121 research outputs found
SIMPEL: Circuit model for photonic spike processing laser neurons
We propose an equivalent circuit model for photonic spike processing laser
neurons with an embedded saturable absorber---a simulation model for photonic
excitable lasers (SIMPEL). We show that by mapping the laser neuron rate
equations into a circuit model, SPICE analysis can be used as an efficient and
accurate engine for numerical calculations, capable of generalization to a
variety of different laser neuron types found in literature. The development of
this model parallels the Hodgkin--Huxley model of neuron biophysics, a circuit
framework which brought efficiency, modularity, and generalizability to the
study of neural dynamics. We employ the model to study various
signal-processing effects such as excitability with excitatory and inhibitory
pulses, binary all-or-nothing response, and bistable dynamics.Comment: 16 pages, 7 figure
Optical computing by injection-locked lasers
A programmable optical computer has remained an elusive concept. To construct
a practical computing primitive equivalent to an electronic Boolean logic, one
should find a nonlinear phenomenon that overcomes weaknesses present in many
optical processing schemes. Ideally, the nonlinearity should provide a
functionally complete set of logic operations, enable ultrafast all-optical
programmability, and allow cascaded operations without a change in the
operating wavelength or in the signal encoding format. Here we demonstrate a
programmable logic gate using an injection-locked Vertical-Cavity
Surface-Emitting Laser (VCSEL). The gate program is switched between the AND
and the OR operations at the rate of 1 GHz with Bit Error Ratio (BER) of 10e-6
without changes in the wavelength or in the signal encoding format. The scheme
is based on nonlinearity of normalization operations, which can be used to
construct any continuous complex function or operation, Boolean or otherwise.Comment: 47 pages, 7 figures in total, 2 tables. Intended for submission to
Nature Physics within the next two week
Scalable wavelength-multiplexing photonic reservoir computing
Photonic reservoir computing (PRC) is a special hardware recurrent neural
network, which is featured with fast training speed and low training cost. This
work shows a wavelength-multiplexing PRC architecture, taking advantage of the
numerous longitudinal modes in a Fabry-Perot semiconductor laser. These modes
construct connected physical neurons in parallel, while an optical feedback
loop provides interactive virtual neurons in series. We experimentally
demonstrate a four-channel wavelength-multiplexing PRC, which runs four times
faster than the single-channel case. It is proved that the multiplexing PRC
exhibits superior performance on the task of signal equalization in an optical
fiber communication link. Particularly, this scheme is highly scalable owing to
the rich mode resources in Fabry-Perot lasers
Neuromorphic nanophotonic systems for artificial intelligence
Over the last decade, we have witnessed an astonishing pace of development in the field of artificial intelligence (AI), followed by proliferation of AI algorithms into virtually every domain of our society. While modern AI models boast impressive performance, they also require massive amounts of energy and resources for operation. This is further fuelling the research into AI-specific, optimised computing hardware. At the same time, the remarkable energy efficiency of the brain brings an interesting question: Can we further borrow from the working principles of biological intelligence to realise a more efficient artificial intelligence? This can be considered as the main research question in the field of neuromorphic engineering. Thanks to the developments in AI and recent advancements in the field of photonics and photonic integration, research into light-powered implementations of neuromorphic hardware has recently experienced a significant uptick of interest. In such hardware, the aim is to seize some of the highly desirable properties of photonics not just for communication, but also to perform computation. Neurons in the brain frequently process information (compute) and communicate using action potentials, which are brief voltage spikes that encode information in the temporal domain. Similar dynamical behaviour can be elicited in some photonic devices, at speeds multiple orders of magnitude higher. Such devices with the capability of neuron-like spiking are of significant research interest for the field of neuromorphic photonics. Two distinct types of such excitable, spiking systems operating with optical signals are studied and investigated in this thesis. First, a vertical cavity surface emitting laser (VCSEL) can be operated under a specific set of conditions to realise a