436 research outputs found
Reliable Modeling of Ideal Generic Memristors via State-Space Transformation
The paper refers to problems of modeling and computer simulation of generic memristors caused by the so-called window functions, namely the stick effect, nonconvergence, and finding fundamentally incorrect solutions. A profoundly different modeling approach is proposed, which is mathematically equivalent to window-based modeling. However, due to its numerical stability, it definitely smoothes the above problems away
Modeling the AgInSbTe Memristor
The AgInSbTe memristor shows gradual resistance tuning characteristics, which makes it a potential candidate to emulate biological plastic synapses. The working mechanism of the device is complex, and both intrinsic charge-trapping mechanism and extrinsic electrochemical metallization effect are confirmed in the AgInSbTe memristor. Mathematical model of the AgInSbTe memristor has not been given before. We propose the flux-voltage controlled memristor model. With piecewise linear approximation technique, we deliver the flux-voltage controlled memristor model of the AgInSbTe memristor based on the experiment data. Our model fits the data well. The flux-voltage controlled memristor model and the piecewise linear approximation method are also suitable for modeling other kinds of memristor devices based on experiment data
SPICE model of memristive devices with threshold
Although memristive devices with threshold voltages are the norm rather than
the exception in experimentally realizable systems, their SPICE programming is
not yet common. Here, we show how to implement such systems in the SPICE
environment. Specifically, we present SPICE models of a popular
voltage-controlled memristive system specified by five different parameters for
PSPICE and NGSPICE circuit simulators. We expect this implementation to find
widespread use in circuits design and testing
Window functions and sigmoidal behaviour of memristive systems
Summary: A common approach to model memristive systems is to include empirical window functions to describe edge effects and nonlinearities in the change of the memristance. We demonstrate that under quite general conditions, each window function can be associated with a sigmoidal curve relating the normalised time-dependent memristance to the time integral of the input. Conversely, this explicit relation allows us to derive window functions suitable for the mesoscopic modelling of memristive systems from a variety of well-known sigmoidals. Such sigmoidal curves are defined in terms of measured variables and can thus be extracted from input and output signals of a device and then transformed to its corresponding window. We also introduce a new generalised window function that allows the flexible modelling of asymmetric edge effects in a simple manner
Computing shortest paths in 2D and 3D memristive networks
Global optimisation problems in networks often require shortest path length
computations to determine the most efficient route. The simplest and most
common problem with a shortest path solution is perhaps that of a traditional
labyrinth or maze with a single entrance and exit. Many techniques and
algorithms have been derived to solve mazes, which often tend to be
computationally demanding, especially as the size of maze and number of paths
increase. In addition, they are not suitable for performing multiple shortest
path computations in mazes with multiple entrance and exit points. Mazes have
been proposed to be solved using memristive networks and in this paper we
extend the idea to show how networks of memristive elements can be utilised to
solve multiple shortest paths in a single network. We also show simulations
using memristive circuit elements that demonstrate shortest path computations
in both 2D and 3D networks, which could have potential applications in various
fields
Memristor models for machine learning
In the quest for alternatives to traditional CMOS, it is being suggested that
digital computing efficiency and power can be improved by matching the
precision to the application. Many applications do not need the high precision
that is being used today. In particular, large gains in area- and power
efficiency could be achieved by dedicated analog realizations of approximate
computing engines. In this work, we explore the use of memristor networks for
analog approximate computation, based on a machine learning framework called
reservoir computing. Most experimental investigations on the dynamics of
memristors focus on their nonvolatile behavior. Hence, the volatility that is
present in the developed technologies is usually unwanted and it is not
included in simulation models. In contrast, in reservoir computing, volatility
is not only desirable but necessary. Therefore, in this work, we propose two
different ways to incorporate it into memristor simulation models. The first is
an extension of Strukov's model and the second is an equivalent Wiener model
approximation. We analyze and compare the dynamical properties of these models
and discuss their implications for the memory and the nonlinear processing
capacity of memristor networks. Our results indicate that device variability,
increasingly causing problems in traditional computer design, is an asset in
the context of reservoir computing. We conclude that, although both models
could lead to useful memristor based reservoir computing systems, their
computational performance will differ. Therefore, experimental modeling
research is required for the development of accurate volatile memristor models.Comment: 4 figures, no tables. Submitted to neural computatio
A neuromorphic systems approach to in-memory computing with non-ideal memristive devices: From mitigation to exploitation
Memristive devices represent a promising technology for building neuromorphic
electronic systems. In addition to their compactness and non-volatility
features, they are characterized by computationally relevant physical
properties, such as state-dependence, non-linear conductance changes, and
intrinsic variability in both their switching threshold and conductance values,
that make them ideal devices for emulating the bio-physics of real synapses. In
this paper we present a spiking neural network architecture that supports the
use of memristive devices as synaptic elements, and propose mixed-signal
analog-digital interfacing circuits which mitigate the effect of variability in
their conductance values and exploit their variability in the switching
threshold, for implementing stochastic learning. The effect of device
variability is mitigated by using pairs of memristive devices configured in a
complementary push-pull mechanism and interfaced to a current-mode normalizer
circuit. The stochastic learning mechanism is obtained by mapping the desired
change in synaptic weight into a corresponding switching probability that is
derived from the intrinsic stochastic behavior of memristive devices. We
demonstrate the features of the CMOS circuits and apply the architecture
proposed to a standard neural network hand-written digit classification
benchmark based on the MNIST data-set. We evaluate the performance of the
approach proposed on this benchmark using behavioral-level spiking neural
network simulation, showing both the effect of the reduction in conductance
variability produced by the current-mode normalizer circuit, and the increase
in performance as a function of the number of memristive devices used in each
synapse.Comment: 13 pages, 12 figures, accepted for Faraday Discussion
- …