9,785 research outputs found
Hierarchical Composition of Memristive Networks for Real-Time Computing
Advances in materials science have led to physical instantiations of
self-assembled networks of memristive devices and demonstrations of their
computational capability through reservoir computing. Reservoir computing is an
approach that takes advantage of collective system dynamics for real-time
computing. A dynamical system, called a reservoir, is excited with a
time-varying signal and observations of its states are used to reconstruct a
desired output signal. However, such a monolithic assembly limits the
computational power due to signal interdependency and the resulting correlated
readouts. Here, we introduce an approach that hierarchically composes a set of
interconnected memristive networks into a larger reservoir. We use signal
amplification and restoration to reduce reservoir state correlation, which
improves the feature extraction from the input signals. Using the same number
of output signals, such a hierarchical composition of heterogeneous small
networks outperforms monolithic memristive networks by at least 20% on waveform
generation tasks. On the NARMA-10 task, we reduce the error by up to a factor
of 2 compared to homogeneous reservoirs with sigmoidal neurons, whereas single
memristive networks are unable to produce the correct result. Hierarchical
composition is key for solving more complex tasks with such novel nano-scale
hardware
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
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
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