5,395 research outputs found
Analog readout for optical reservoir computers
Reservoir computing is a new, powerful and flexible machine learning
technique that is easily implemented in hardware. Recently, by using a
time-multiplexed architecture, hardware reservoir computers have reached
performance comparable to digital implementations. Operating speeds allowing
for real time information operation have been reached using optoelectronic
systems. At present the main performance bottleneck is the readout layer which
uses slow, digital postprocessing. We have designed an analog readout suitable
for time-multiplexed optoelectronic reservoir computers, capable of working in
real time. The readout has been built and tested experimentally on a standard
benchmark task. Its performance is better than non-reservoir methods, with
ample room for further improvement. The present work thereby overcomes one of
the major limitations for the future development of hardware reservoir
computers.Comment: to appear in NIPS 201
High performance photonic reservoir computer based on a coherently driven passive cavity
Reservoir computing is a recent bio-inspired approach for processing
time-dependent signals. It has enabled a breakthrough in analog information
processing, with several experiments, both electronic and optical,
demonstrating state-of-the-art performances for hard tasks such as speech
recognition, time series prediction and nonlinear channel equalization. A
proof-of-principle experiment using a linear optical circuit on a photonic chip
to process digital signals was recently reported. Here we present a photonic
implementation of a reservoir computer based on a coherently driven passive
fiber cavity processing analog signals. Our experiment has error rate as low or
lower than previous experiments on a wide variety of tasks, and also has lower
power consumption. Furthermore, the analytical model describing our experiment
is also of interest, as it constitutes a very simple high performance reservoir
computer algorithm. The present experiment, given its good performances, low
energy consumption and conceptual simplicity, confirms the great potential of
photonic reservoir computing for information processing applications ranging
from artificial intelligence to telecommunicationsComment: non
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
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
Photonic Delay Systems as Machine Learning Implementations
Nonlinear photonic delay systems present interesting implementation platforms
for machine learning models. They can be extremely fast, offer great degrees of
parallelism and potentially consume far less power than digital processors. So
far they have been successfully employed for signal processing using the
Reservoir Computing paradigm. In this paper we show that their range of
applicability can be greatly extended if we use gradient descent with
backpropagation through time on a model of the system to optimize the input
encoding of such systems. We perform physical experiments that demonstrate that
the obtained input encodings work well in reality, and we show that optimized
systems perform significantly better than the common Reservoir Computing
approach. The results presented here demonstrate that common gradient descent
techniques from machine learning may well be applicable on physical
neuro-inspired analog computers
Large-Scale Optical Neural Networks based on Photoelectric Multiplication
Recent success in deep neural networks has generated strong interest in
hardware accelerators to improve speed and energy consumption. This paper
presents a new type of photonic accelerator based on coherent detection that is
scalable to large () networks and can be operated at high (GHz)
speeds and very low (sub-aJ) energies per multiply-and-accumulate (MAC), using
the massive spatial multiplexing enabled by standard free-space optical
components. In contrast to previous approaches, both weights and inputs are
optically encoded so that the network can be reprogrammed and trained on the
fly. Simulations of the network using models for digit- and
image-classification reveal a "standard quantum limit" for optical neural
networks, set by photodetector shot noise. This bound, which can be as low as
50 zJ/MAC, suggests performance below the thermodynamic (Landauer) limit for
digital irreversible computation is theoretically possible in this device. The
proposed accelerator can implement both fully-connected and convolutional
networks. We also present a scheme for back-propagation and training that can
be performed in the same hardware. This architecture will enable a new class of
ultra-low-energy processors for deep learning.Comment: Text: 10 pages, 5 figures, 1 table. Supplementary: 8 pages, 5,
figures, 2 table
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