20,327 research outputs found
Toward bio-inspired information processing with networks of nano-scale switching elements
Unconventional computing explores multi-scale platforms connecting
molecular-scale devices into networks for the development of scalable
neuromorphic architectures, often based on new materials and components with
new functionalities. We review some work investigating the functionalities of
locally connected networks of different types of switching elements as
computational substrates. In particular, we discuss reservoir computing with
networks of nonlinear nanoscale components. In usual neuromorphic paradigms,
the network synaptic weights are adjusted as a result of a training/learning
process. In reservoir computing, the non-linear network acts as a dynamical
system mixing and spreading the input signals over a large state space, and
only a readout layer is trained. We illustrate the most important concepts with
a few examples, featuring memristor networks with time-dependent and history
dependent resistances
Magnetic Cellular Nonlinear Network with Spin Wave Bus for Image Processing
We describe and analyze a cellular nonlinear network based on magnetic
nanostructures for image processing. The network consists of magneto-electric
cells integrated onto a common ferromagnetic film - spin wave bus. The
magneto-electric cell is an artificial two-phase multiferroic structure
comprising piezoelectric and ferromagnetic materials. A bit of information is
assigned to the cell's magnetic polarization, which can be controlled by the
applied voltage. The information exchange among the cells is via the spin waves
propagating in the spin wave bus. Each cell changes its state as a combined
effect of two: the magneto-electric coupling and the interaction with the spin
waves. The distinct feature of the network with spin wave bus is the ability to
control the inter-cell communication by an external global parameter - magnetic
field. The latter makes possible to realize different image processing
functions on the same template without rewiring or reconfiguration. We present
the results of numerical simulations illustrating image filtering, erosion,
dilation, horizontal and vertical line detection, inversion and edge detection
accomplished on one template by the proper choice of the strength and direction
of the external magnetic field. We also present numerical assets on the major
network parameters such as cell density, power dissipation and functional
throughput, and compare them with the parameters projected for other
nano-architectures such as CMOL-CrossNet, Quantum Dot Cellular Automata, and
Quantum Dot Image Processor. Potentially, the utilization of spin waves
phenomena at the nanometer scale may provide a route to low-power consuming and
functional logic circuits for special task data processing
Memory and information processing in neuromorphic systems
A striking difference between brain-inspired neuromorphic processors and
current von Neumann processors architectures is the way in which memory and
processing is organized. As Information and Communication Technologies continue
to address the need for increased computational power through the increase of
cores within a digital processor, neuromorphic engineers and scientists can
complement this need by building processor architectures where memory is
distributed with the processing. In this paper we present a survey of
brain-inspired processor architectures that support models of cortical networks
and deep neural networks. These architectures range from serial clocked
implementations of multi-neuron systems to massively parallel asynchronous ones
and from purely digital systems to mixed analog/digital systems which implement
more biological-like models of neurons and synapses together with a suite of
adaptation and learning mechanisms analogous to the ones found in biological
nervous systems. We describe the advantages of the different approaches being
pursued and present the challenges that need to be addressed for building
artificial neural processing systems that can display the richness of behaviors
seen in biological systems.Comment: Submitted to Proceedings of IEEE, review of recently proposed
neuromorphic computing platforms and system
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
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