839 research outputs found
Neuromorphic, Digital and Quantum Computation with Memory Circuit Elements
Memory effects are ubiquitous in nature and the class of memory circuit
elements - which includes memristors, memcapacitors and meminductors - shows
great potential to understand and simulate the associated fundamental physical
processes. Here, we show that such elements can also be used in electronic
schemes mimicking biologically-inspired computer architectures, performing
digital logic and arithmetic operations, and can expand the capabilities of
certain quantum computation schemes. In particular, we will discuss few
examples where the concept of memory elements is relevant to the realization of
associative memory in neuronal circuits, spike-timing-dependent plasticity of
synapses, digital and field-programmable quantum computing
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
Principles of Neuromorphic Photonics
In an age overrun with information, the ability to process reams of data has
become crucial. The demand for data will continue to grow as smart gadgets
multiply and become increasingly integrated into our daily lives.
Next-generation industries in artificial intelligence services and
high-performance computing are so far supported by microelectronic platforms.
These data-intensive enterprises rely on continual improvements in hardware.
Their prospects are running up against a stark reality: conventional
one-size-fits-all solutions offered by digital electronics can no longer
satisfy this need, as Moore's law (exponential hardware scaling),
interconnection density, and the von Neumann architecture reach their limits.
With its superior speed and reconfigurability, analog photonics can provide
some relief to these problems; however, complex applications of analog
photonics have remained largely unexplored due to the absence of a robust
photonic integration industry. Recently, the landscape for
commercially-manufacturable photonic chips has been changing rapidly and now
promises to achieve economies of scale previously enjoyed solely by
microelectronics.
The scientific community has set out to build bridges between the domains of
photonic device physics and neural networks, giving rise to the field of
\emph{neuromorphic photonics}. This article reviews the recent progress in
integrated neuromorphic photonics. We provide an overview of neuromorphic
computing, discuss the associated technology (microelectronic and photonic)
platforms and compare their metric performance. We discuss photonic neural
network approaches and challenges for integrated neuromorphic photonic
processors while providing an in-depth description of photonic neurons and a
candidate interconnection architecture. We conclude with a future outlook of
neuro-inspired photonic processing.Comment: 28 pages, 19 figure
Memristors for the Curious Outsiders
We present both an overview and a perspective of recent experimental advances
and proposed new approaches to performing computation using memristors. A
memristor is a 2-terminal passive component with a dynamic resistance depending
on an internal parameter. We provide an brief historical introduction, as well
as an overview over the physical mechanism that lead to memristive behavior.
This review is meant to guide nonpractitioners in the field of memristive
circuits and their connection to machine learning and neural computation.Comment: Perpective paper for MDPI Technologies; 43 page
Teaching Memory Circuit Elements via Experiment-Based Learning
The class of memory circuit elements which comprises memristive,
memcapacitive, and meminductive systems, is gaining considerable attention in a
broad range of disciplines. This is due to the enormous flexibility these
elements provide in solving diverse problems in analog/neuromorphic and
digital/quantum computation; the possibility to use them in an integrated
computing-memory paradigm, massively-parallel solution of different
optimization problems, learning, neural networks, etc. The time is therefore
ripe to introduce these elements to the next generation of physicists and
engineers with appropriate teaching tools that can be easily implemented in
undergraduate teaching laboratories. In this paper, we suggest the use of
easy-to-build emulators to provide a hands-on experience for the students to
learn the fundamental properties and realize several applications of these
memelements. We provide explicit examples of problems that could be tackled
with these emulators that range in difficulty from the demonstration of the
basic properties of memristive, memcapacitive, and meminductive systems to
logic/computation and cross-bar memory. The emulators can be built from
off-the-shelf components, with a total cost of a few tens of dollars, thus
providing a relatively inexpensive platform for the implementation of these
exercises in the classroom. We anticipate that this experiment-based learning
can be easily adopted and expanded by the instructors with many more case
studies.Comment: IEEE Circuits and Systems Magazine (in press
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