6,014 research outputs found
CMOL: Second Life for Silicon?
This report is a brief review of the recent work on architectures for the
prospective hybrid CMOS/nanowire/ nanodevice ("CMOL") circuits including
digital memories, reconfigurable Boolean-logic circuits, and mixed-signal
neuromorphic networks. The basic idea of CMOL circuits is to combine the
advantages of CMOS technology (including its flexibility and high fabrication
yield) with the extremely high potential density of molecular-scale
two-terminal nanodevices. Relatively large critical dimensions of CMOS
components and the "bottom-up" approach to nanodevice fabrication may keep CMOL
fabrication costs at affordable level. At the same time, the density of active
devices in CMOL circuits may be as high as 1012 cm2 and that they may provide
an unparalleled information processing performance, up to 1020 operations per
cm2 per second, at manageable power consumption.Comment: Submitted on behalf of TIMA Editions
(http://irevues.inist.fr/tima-editions
Homogeneous Spiking Neuromorphic System for Real-World Pattern Recognition
A neuromorphic chip that combines CMOS analog spiking neurons and memristive
synapses offers a promising solution to brain-inspired computing, as it can
provide massive neural network parallelism and density. Previous hybrid analog
CMOS-memristor approaches required extensive CMOS circuitry for training, and
thus eliminated most of the density advantages gained by the adoption of
memristor synapses. Further, they used different waveforms for pre and
post-synaptic spikes that added undesirable circuit overhead. Here we describe
a hardware architecture that can feature a large number of memristor synapses
to learn real-world patterns. We present a versatile CMOS neuron that combines
integrate-and-fire behavior, drives passive memristors and implements
competitive learning in a compact circuit module, and enables in-situ
plasticity in the memristor synapses. We demonstrate handwritten-digits
recognition using the proposed architecture using transistor-level circuit
simulations. As the described neuromorphic architecture is homogeneous, it
realizes a fundamental building block for large-scale energy-efficient
brain-inspired silicon chips that could lead to next-generation cognitive
computing.Comment: This is a preprint of an article accepted for publication in IEEE
Journal on Emerging and Selected Topics in Circuits and Systems, vol 5, no.
2, June 201
Perspective: Organic electronic materials and devices for neuromorphic engineering
Neuromorphic computing and engineering has been the focus of intense research
efforts that have been intensified recently by the mutation of Information and
Communication Technologies (ICT). In fact, new computing solutions and new
hardware platforms are expected to emerge to answer to the new needs and
challenges of our societies. In this revolution, lots of candidates
technologies are explored and will require leveraging of the pro and cons. In
this perspective paper belonging to the special issue on neuromorphic
engineering of Journal of Applied Physics, we focus on the current achievements
in the field of organic electronics and the potentialities and specificities of
this research field. We highlight how unique material features available
through organic materials can be used to engineer useful and promising
bioinspired devices and circuits. We also discuss about the opportunities that
organic electronic are offering for future research directions in the
neuromorphic engineering field
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