7,538 research outputs found
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
Limits on Fundamental Limits to Computation
An indispensable part of our lives, computing has also become essential to
industries and governments. Steady improvements in computer hardware have been
supported by periodic doubling of transistor densities in integrated circuits
over the last fifty years. Such Moore scaling now requires increasingly heroic
efforts, stimulating research in alternative hardware and stirring controversy.
To help evaluate emerging technologies and enrich our understanding of
integrated-circuit scaling, we review fundamental limits to computation: in
manufacturing, energy, physical space, design and verification effort, and
algorithms. To outline what is achievable in principle and in practice, we
recall how some limits were circumvented, compare loose and tight limits. We
also point out that engineering difficulties encountered by emerging
technologies may indicate yet-unknown limits.Comment: 15 pages, 4 figures, 1 tabl
Skyrmion Gas Manipulation for Probabilistic Computing
The topologically protected magnetic spin configurations known as skyrmions
offer promising applications due to their stability, mobility and localization.
In this work, we emphasize how to leverage the thermally driven dynamics of an
ensemble of such particles to perform computing tasks. We propose a device
employing a skyrmion gas to reshuffle a random signal into an uncorrelated copy
of itself. This is demonstrated by modelling the ensemble dynamics in a
collective coordinate approach where skyrmion-skyrmion and skyrmion-boundary
interactions are accounted for phenomenologically. Our numerical results are
used to develop a proof-of-concept for an energy efficient
() device with a low area imprint ().
Whereas its immediate application to stochastic computing circuit designs will
be made apparent, we argue that its basic functionality, reminiscent of an
integrate-and-fire neuron, qualifies it as a novel bio-inspired building block.Comment: 41 pages, 20 figure
Accelerate & Actualize: Can 2D Materials Bridge the Gap Between Neuromorphic Hardware and the Human Brain?
Two-dimensional (2D) materials present an exciting opportunity for devices
and systems beyond the von Neumann computing architecture paradigm due to their
diversity of electronic structure, physical properties, and atomically-thin,
van der Waals structures that enable ease of integration with conventional
electronic materials and silicon-based hardware. All major classes of
non-volatile memory (NVM) devices have been demonstrated using 2D materials,
including their operation as synaptic devices for applications in neuromorphic
computing hardware. Their atomically-thin structure, superior physical
properties, i.e., mechanical strength, electrical and thermal conductivity, as
well as gate-tunable electronic properties provide performance advantages and
novel functionality in NVM devices and systems. However, device performance and
variability as compared to incumbent materials and technology remain major
concerns for real applications. Ultimately, the progress of 2D materials as a
novel class of electronic materials and specifically their application in the
area of neuromorphic electronics will depend on their scalable synthesis in
thin-film form with desired crystal quality, defect density, and phase purity.Comment: Neuromorphic Computing, 2D Materials, Heterostructures, Emerging
Memory Devices, Resistive, Phase-Change, Ferroelectric, Ferromagnetic,
Crossbar Array, Machine Learning, Deep Learning, Spiking Neural Network
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
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