high-speed, all-optical excitable photonic neuron that operates at standard telecom wavelengths. The photonic VCSEL-neuron was dynamically characterised and various information encoding mechanisms were studied in this device. In particular, a spiking rate-coding regime of operation was experimentally demonstrated, and its viability for performing spiking domain conversion of digital images was explored. Furthermore, for the first time, a joint architecture utilising a VCSEL-neuron coupled to a photonic integrated circuit (PIC) silicon microring weight bank was experimentally demonstrated in two different functional layouts. Second, an optoelectronic (O/E/O) circuit based upon a resonant tunnelling diode (RTD) was introduced. Two different types of RTD devices were studied experimentally:
a higher output power, µ-scale RTD that was RF coupled to an active photodetector and a VCSEL (this layout is referred to as a PRL node); and a simplified, photosensitive RTD with nanoscale injector that was RF coupled to a VCSEL (referred to as a nanopRL node). Hallmark excitable behaviours were studied in both devices, including excitability thresholding and refractory periods. Furthermore, a more exotic resonate and-fire dynamical behaviour was also reported in the nano-pRL device. Finally, a modular numerical model of the RTD was introduced, and various information processing methods were demonstrated using both a single RTD spiking node, as well as a perceptron-type spiking neural network with physical models of optoelectronic RTD nodes serving as artificial spiking neurons.Over the last decade, we have witnessed an astonishing pace of development in the field of artificial intelligence (AI), followed by proliferation of AI algorithms into virtually every domain of our society. While modern AI models boast impressive performance, they also require massive amounts of energy and resources for operation. This is further fuelling the research into AI-specific, optimised computing hardware. At the same time, the remarkable energy efficiency of the brain brings an interesting question: Can we further borrow from the working principles of biological intelligence to realise a more efficient artificial intelligence? This can be considered as the main research question in the field of neuromorphic engineering. Thanks to the developments in AI and recent advancements in the field of photonics and photonic integration, research into light-powered implementations of neuromorphic hardware has recently experienced a significant uptick of interest. In such hardware, the aim is to seize some of the highly desirable properties of photonics not just for communication, but also to perform computation. Neurons in the brain frequently process information (compute) and communicate using action potentials, which are brief voltage spikes that encode information in the temporal domain. Similar dynamical behaviour can be elicited in some photonic devices, at speeds multiple orders of magnitude higher. Such devices with the capability of neuron-like spiking are of significant research interest for the field of neuromorphic photonics. Two distinct types of such excitable, spiking systems operating with optical signals are studied and investigated in this thesis. First, a vertical cavity surface emitting laser (VCSEL) can be operated under a specific set of conditions to realise a high-speed, all-optical excitable photonic neuron that operates at standard telecom wavelengths. The photonic VCSEL-neuron was dynamically characterised and various information encoding mechanisms were studied in this device. In particular, a spiking rate-coding regime of operation was experimentally demonstrated, and its viability for performing spiking domain conversion of digital images was explored. Furthermore, for the first time, a joint architecture utilising a VCSEL-neuron coupled to a photonic integrated circuit (PIC) silicon microring weight bank was experimentally demonstrated in two different functional layouts. Second, an optoelectronic (O/E/O) circuit based upon a resonant tunnelling diode (RTD) was introduced. Two different types of RTD devices were studied experimentally:
a higher output power, µ-scale RTD that was RF coupled to an active photodetector and a VCSEL (this layout is referred to as a PRL node); and a simplified, photosensitive RTD with nanoscale injector that was RF coupled to a VCSEL (referred to as a nanopRL node). Hallmark excitable behaviours were studied in both devices, including excitability thresholding and refractory periods. Furthermore, a more exotic resonate and-fire dynamical behaviour was also reported in the nano-pRL device. Finally, a modular numerical model of the RTD was introduced, and various information processing methods were demonstrated using both a single RTD spiking node, as well as a perceptron-type spiking neural network with physical models of optoelectronic RTD nodes serving as artificial spiking neurons
