6,599 research outputs found
Experimental Demonstrations of Optical Neural Computers
We describe two experiments in optical neural computing. In the first a closed optical feedback loop is used to implement auto-associative image
recall. In the second a perceptron-like learning algorithm is implemented with photorefractive holography
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
Optoelectronic Reservoir Computing
Reservoir computing is a recently introduced, highly efficient bio-inspired
approach for processing time dependent data. The basic scheme of reservoir
computing consists of a non linear recurrent dynamical system coupled to a
single input layer and a single output layer. Within these constraints many
implementations are possible. Here we report an opto-electronic implementation
of reservoir computing based on a recently proposed architecture consisting of
a single non linear node and a delay line. Our implementation is sufficiently
fast for real time information processing. We illustrate its performance on
tasks of practical importance such as nonlinear channel equalization and speech
recognition, and obtain results comparable to state of the art digital
implementations.Comment: Contains main paper and two Supplementary Material
NASA'S information technology activities for the 90's
The Office of Aeronautics, Exploration and Technology (OAET) is completing an extensive assessment of its nearly five hundred million dollars of proposed space technology development work. The budget is divided into four segments which are as follows: (1) the base research and technology program; (2) the Civil Space Technology Initiative (CSTI); (3) the Exploration Technology Program (ETP); and (4) the High Performance Computing Initiative (HPCI). The programs are briefly discussed in the context of Astrotech 21
Programming multi-level quantum gates in disordered computing reservoirs via machine learning and TensorFlow
Novel machine learning computational tools open new perspectives for quantum
information systems. Here we adopt the open-source programming library
TensorFlow to design multi-level quantum gates including a computing reservoir
represented by a random unitary matrix. In optics, the reservoir is a
disordered medium or a multi-modal fiber. We show that trainable operators at
the input and the readout enable one to realize multi-level gates. We study
various qudit gates, including the scaling properties of the algorithms with
the size of the reservoir. Despite an initial low slop learning stage,
TensorFlow turns out to be an extremely versatile resource for designing gates
with complex media, including different models that use spatial light
modulators with quantized modulation levels.Comment: Added a new section and a new figure about implementation of the
gates by a single spatial light modulator. 9 pages and 4 figure
Gaussian Optical Ising Machines
It has recently been shown that optical parametric oscillator (OPO) Ising
machines, consisting of coupled optical pulses circulating in a cavity with
parametric gain, can be used to probabilistically find low-energy states of
Ising spin systems. In this work, we study optical Ising machines that operate
under simplified Gaussian dynamics. We show that these dynamics are sufficient
for reaching probabilities of success comparable to previous work. Based on
this result, we propose modified optical Ising machines with simpler designs
that do not use parametric gain yet achieve similar performance, thus
suggesting a route to building much larger systems.Comment: 6 page
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