7,601 research outputs found
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
Anomalous tag diffusion in the asymmetric exclusion model with particles of arbitrary sizes
Anomalous behavior of correlation functions of tagged particles are studied
in generalizations of the one dimensional asymmetric exclusion problem. In
these generalized models the range of the hard-core interactions are changed
and the restriction of relative ordering of the particles is partially brocken.
The models probing these effects are those of biased diffusion of particles
having size S=0,1,2,..., or an effective negative "size" S=-1,-2,..., in units
of lattice space. Our numerical simulations show that irrespective of the range
of the hard-core potential, as long some relative ordering of particles are
kept, we find suitable sliding-tag correlation functions whose fluctuations
growth with time anomalously slow (), when compared with the normal
diffusive behavior (). These results indicate that the critical
behavior of these stochastic models are in the Kardar-Parisi-Zhang (KPZ)
universality class. Moreover a previous Bethe-ansatz calculation of the
dynamical critical exponent , for size particles is extended to
the case and the KPZ result is predicted for all values of .Comment: 4 pages, 3 figure
Implementation of a Space Communications Cognitive Engine
Although communications-based cognitive engines have been proposed, very few have been implemented in a full system, especially in a space communications system. In this paper, we detail the implementation of a multi-objective reinforcement-learning algorithm and deep artificial neural networks for the use as a radio-resource-allocation controller. The modular software architecture presented encourages re-use and easy modification for trying different algorithms. Various trade studies involved with the system implementation and integration are discussed. These include the choice of software libraries that provide platform flexibility and promote reusability, choices regarding the deployment of this cognitive engine within a system architecture using the DVB-S2 standard and commercial hardware, and constraints placed on the cognitive engine caused by real-world radio constraints. The implemented radio-resource allocation-management controller was then integrated with the larger spaceground system developed by NASA Glenn Research Center (GRC)
Finite group actions on reductive groups and buildings and tamely-ramified descent in Bruhat-Tits theory
The purpose of the paper is to give a new approach to tamely-ramified descent
in Bruhat-Tits theory. This descent was first studied by Guy Rousseau in his
thesis.Comment: 28 pages. arXiv admin note: text overlap with arXiv:1611.0743
A system-on-chip microwave photonic processor solves dynamic RF interference in real time with picosecond latency
Radio-frequency interference is a growing concern as wireless technology
advances, with potentially life-threatening consequences like interference
between radar altimeters and 5G cellular networks. Mobile transceivers mix
signals with varying ratios over time, posing challenges for conventional
digital signal processing (DSP) due to its high latency. These challenges will
worsen as future wireless technologies adopt higher carrier frequencies and
data rates. However, conventional DSPs, already on the brink of their clock
frequency limit, are expected to offer only marginal speed advancements. This
paper introduces a photonic processor to address dynamic interference through
blind source separation (BSS). Our system-on-chip processor employs a fully
integrated photonic signal pathway in the analogue domain, enabling rapid
demixing of received mixtures and recovering the signal-of-interest in under 15
picoseconds. This reduction in latency surpasses electronic counterparts by
more than three orders of magnitude. To complement the photonic processor,
electronic peripherals based on field-programmable gate array (FPGA) assess the
effectiveness of demixing and continuously update demixing weights at a rate of
up to 305 Hz. This compact setup features precise dithering weight control,
impedance-controlled circuit board and optical fibre packaging, suitable for
handheld and mobile scenarios. We experimentally demonstrate the processor's
ability to suppress transmission errors and maintain signal-to-noise ratios in
two scenarios, radar altimeters and mobile communications. This work pioneers
the real-time adaptability of integrated silicon photonics, enabling online
learning and weight adjustments, and showcasing practical operational
applications for photonic processing
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