Search results for "Probabilistic computing"

showing 3 items of 3 documents

Modeling Networks of Probabilistic Memristors in SPICE

2021

Efficient simulation of stochastic memristors and their networks requires novel modeling approaches. Utilizing a master equation to find occupation probabilities of network states is a recent major departure from typical memristor modeling [Chaos, solitons fractals 142, 110385 (2021)]. In the present article we show how to implement such master equations in SPICE – a general purpose circuit simulation program. In the case studies we simulate the dynamics of acdriven probabilistic binary and multi-state memristors, and dc-driven networks of probabilistic binary and multi-state memristors. Our SPICE results are in perfect agreement with known analytical solutions. Examples of LTspice code are…

FOS: Computer and information sciencesHardware_MEMORYSTRUCTURESCondensed Matter - Mesoscale and Nanoscale PhysicsFOS: Physical sciencesComputer Science - Emerging TechnologiesComputer Science::Hardware ArchitectureEmerging Technologies (cs.ET)Computer Science::Emerging TechnologiesmemristorsspicenetworksMesoscale and Nanoscale Physics (cond-mat.mes-hall)lcsh:Electrical engineering. Electronics. Nuclear engineeringprobabilistic computinglcsh:TK1-9971Radioengineering
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Probabilistic Memristive Networks: Application of a Master Equation to Networks of Binary ReRAM cells

2020

Abstract The possibility of using non-deterministic circuit components has been gaining significant attention in recent years. The modeling and simulation of their circuits require novel approaches, as now the state of a circuit at an arbitrary moment in time cannot be predicted deterministically. Generally, these circuits should be described in terms of probabilities, the circuit variables should be calculated on average, and correlation functions should be used to explore interrelations among the variables. In this paper, we use, for the first time, a master equation to analyze the networks composed of probabilistic binary memristors. Analytical solutions of the master equation for the ca…

FOS: Computer and information sciencesProbabilistic computingComputer scienceGeneral MathematicsGeneral Physics and AstronomyBinary numberFOS: Physical sciencesComputer Science - Emerging TechnologiesMemristorTopologylaw.inventionModeling and simulationComputer Science::Hardware ArchitectureComputer Science::Emerging TechnologieslawMaster equationMesoscale and Nanoscale Physics (cond-mat.mes-hall)Probabilistic logicElectronic circuitCondensed Matter - Materials ScienceCondensed Matter - Mesoscale and Nanoscale PhysicsApplied MathematicsProbabilistic logicMaterials Science (cond-mat.mtrl-sci)Statistical and Nonlinear PhysicsMoment (mathematics)Emerging Technologies (cs.ET)State (computer science)NetworksMemristors
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Modeling Networks of Probabilistic Memristors in SPICE

2021

Efficient simulation of stochastic memristors and their networks requires novel modeling approaches. Utilizing a master equation to find occupation probabilities of network states is a recent major departure from typical memristor modeling [Chaos, solitons fractals 142, 110385 (2021)]. In the present article we show how to implement such master equations in SPICE – a general purpose circuit simulation program. In the case studies we simulate the dynamics of acdriven probabilistic binary and multi-state memristors, and dc-driven networks of probabilistic binary and multi-state memristors. Our SPICE results are in perfect agreement with known analytical solutions. Examples of LTspice code are…

SPICEComputer scienceSpiceProbabilistic logicBinary number020206 networking & telecommunications02 engineering and technologyMemristorlaw.inventionComputer Science::Hardware ArchitectureComputer Science::Emerging TechnologieslawnetworksMaster equation0202 electrical engineering electronic engineering information engineeringApplied mathematicsElectrical and Electronic EngineeringMemristorsprobabilistic computingRadioengineering
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