289 research outputs found
Memcapacitive Devices in Logic and Crossbar Applications
Over the last decade, memristive devices have been widely adopted in
computing for various conventional and unconventional applications. While the
integration density, memory property, and nonlinear characteristics have many
benefits, reducing the energy consumption is limited by the resistive nature of
the devices. Memcapacitors would address that limitation while still having all
the benefits of memristors. Recent work has shown that with adjusted parameters
during the fabrication process, a metal-oxide device can indeed exhibit a
memcapacitive behavior. We introduce novel memcapacitive logic gates and
memcapacitive crossbar classifiers as a proof of concept that such applications
can outperform memristor-based architectures. The results illustrate that,
compared to memristive logic gates, our memcapacitive gates consume about 7x
less power. The memcapacitive crossbar classifier achieves similar
classification performance but reduces the power consumption by a factor of
about 1,500x for the MNIST dataset and a factor of about 1,000x for the
CIFAR-10 dataset compared to a memristive crossbar. Our simulation results
demonstrate that memcapacitive devices have great potential for both Boolean
logic and analog low-power applications
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
Neuro-memristive Circuits for Edge Computing: A review
The volume, veracity, variability, and velocity of data produced from the
ever-increasing network of sensors connected to Internet pose challenges for
power management, scalability, and sustainability of cloud computing
infrastructure. Increasing the data processing capability of edge computing
devices at lower power requirements can reduce several overheads for cloud
computing solutions. This paper provides the review of neuromorphic
CMOS-memristive architectures that can be integrated into edge computing
devices. We discuss why the neuromorphic architectures are useful for edge
devices and show the advantages, drawbacks and open problems in the field of
neuro-memristive circuits for edge computing
A differential memristive synapse circuit for on-line learning in neuromorphic computing systems
Spike-based learning with memristive devices in neuromorphic computing
architectures typically uses learning circuits that require overlapping pulses
from pre- and post-synaptic nodes. This imposes severe constraints on the
length of the pulses transmitted in the network, and on the network's
throughput. Furthermore, most of these circuits do not decouple the currents
flowing through memristive devices from the one stimulating the target neuron.
This can be a problem when using devices with high conductance values, because
of the resulting large currents. In this paper we propose a novel circuit that
decouples the current produced by the memristive device from the one used to
stimulate the post-synaptic neuron, by using a novel differential scheme based
on the Gilbert normalizer circuit. We show how this circuit is useful for
reducing the effect of variability in the memristive devices, and how it is
ideally suited for spike-based learning mechanisms that do not require
overlapping pre- and post-synaptic pulses. We demonstrate the features of the
proposed synapse circuit with SPICE simulations, and validate its learning
properties with high-level behavioral network simulations which use a
stochastic gradient descent learning rule in two classification tasks.Comment: 18 Pages main text, 9 pages of supplementary text, 19 figures.
Patente
Simple Floating Voltage-Controlled Memductor Emulator for Analog Applications
The topic of memristive circuits is a novel topic in circuit theory that has become of great importance due to its unique behavior which is useful in different applications. But since there is a lack of memristor samples, a memristor emulator is used instead of a solid state memristor. In this paper, a new simple floating voltage-controlled memductor emulator is introduced which is implemented using commercial off the shelf (COTS) realization. The mathematical modeling of the proposed circuit is derived to match the theoretical model. The proposed circuit is tested experimentally using different excitation signals such as sinusoidal, square, and triangular waves showing an excellent matching with previously reported simulations
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