Evolving nanoscale associative memories with memristors

Sinha, Arpita; Kulkarni, Manjari S.; Teuscher, Christof

doi:10.1109/nano.2011.6144623

Cited by 16 publications

(8 citation statements)

References 10 publications

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“…If there is no current flow, the internal state will be retained. From this it follows that a memristor acts like a nonvolatile memory, whereas the range of values is continuous [14], [15]. That is an interesting fact comparable to transistor memory technology.…”

Section: Propertiesmentioning

confidence: 86%

Sinusoidal analysis of memristor bridge circuit-rectifier for low frequencies

Pabst¹,

Schmidt²

2012

Preprint

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Reasoned by its dynamical behavior, the memristor enables a lot of new applications in analog circuit design. Since some realizations are shown (e.g. 2007 by Hewlett Packard), the development of applications with memristors becomes more and more interesting. Whereas most of the research was done in the direction of memristor applications in neural networks and storage devices, less publications deal with practical applications of analog memristive circuits. But this topic is also promising further applications. Therefore, this article proposes a frequency dependent rectifier memristor bridge for different purposes (e.g. using as a programmable synaptic membrane voltage generator for Spike-Time-Dependent-Plasticity) and describes the circuit theory. In this context it is shown that the Picard Iteration is one possibility to solve the system of nonlinear state equations of memristor circuits analytically. An intuitive picture of how a memristor works in a network in general is given as well. In this context some research on the dynamical behavior of a HP memristor should be done.

show abstract

Section: Propertiesmentioning

confidence: 86%

Sinusoidal analysis of memristor bridge circuit-rectifier for low frequencies

Pabst¹,

Schmidt²

2012

Preprint

View full text Add to dashboard Cite

show abstract

“…If no current is applied, the internal state will be retained. It follows that a memristor acts like a nonvolatile memory, whereas the range of values is continuous [14,15]. That is an interesting fact comparable to transistor memory technology.…”

Section: Propertiesmentioning

confidence: 98%

Frequency dependent rectifier memristor bridge used as a programmable synaptic membrane voltage generator

Pabst

Schmidt

2013

Journal of Electrical Bioimpedance

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Reasoned by its dynamical behavior, the memristor enables a lot of new applications in analog circuit design. Since some realizations have been shown (e.g. 2007, Hewlett Packard), the development of applications with memristors becomes more and more interesting. Besides applications in neural networks and storage devices, analog memristive circuits also promise further applications. Therefore, this article proposes a frequency dependent rectifier memristor bridge for different purposes, for example, using as a programmable synaptic membrane voltage generator for Spike-Time-Dependent-Plasticity and describes the circuit theory. In this context it is shown that the Picard Iteration is one possibility to analytically solve the system of nonlinear state equations of memristor circuits. An intuitive picture of how a memristor works in a network in general is given as well and in this context some research on the dynamical behavior of a HP memristor should be done. After all it is suggested to use the memristor bridge as a neuron.

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“…This challenge has previously been addressed in many different ways, such as for example by modeling artificial neural networks with traditional components (e.g., resistors, capacitors, operational amplifiers, including voltage and current sources) [28]. We have recently evolved a 3memristor circuit [19] that can solve the simple task as outlined below.…”

Section: B Associative Memorymentioning

confidence: 99%

“…We have previously shown that simple memristor circuits can solve associative tasks [19]. However, the circuits were automatically discovered by using Genetic Programming (GP) and did not use a reservoir.…”

Section: Introductionmentioning

confidence: 99%

Memristor-based reservoir computing

Kulkarni

Teuscher

2012

Proceedings of the 2012 IEEE/ACM International Symposium on Nanoscale Architectures

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116

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As feature-size scaling and "Moore's Law" in integrated CMOS circuits further slows down, attention is shifting to computing by non-von Neumann and non-Boolean computing models. Reservoir computing (RC) is a new computing paradigm that allows to harness the intrinsic dynamics of a "reservoir" to perform useful computations. The reservoir, or compute core, must only provide sufficiently rich dynamics that are then mapped onto a low-dimensional space by an readout layer. One of the key advantages of this approach is that only the readout layer needs to be adapted to perform the desired computation. The reservoir itself remains unchanged. In this paper we use for the first time memristive components as reservoir building blocks that are assembled into device networks. Memristive components are particularly interesting for this purpose because of their non-linear and memory characteristics. In addition, they can be integrated very densely and provide rich dynamics with a few components only. We use pattern recognition and associative memory tasks to illustrate the memristive reservoir computing approach. For that purpose, we have built a software framework that allows to create valid memristor networks, to simulate and evaluate them in Ngspice, and to train the readout layer by means of a Genetic Algorithm (GA). Our results show that we can efficiently and robustly classify temporal patterns. The approach presents a promising new computing paradigm that harnesses the non-linear, time-dependent, and highly-variable properties of current memristive components for solving computational tasks.

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Evolving nanoscale associative memories with memristors

Cited by 16 publications

References 10 publications

Sinusoidal analysis of memristor bridge circuit-rectifier for low frequencies

Sinusoidal analysis of memristor bridge circuit-rectifier for low frequencies

Frequency dependent rectifier memristor bridge used as a programmable synaptic membrane voltage generator

Memristor-based reservoir computing

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