2 research outputs found
Training and Operation of an Integrated Neuromorphic Network Based on Metal-Oxide Memristors
Despite all the progress of semiconductor integrated circuit technology, the
extreme complexity of the human cerebral cortex makes the hardware
implementation of neuromorphic networks with a comparable number of devices
exceptionally challenging. One of the most prospective candidates to provide
comparable complexity, while operating much faster and with manageable power
dissipation, are so-called CrossNets based on hybrid CMOS/memristor circuits.
In these circuits, the usual CMOS stack is augmented with one or several
crossbar layers, with adjustable two-terminal memristors at each crosspoint.
Recently, there was a significant progress in improvement of technology of
fabrication of such memristive crossbars and their integration with CMOS
circuits, including first demonstrations of their vertical integration.
Separately, there have been several demonstrations of discrete memristors as
artificial synapses for neuromorphic networks. Very recently such experiments
were extended to crossbar arrays of phase-change memristive devices. The
adjustment of such devices, however, requires an additional transistor at each
crosspoint, and hence the prospects of their scaling are less impressive than
those of metal-oxide memristors, whose nonlinear I-V curves enable
transistor-free operation. Here we report the first experimental implementation
of a transistor-free metal-oxide memristor crossbar with device variability
lowered sufficiently to demonstrate a successful operation of a simple
integrated neural network, a single layer-perceptron. The network could be
taught in situ using a coarse-grain variety of the delta-rule algorithm to
perform the perfect classification of 3x3-pixel black/white images into 3
classes. We believe that this demonstration is an important step towards the
implementation of much larger and more complex memristive neuromorphic
networks.Comment: 21 pages, 12 figure
A Survey of Neuromorphic Computing and Neural Networks in Hardware
Neuromorphic computing has come to refer to a variety of brain-inspired
computers, devices, and models that contrast the pervasive von Neumann computer
architecture. This biologically inspired approach has created highly connected
synthetic neurons and synapses that can be used to model neuroscience theories
as well as solve challenging machine learning problems. The promise of the
technology is to create a brain-like ability to learn and adapt, but the
technical challenges are significant, starting with an accurate neuroscience
model of how the brain works, to finding materials and engineering
breakthroughs to build devices to support these models, to creating a
programming framework so the systems can learn, to creating applications with
brain-like capabilities. In this work, we provide a comprehensive survey of the
research and motivations for neuromorphic computing over its history. We begin
with a 35-year review of the motivations and drivers of neuromorphic computing,
then look at the major research areas of the field, which we define as
neuro-inspired models, algorithms and learning approaches, hardware and
devices, supporting systems, and finally applications. We conclude with a broad
discussion on the major research topics that need to be addressed in the coming
years to see the promise of neuromorphic computing fulfilled. The goals of this
work are to provide an exhaustive review of the research conducted in
neuromorphic computing since the inception of the term, and to motivate further
work by illuminating gaps in the field where new research is needed