Phase-type distributions for studying variability in resistive memories

Acal, Christian; Castro, Juan Luis; Aguilera, Ana M.; Jiménez-Molinos, F.; Roldán, J.B.

doi:10.1016/j.cam.2018.06.010

Cited by 28 publications

(28 citation statements)

References 26 publications

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“…It is known that, due to the intrinsic variability of resistive switching devices [2,[11][12][13][14][15], a well-conceived circuitry is needed for memristor multilevel operation. In this approach, multilevel device operation is provided as the basis for hardware quantized ANNs; i.e., networks with quantized synaptic weights and biases.…”

Section: Introductionmentioning

confidence: 99%

Study of Quantized Hardware Deep Neural Networks Based on Resistive Switching Devices, Conventional versus Convolutional Approaches

et al. 2021

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A comprehensive analysis of two types of artificial neural networks (ANN) is performed to assess the influence of quantization on the synaptic weights. Conventional multilayer-perceptron (MLP) and convolutional neural networks (CNN) have been considered by changing their features in the training and inference contexts, such as number of levels in the quantization process, the number of hidden layers on the network topology, the number of neurons per hidden layer, the image databases, the number of convolutional layers, etc. A reference technology based on 1T1R structures with bipolar memristors including HfO2 dielectrics was employed, accounting for different multilevel schemes and the corresponding conductance quantization algorithms. The accuracy of the image recognition processes was studied in depth. This type of studies are essential prior to hardware implementation of neural networks. The obtained results support the use of CNNs for image domains. This is linked to the role played by convolutional layers at extracting image features and reducing the data complexity. In this case, the number of synaptic weights can be reduced in comparison to MLPs.

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Section: Introductionmentioning

confidence: 99%

Study of Quantized Hardware Deep Neural Networks Based on Resistive Switching Devices, Conventional versus Convolutional Approaches

et al. 2021

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“…Accounting for the aforementioned intrinsic stochasticity of these devices, the choice of a correct statistical strategy to attack this problem is essential in the analysis. In this respect, we use the PH distributions, which have already been employed to depict some facets of RRAM variability [24]; nonetheless, as far as we know, they have not been used in RTN analysis.…”

Section: Introductionmentioning

confidence: 99%

A Complex Model via Phase-Type Distributions to Study Random Telegraph Noise in Resistive Memories

et al. 2021

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A new stochastic process was developed by considering the internal performance of macro-states in which the sojourn time in each one is phase-type distributed depending on time. The stationary distribution was calculated through matrix-algorithmic methods and multiple interesting measures were worked out. The number of visits distribution to a determine macro-state were analyzed from the respective differential equations and the Laplace transform. The mean number of visits to a macro-state between any two times was given. The results were implemented computationally and were successfully applied to study random telegraph noise (RTN) in resistive memories. RTN is an important concern in resistive random access memory (RRAM) operation. On one hand, it could limit some of the technological applications of these devices; on the other hand, RTN can be used for the physical characterization. Therefore, an in-depth statistical analysis to model the behavior of these devices is of essential importance.

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“…In this respect, the device stochasticity has been studied and modeled from different viewpoints. For instance, advanced statistical distribution functions, e.g., Phase-type distributions have been successfully applied to deal with variability [15,16]; approaches founded on functional data analysis were also used [17][18][19]; from another perspective, simulations linked to kinetic Monte Carlo techniques proved their adequacy [20,21]. Here, we address CTC variability in h-BN memristors from the time series analysis perspective [22][23][24].…”

Section: Introductionmentioning

confidence: 99%