1,720,999 research outputs found
Theoretical Foundations of Memristor Cellular Nonlinear Networks: Memcomputing With Bistable-Like Memristors
No full textThis paper presents the theory of a novel memcomputing
paradigm based upon a memristive version of standard
Cellular Nonlinear Networks. The insertion of a nonvolatile
memristor in the circuit of each cell endows the dynamic array
with the capability to store and retrieve data into and from
the resistance switching memories, obviating the current need
for extra memory blocks. Choosing the parameters of each
cell circuit so that the memristors may undergo solely sharp
transitions between two states, each processing element may be
approximately described at any time as one of two first-order
systems. Under this assumption, the classical Dynamic Route
Map may be employed to synthesise and analyse the data storage
and retrieval genes. A new system-theoretic methodology, called
Second-Order Dynamic Route Map, is also introduced for the first
time in this paper. This technique allows to study the operating
principles of arrays with second-order processing elements, as is
the case, in the proposed network, if the set up of cell circuit
parameters induces analogue memristive dynamics. This paper
shows how the novel tool may be adopted to investigate the
operating mechanisms of a cellular array with second-order cells,
which compute the element-wise logical OR between two binary
images
Analytical Study of the Fading Memory Phenomenon in a TaOx Memristor Model
No full textThis paper presents a theoretical study of the fading
memory phenomenon in a TaOx-based memristor, manufactured
and modeled at Hewlett-Packard Labs. Specifically, we derive
a set of equations that can be used to characterize its steady-state
response to a high-frequency zero-mean periodic square-wave
voltage stimulus. Our results reveal a hidden property of
the Dynamic Route Map (DRM) system-theoretic analysis tool,
i.e. its capability to predict accurately the mean value of the
state variable oscillation in first-order non-volatile memristors,
at steady-state, as a function of the input amplitude alone. This
DRM property may be useful in real-world memristor-based
applications, where voltage pulses are most often employed to
modulate the states of practical memristor devices
High Frequency Response of Non-Volatile Memristors
No full textThis paper presents an analytical investigation of
the transient and steady-state response of non-volatile memristors
to high frequency periodic inputs, using as a case study a
TaOx-based nano-scale memristor model derived at HP Labs.
For the first time, we provide a mathematical proof for the fading
memory phenomenon in memristors stimulated by periodic
inputs in the high frequency limit. Specifically, we demonstrate
that the steady-state response of a non-volatile memristor, exhibiting
asymmetric switching kinetics with respect to the polarity of
the input, depends only on the amplitude of the testing signal
and not on the device initial conditions. Based on the results
of our analyses, we provide an alternative method for tuning
the memristor state by using high-frequency AC inputs, and
introduce a new system-theoretic visualization tool, namely the
input-referred High-Frequency Dynamic Route Map (HF-DRM),
that allows the reproduction of the memristor time-response to
any high-frequency periodic input from each admissible initial
condition. The purely theoretical results introduced in this paper
could inspire new approaches for modulating the memory states
of practical non-volatile memristors
Multi-tasking and Memcomputing with Memristor Cellular Nonlinear Networks
No full textMemristor Cellular Nonlinear Networks (M-CNNs)
have been recently introduced as a functional upgrade of
standard CNNs, empowered by the potential of memristors to
perform storage and computing functionalities in the same area.
This paper exploits the diverse features of M-CNNs, which
are equipped with threshold-based binary resistance switching
devices, introducing two state-of-the-art image processing MCNNs:
a) the multi-tasking CORNER-EDGE M-CNN, which
performs corner or edge detection depending on the initial states
of the memristors within the network; b) the memcomputing
STORE-EDGE M-CNN, which outputs the edges of a binary
input image, that is simultaneously stored in the memristors of
the cellular array
Image Processing by Cellular Memcomputing Structures
No full textThe introduction of memcomputing memristors
into the design of Cellular Nonlinear Networks (CNNs) allows
to reduce the integrated circuit area typically allocated to each
processing element in hardware realizations. Furthermore, the
highly nonlinear dynamics of memristors enriches the multivariate
signal processing capabilities of these cellular memprocessing
structures. This is demonstrated in this paper, where the standard
and generalized Dynamic Route Map analysis tools are employed
to elucidate the mechanisms by which a Memristor CNN with
bistable-like and analog dynamic nonvolatile memristors executes
fundamental image processing operations, respectively
Theoretical Foundations of Memristor Cellular Nonlinear Networks: Stability Analysis With Dynamic Memristors
No full textIf the memristor, used in each cell of a memristive
variant of the standard space-invariant Cellular Nonlinear
Network (CNN), undergoes analogue memductance changes,
the processing element operates as a second-order system. The
Dynamic Route Map (DRM) technique, applicable to investigate
first-order systems only, is no longer relevant. In this manuscript,
a recently introduced methodology, generalizing the DRM
technique to second-order systems, is applied to the models of
Memristor CNN (M-CNN) cells, accomodating dynamic memristors.
