CNN using memristors for neighborhood connections

Lehtonen, Eero; Laiho, Mika

doi:10.1109/cnna.2010.5430304

Cited by 180 publications

(193 citation statements)

References 11 publications

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“…The nonlinear I-V characteristic is desirable for logic circuits, and hence more appropriate memristor models have been proposed. In [17], a model is proposed based on the experimental results described in [15]. The relationship between the current and voltage is…”

Section: Nonlinear Ion Drift Modelmentioning

confidence: 99%

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TEAM: ThrEshold Adaptive Memristor Model

Kvatinsky

Friedman

Kolodny

et al. 2013

IEEE Trans. Circuits Syst. I

699

324

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Abstract-Memristors are novel devices, which can be used in applications such as memory, logic, and neuromorphic systems. A memristor offers several advantages to existing applications: nonvolatility, good scalability, effectively no leakage current, and compatibility with CMOS technology, both electrically and in terms of manufacturing. Several models for memristors have been developed and are discussed in this paper. Digital applications such as memory and logic require a model that is highly nonlinear, simple for calculations, and sufficiently accurate. In this paper, a new memristor model is presented -TEAM, ThrEshold Adaptive Memristor model. Previously published models are compared in this paper to the proposed TEAM model. It is shown that the proposed model is reasonably accurate and computationally efficient, and is more appropriate for circuit simulation than previously published models.

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Section: Nonlinear Ion Drift Modelmentioning

confidence: 99%

“…There are several proposed SPICE macromodels for the linear ion drift model [13], [22] and the nonlinear ion drift model [17]. A SPICE model for the Simmons tunneling barrier model has recently been proposed [21], but is complicated and inefficient in terms of computational time.…”

Section: Comparison Between the Modelsmentioning

confidence: 99%

TEAM: ThrEshold Adaptive Memristor Model

Kvatinsky

Friedman

Kolodny

et al. 2013

IEEE Trans. Circuits Syst. I

699

324

View full text Add to dashboard Cite

show abstract

“…From the definitions of the states of the units in (20) and the its update in (19), the state vector xðnÞ is determined by its previous state xðn À 1Þ, the input uðnÞ and states of other units. Thus, according to the definition of CNN, the reservoir network is redefined to have a regular geometric grid and local connections by…”

Section: The Reservoir With Memristor-based Local Connectionsmentioning

confidence: 99%

“…Recently, a memristor based CAM cell [7,14] was proposed to improve the density and power consumption of CAM. Beyond CAM, the memristor is a potential element to mimic the synapses in neural networks with the benefits of nano-scale structure and non-volatility [19,20,29]. It works as the connection sites and represents the connection strength between neurons.…”

Section: Introductionmentioning

confidence: 99%

Investigations of the staircase memristor model and applications of memristor-based local connections

Yang

Chen

Wang

2016

Analog Integr Circ Sig Process

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The memristor is postulated by Chua (IEEE Trans Circuit Theory, 18(5): [507][508][509][510][511][512][513][514][515][516][517][518][519] 1971) as the fourth fundamental passive circuit element and experimentally validated by HP labs in 2008. It is an emerging device in nano technology that provides low power consumption as well as high density. In this paper, the staircase memristor model is discussed and investigated with the HP memristor model. By connecting HP memristor models in series or parallel, a staircase memristor model could be constructed and demonstrate staircase behavior. By comparing the staircase memristor model to the general HP memristor model, distinctions between them are demonstrated and this lends themselves to different applications. Further to the memristor-based cellular neural network (CNN), the structure is modified and applied to the echo state network (ESN) where memristors are used as local connections. By this means, the ESN benefits from the simple structure, non-volatility and low power feature of memristors and therefore the complexity and size of the original ESN architecture can be reduced. Meanwhile, the simpler structure still has satisfactory performance in applications compared with the original ESN.

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“…To design/simulate many applications, several SPICE models of the memristor have been presented, for example in [7,8], and [9]. In these models, the boundary conditions of the memristor are solved using the window function.…”

Section: Introductionmentioning

confidence: 99%