20 research outputs found
A neuromorphic silicon photonics nonlinear equalizer for optical communications with intensity modulation and direct detection
We present the design and numerical study of a nonlinear equalizer for optical communications based on silicon photonics and reservoir computing. The proposed equalizer leverages the optical information processing capabilities of integrated photonic reservoirs to combat distortions both in metro links of a few hundred kilometers and in high-speed short-reach intensity-modulation-direct-detection links. We show nonlinear compensation in unrepeated metro links of up to 200 km that outperform electrical feedforward equalizers based equalizers, and ultimately any linear compensation device. For a high-speed short-reach 40Gb/s link based on a distributed feedback laser and an electroabsorptive modulator, and considering a hard decision forward error correction limit of 0.2 x 10(-2), we can increase the reach by almost 10 km. Our equalizer is compact (only 16 nodes) and operates in the optical domain without the need for complex electronic DSP, meaning its performance is not bandwidth constrained. The approach is, therefore, a viable candidate even for equalization techniques far beyond 100G optical communication links
Photonic Neural Networks and Optics-informed Deep Learning Fundamentals
The recent explosive compute growth, mainly fueled by the boost of AI and
DNNs, is currently instigating the demand for a novel computing paradigm that
can overcome the insurmountable barriers imposed by conventional electronic
computing architectures. PNNs implemented on silicon integration platforms
stand out as a promising candidate to endow NN hardware, offering the potential
for energy efficient and ultra-fast computations through the utilization of the
unique primitives of photonics i.e. energy efficiency, THz bandwidth and
low-latency. Thus far, several demonstrations have revealed the huge potential
of PNNs in performing both linear and non-linear NN operations at unparalleled
speed and energy consumption metrics. Transforming this potential into a
tangible reality for DL applications requires, however, a deep understanding of
the basic PNN principles, requirements and challenges across all constituent
architectural, technological and training aspects. In this tutorial, we,
initially, review the principles of DNNs along with their fundamental building
blocks, analyzing also the key mathematical operations needed for their
computation in a photonic hardware. Then, we investigate, through an intuitive
mathematical analysis, the interdependence of bit precision and energy
efficiency in analog photonic circuitry, discussing the opportunities and
challenges of PNNs. Followingly, a performance overview of PNN architectures,
weight technologies and activation functions is presented, summarizing their
impact in speed, scalability and power consumption. Finally, we provide an
holistic overview of the optics-informed NN training framework that
incorporates the physical properties of photonic building blocks into the
training process in order to improve the NN classification accuracy and
effectively elevate neuromorphic photonic hardware into high-performance DL
computational settings
Multidimensional fiber echo state network analogue
Abstract: Optical neuoromorphic technologies enable neural network-based signal processing through a specifically designed hardware and may confer advantages in speed and energy. However, the advances of such technologies in bandwidth and/or dimensionality are often limited by the constraints of the underlying material. Optical fiber presents a well-studied low-cost solution with unique advantages for low-loss high-speed signal processing. The fiber echo state network analogue (FESNA), fiber-based neuromorphic processor, has been the first technology suitable for multichannel high bandwidth (including THz) and dual-quadrature signal processing. Here we propose the multidimensional FESNA (MD-FESNA) processing by utilizing multi-mode fiber non-linearity. Thus, the developed MD-FESNA is the first neuromorphic technology which augments all aforementioned advantages of FESNA with multidimensional spatio-temporal processing. We demonstrate the performance and flexibility of the technology on the example of prediction tasks for hyperchaotic systems. These results will pave the way for a high-speed neuromorphic processing of multidimensional tasks, hardware for spatio-temporal neural networks and open new application venues for fiber-based spatio-temporal multiplexing
Self-Similar Nanocavity Design with Ultrasmall Mode Volume for Single-Photon Nonlinearities
United States. Air Force Office of Scientific Research (FA8750-13-2-0120
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
High-performance end-to-end deep learning IM/DD link using optics-informed neural networks
: In this paper, we introduce optics-informed Neural Networks and demonstrate experimentally how they can improve performance of End-to-End deep learning models for IM/DD optical transmission links. Optics-informed or optics-inspired NNs are defined as the type of DL models that rely on linear and/or nonlinear building blocks whose mathematical description stems directly from the respective response of photonic devices, drawing their mathematical framework from neuromorphic photonic hardware developments and properly adapting their DL training algorithms. We investigate the application of an optics-inspired activation function that can be obtained by a semiconductor-based nonlinear optical module and is a variant of the logistic sigmoid, referred to as the Photonic Sigmoid, in End-to-End Deep Learning configurations for fiber communication links. Compared to state-of-the-art ReLU-based configurations used in End-to-End DL fiber link demonstrations, optics-informed models based on the Photonic Sigmoid show improved noise- and chromatic dispersion compensation properties in fiber-optic IM/DD links. An extensive simulation and experimental analysis revealed significant performance benefits for the Photonic Sigmoid NNs that can reach below BER HD FEC limit for fiber lengths up to 42 km, at an effective bit transmission rate of 48 Gb/s
Equalization of a 10 Gbps IMDD signal by a small silicon photonics time delayed neural network
A small 4-channels time-delayed complex perceptron is used as a silicon
photonics neural network (NN) device to compensate for chromatic dispersion in
optical fiber links. The NN device is experimentally tested with
non-return-to-zero optical signals at 10 Gbps after propagation through up to
125 km optical fiber link. During the learning phase, a separation-loss
function is optimized in order to maximally separate the transmitted levels of
0s from the 1s, which implies an optimization of the bit-error-rate. Testing of
the NN device shows that the excess losses introduced by the NN device are
compensated by the gain in transmitted signal equalization for a link longer
than 100 km. The measured data are reproduced by a model which accounts for the
optical link and the neural network device. This allows simulating the network
performances for higher data rates, where the device shows improvement with
respect to the benchmark both in terms of performance as well as ease of use.Comment: 14 pages, 6 figure