This allows to gain insights into the operating principles
of these cellular structures, which make computations through
the evolution of their states toward prescribed equilibria. Our
analysis uncovers all possible local and global phenomena, which
may emerge in the cell phase space under zero offset current
for any self-feedback synaptic weight. Under these hypotheses,
the dynamics of the M-CNN cell may significantly differ from
those of a standard space-invariant CNN counterpart. The
insertion of an offset current into each cell endows it with further
properties, including monostability. The analysis method is used
to demonstrate how a non-autonomous memristive array exploits
the capability of its cells to feature monostability or bistability,
depending upon the respective offset currents, to compute the
element-wise logical AND between two binary images
CNNs with bistable-like non-volatile memristors: a novel mem-computing paradigm for the IoT era
No full textThis work presents a novel mem-computing
paradigm for the Internet-of-Things era. The paradigm revolves
around the peculiar feature of memristors to process information
and store data in the same physical location in order to enhance
the performance of Cellular Nonlinear Networks, especially in
view of a future hardware implementation and its integration
with state-of-the-art high-resolution visual sensor arrays. A
bistable-like non-volatile memristor is used in each dynamic
processor of the cellular network in place for the linear resistor of
a standard realization. The resulting cell is then endowed with a
couple of possible Dynamic Route Maps, one for each of the two
memristor states. This peculiar property endows the network
with new functionalities as compared to a standard nonlinear
dynamic array
DC Characterization of Numerically Efficient and Stable Locally Active Device Models
No full textIn this work, we focus on two locally active device
models and present, firstly, the simplification of a 3D modified
Poole-Frenkel conduction based model on a mathematical basis,
which leads to a compact and numerically efficient form of the
conductance expression. Then, we present a transformation of
the equations of a thermally induced phase transition based
locally active device model such that the resulting set of
equations is numerically stable and requires less simulation
time. The new versions of both models share a similar form,
though their state variables have different physical meanings. As
an important aspect of their S-shaped DC I-V curves, we
examine the DC characteristics of these equivalent models and
derive analytical expressions for the peak and valley points in
terms of the physically meaningful model parameters.
Theoretical results are verified through numerical simulations
while our findings may support device manufacturers to adopt
a systematical approach to tailor the fundamental
characteristics of locally active devices and help circuit designers
to execute low cost and robust simulations of large scale systems
utilizing them
Analytical Derivation of Sharp-Edge-of-Chaos Domain in a One-Dimensional Memristor Array
No full textIn this work, we present analytical derivation of
Sharp-Edge-of-Chaos (SEOC) domain for one dimensional (1D)
reaction-diffusion arrays where we assume 1-port coupling with
periodic boundary conditions. We consider a general form for
the complexity function of the uncoupled cell and following an
iterative approach, we derive the analytical formula of the
destabilization condition for the 1D reaction-diffusion array
with n elements. The destabilization condition further gives the
critical value of the coupling resistor element for the emergence
of pattern formation across the array. In order to demonstrate
the functionality of the analytical derivations, we examine the
normalized version of the complexity function of a practical
memristive cell, and investigate the evolution of the critical value
of the coupling resistor of the 1D array with respect to the
parameter values of the complexity function and to the array
size. In this way, we reveal a time-efficient simulation method
for the determination of the destabilization condition in 1D
memristive reaction-diffusion arrays which can be adopted for
arrays of higher dimensions as well as for n-port couplings in the
future
Mem-computing CNNs with bistable-like memristors
No full textIn this paper we propose a new mem-computing image
processing architecture, called Memristor Cellular Nonlinear
Network, which leverages the unique capability of nonvolatile
memristors to compute and store data in the same physical
nano-scale locations. Adopting a bistable-like memristor in place
for the linear resistor in the standard realization of a cell of
the nonlinear dynamic array, the resulting network is capable
to process information by exploiting the time evolution of the
voltages across the memristors as well as to store/retrieve results
into/ from the memristances. This attractive feature, absent in a
standard Cellular Nonlinear Network, may pave the way towards
the future development of a new generation of visual processors
with unprecedented spatial resolution
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