitors and inductors. Nonlinear circuits with memristors and nonlinear lossless elements are widely used to several real nanoscale devices including the well-known Josephson junction. After deriving the constitutive relation in the flux charge domain of each two terminal element in N, the work focuses on a relevant subclass of N for which a state equation description can be obtained. State Equations (SE) formulation provides the fundamental basis for studying the chief features of the nonlinear dynamics: presence of invariant manifolds in autonomous circuits; coexistence of infinitely many different reduced order dynamics on the manifolds; bifurcations due to changing of initial conditions for a fixed set of parameters, a.k.a. bifurcations without parameters
Unfolding nonlinear dynamics in computing systems with mem-elements
No full textGoal of the paper is to investigate the analogue computational capabilities of dynamic networks with mem-elements. A relevant class of networks containing memristors and (possibly) nonlinear capacitors and inductors is considered. The paper unfolds the nonlinear dynamics of these networks by highlighting some main features that are potentially useful for real-time signal processing and in-memory computing. These include the presence of bifurcations without parameters and the coexistence of different regimes and complex dynamics. The analysis is conducted by means of a recently developed technique named flux-charge analysis method
Complex behavior in memristor circuits based on static nonlinear two-ports and dynamic bipole
No full textSince memristors are widely studied and the range
of applications developed for these devices are becoming increasingly
broad, circuital implementations exhibiting fingerprints of
memristive behavior have become highly relevant. A class of
memristor circuits was recently obtained by cascading a static
nonlinear two-port with a dynamical one-port. In general, these
circuits are classifiable as extended memristors and may be
controlled either in current or in voltage. This paper has the aim
of presenting a novel element from this class which experiences
various complex behaviors, including periodic oscillations and
chaos. This thorough investigation of the rich nonlinear dynamics
emerging in the proposed circuit may shed light on interesting
engineering applications that memristors may be suited for
Investigating High-Frequency Memristor-Based Backscattering Circuits
No full textThis paper investigates memristor-based radiofrequency (RF) backscattering systems, exploring the feasibility of using memristors as the core component in RF backscattering applications. The research aims to exploit the memory properties of non-volatile memristors to design novel high-frequency circuits capable of modulating backscattered signals without employing RF transistors. Utilizing the TaOx-based nanoscale memristor model developed by HP Labs, this study conducts a preliminary numerical investigation. The focus is on introducing a novel architecture and exploring the class of RF signals under which memristors can function effectively as backscatter devices. In this regard, the paper gives preliminary indications that can be properly used to address the design of novel memristor-based backscattering circuits
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