2,433 research outputs found
Wavelength-multiplexed duplex transceiver based on III-V/Si hybrid integration for off-chip and on-chip optical interconnects
A six-channel wavelength-division-multiplexed optical transceiver with a compact footprint of 1.5 x 0.65 mm(2) for off-chip and on-chip interconnects is demonstrated on a single silicon-on-insulator chip. An arrayed waveguide grating is used as the (de)multiplexer, and III-V electroabsorption sections fabricated by hybrid integration technology are used as both modulators and detectors, which also enable duplex links. The 30-Gb/s capacity for each of the six wavelength channels for the off-chip transceiver is demonstrated. For the on-chip interconnect, an electrical-to-electrical 3-dB bandwidth of 13 GHz and a data rate of 30 Gb/s per wavelength are achieved
PCNNA: A Photonic Convolutional Neural Network Accelerator
Convolutional Neural Networks (CNN) have been the centerpiece of many
applications including but not limited to computer vision, speech processing,
and Natural Language Processing (NLP). However, the computationally expensive
convolution operations impose many challenges to the performance and
scalability of CNNs. In parallel, photonic systems, which are traditionally
employed for data communication, have enjoyed recent popularity for data
processing due to their high bandwidth, low power consumption, and
reconfigurability. Here we propose a Photonic Convolutional Neural Network
Accelerator (PCNNA) as a proof of concept design to speedup the convolution
operation for CNNs. Our design is based on the recently introduced silicon
photonic microring weight banks, which use broadcast-and-weight protocol to
perform Multiply And Accumulate (MAC) operation and move data through layers of
a neural network. Here, we aim to exploit the synergy between the inherent
parallelism of photonics in the form of Wavelength Division Multiplexing (WDM)
and sparsity of connections between input feature maps and kernels in CNNs.
While our full system design offers up to more than 3 orders of magnitude
speedup in execution time, its optical core potentially offers more than 5
order of magnitude speedup compared to state-of-the-art electronic
counterparts.Comment: 5 Pages, 6 Figures, IEEE SOCC 201
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
Hardware-algorithm collaborative computing with photonic spiking neuron chip based on integrated Fabry-P\'erot laser with saturable absorber
Photonic neuromorphic computing has emerged as a promising avenue toward
building a low-latency and energy-efficient non-von-Neuman computing system.
Photonic spiking neural network (PSNN) exploits brain-like spatiotemporal
processing to realize high-performance neuromorphic computing. However, the
nonlinear computation of PSNN remains a significant challenging. Here, we
proposed and fabricated a photonic spiking neuron chip based on an integrated
Fabry-P\'erot laser with a saturable absorber (FP-SA) for the first time. The
nonlinear neuron-like dynamics including temporal integration, threshold and
spike generation, refractory period, and cascadability were experimentally
demonstrated, which offers an indispensable fundamental building block to
construct the PSNN hardware. Furthermore, we proposed time-multiplexed spike
encoding to realize functional PSNN far beyond the hardware integration scale
limit. PSNNs with single/cascaded photonic spiking neurons were experimentally
demonstrated to realize hardware-algorithm collaborative computing, showing
capability in performing classification tasks with supervised learning
algorithm, which paves the way for multi-layer PSNN for solving complex tasks.Comment: 10 pages, 8 figure
Monolithic integration of erbium-doped amplifiers with silicon-on-insulator waveguides
Monolithic integration of Al2O3:Er3+ amplifier technology with passive silicon-on-insulator waveguides is demonstrated. A signal enhancement of >7 dB at 1533 nm wavelength is obtained. The straightforward wafer-scale fabrication process, which includes reactive co-sputtering and subsequent reactive ion etching, allows for parallel integration of multiple amplifier and laser sections with silicon or other photonic circuits on a chip
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