438 research outputs found
On-chip passive photonic reservoir computing with integrated optical readout
Photonic reservoir computing is a recent bio-inspired paradigm for signal processing. Despite first successes, the paradigm still faces challenges. We address some of these challenges and introduce our approaches to solve them. In detail, we discuss how integrated reservoirs can be scaled up by injecting multiple copies of the input. Further we introduce a new hardware-friendly training method for integrated optical readouts
Training Passive Photonic Reservoirs with Integrated Optical Readout
As Moore's law comes to an end, neuromorphic approaches to computing are on
the rise. One of these, passive photonic reservoir computing, is a strong
candidate for computing at high bitrates (> 10 Gbps) and with low energy
consumption. Currently though, both benefits are limited by the necessity to
perform training and readout operations in the electrical domain. Thus, efforts
are currently underway in the photonic community to design an integrated
optical readout, which allows to perform all operations in the optical domain.
In addition to the technological challenge of designing such a readout, new
algorithms have to be designed in order to train it. Foremost, suitable
algorithms need to be able to deal with the fact that the actual on-chip
reservoir states are not directly observable. In this work, we investigate
several options for such a training algorithm and propose a solution in which
the complex states of the reservoir can be observed by appropriately setting
the readout weights, while iterating over a predefined input sequence. We
perform numerical simulations in order to compare our method with an ideal
baseline requiring full observability as well as with an established black-box
optimization approach (CMA-ES).Comment: Accepted for publication in IEEE Transactions on Neural Networks and
Learning Systems (TNNLS-2017-P-8539.R1), copyright 2018 IEEE. This research
was funded by the EU Horizon 2020 PHRESCO Grant (Grant No. 688579) and the
BELSPO IAP P7-35 program Photonics@be. 11 pages, 9 figure
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
Nanophotonic reservoir computing with photonic crystal cavities to generate periodic patterns
Reservoir computing (RC) is a technique in machine learning inspired by neural systems. RC has been used successfully to solve complex problems such as signal classification and signal generation. These systems are mainly implemented in software, and thereby they are limited in speed and power efficiency. Several optical and optoelectronic implementations have been demonstrated, in which the system has signals with an amplitude and phase. It is proven that these enrich the dynamics of the system, which is beneficial for the performance. In this paper, we introduce a novel optical architecture based on nanophotonic crystal cavities. This allows us to integrate many neurons on one chip, which, compared with other photonic solutions, closest resembles a classical neural network. Furthermore, the components are passive, which simplifies the design and reduces the power consumption. To assess the performance of this network, we train a photonic network to generate periodic patterns, using an alternative online learning rule called first-order reduced and corrected error. For this, we first train a classical hyperbolic tangent reservoir, but then we vary some of the properties to incorporate typical aspects of a photonics reservoir, such as the use of continuous-time versus discrete-time signals and the use of complex-valued versus real-valued signals. Then, the nanophotonic reservoir is simulated and we explore the role of relevant parameters such as the topology, the phases between the resonators, the number of nodes that are biased and the delay between the resonators. It is important that these parameters are chosen such that no strong self-oscillations occur. Finally, our results show that for a signal generation task a complex-valued, continuous-time nanophotonic reservoir outperforms a classical (i.e., discrete-time, real-valued) leaky hyperbolic tangent reservoir (normalized root-mean-square errors = 0.030 versus NRMSE = 0.127)
Integrated Photonic Reservoir Computing with All-Optical Readout
Integrated photonic reservoir computing has been demonstrated to be able to
tackle different problems because of its neural network nature. A key advantage
of photonic reservoir computing over other neuromorphic paradigms is its
straightforward readout system, which facilitates both rapid training and
robust, fabrication variation-insensitive photonic integrated hardware
implementation for real-time processing. We present our recent development of a
fully-optical, coherent photonic reservoir chip integrated with an optical
readout system, capitalizing on these benefits. Alongside the integrated
system, we also demonstrate a weight update strategy that is suitable for the
integrated optical readout hardware. Using this online training scheme, we
successfully solved 3-bit header recognition and delayed XOR tasks at 20 Gbps
in real-time, all within the optical domain without excess delays
Photonic neuromorphic information processing and reservoir computing
Photonic neuromorphic computing is attracting tremendous research interest now, catalyzed in no small part by the rise of deep learning in many applications. In this paper, we will review some of the exciting work that has been going in this area and then focus on one particular technology, namely, photonic reservoir computing
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