Polarization Modulation in Quantum-Dot Spin-VCSELs for Ultrafast Data Transmission
Spin-Vertical Cavity Surface Emitting Lasers (spin-VCSELs) are undergoing
increasing research effort for new paradigms in high-speed optical
communications and photon-enabled computing. To date research in spin-VCSELs
has mostly focused on Quantum-Well (QW) devices. However, novel Quantum-Dot
(QD) spin-VCSELs, offer enhanced parameter controls permitting the effective,
dynamical and ultrafast manipulation of their light emissions polarization. In
the present contribution we investigate in detail the operation of QD
spin-VCSELs subject to polarization modulation for their use as ultrafast light
sources in optical communication systems. We reveal that QD spin-VCSELs
outperform their QW counterparts in terms of modulation efficiency, yielding a
nearly two-fold improvement. We also analyse the impact of key device
parameters in QD spin-VCSELs (e.g. photon decay rate and intra-dot relaxation
rate) on the large signal modulation performance with regard to optical
modulation amplitude and eye-diagram opening penalty. We show that in addition
to exhibiting enhanced polarization modulation performance for data rates up to
250Gbps, QD spin-VCSELs enable operation in dual (ground and excited state)
emission thus allowing future exciting routes for multiplexing of information
in optical communication links
Spike-based information encoding in vertical cavity surface emitting lasers for neuromorphic photonic systems
The ongoing growth of use-cases for artificial neural networks (ANNs) fuels the search for new, tailor-made ANN-optimized hardware. Neuromorphic (brain-like) computers are among the proposed highly promising solutions, with optical neuromorphic realizations recently receiving increasing research interest. Among these, photonic neuronal models based on vertical cavity surface emitting lasers (VCSELs) stand out due to their favourable properties, fast operation and mature technology. In this work, we experimentally demonstrate different strategies to encode information into ultrafast spiking events in a VCSEL-neuron. We evaluate how the strength of the input perturbations (stimuli) influences the spike activation time, allowing for spike latency input coding. Based on a study of refractory behaviour in the system, we demonstrate the capability of the VCSEL-neuron to perform reliable binary-to-spike information coding with spiking rates surpassing 1 GHz. We also report experimentally on neuro-inspired spike firing rate-coding with a VCSEL-neuron, where the strength of the input perturbation (stimulus) is continuously encoded into the spiking frequency (spike firing rate). With the prospects of neuromorphic photonic systems constantly growing, we believe the reported functionalities with the ultrafast spiking VCSEL-neurons provide a reliable, multifaceted approach for interfacing photonic neuromorphic platforms with existing computation and communication systems
GHz rate neuromorphic photonic spiking neural network with a single Vertical-Cavity Surface-Emitting Laser (VCSEL)
Vertical-Cavity Surface-Emitting Lasers (VCSELs) are highly promising devices for the construction of neuro- morphic photonic information processing systems, due to their numerous desirable properties such as low power consumption, high modulation speed, and compactness. Of particular interest is the ability of VCSELs to exhibit neuron-like spiking responses at ultrafast sub-nanosecond rates; thus offering great prospects for high-speed light-enabled spike-based processors. Recent works have shown spiking VCSELs are capable of pattern recognition and image processing problems, but additionally, VCSELs have been used as nonlinear elements in photonic reservoir comput- ing (RC) implementations, yielding state of the art operation. This work introduces and experimentally demonstrates for the first time a new GHz-rate photonic spiking neural network (SNN) built with a single VCSEL neuron. The reported system effectively implements a photonic VCSEL-based spiking reser- voir computer, and demonstrates its successful application to a complex nonlinear classification task. Importantly, the proposed system benefits from a highly hardware-friendly, inexpensive realization (a single VCSEL device and off-the-shelf fibre-optic components), for high-speed (GHz-rate inputs) and low-power (sub-mW optical input power) photonic operation. These results open new pathways towards future neuromorphic photonic spike- based processing systems based upon VCSELs (or other laser types) for novel ultrafast machine learning and AI hardware
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