Excitable laser processing network node in hybrid silicon: analysis and simulation

Nahmias, Mitchell A.; Tait, Alexander N.; Shastri, Bhavin J.; Lima, Thomas Ferreira de; Prucnal, Paul R.

doi:10.1364/oe.23.026800

Cited by 42 publications

(22 citation statements)

References 38 publications

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“…The spiking behavior of excitable photonic devices, which can be implemented by semiconductor technology, resembles the properties of biological neurons [39][40][41][42][43][44]. Networks of such excitable, nanophotonic devices would therefore correspond to a neural network implementation very close to their biological inspiration.…”

Section: Excitable Photonic Devices For Rcmentioning

confidence: 99%

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Advances in photonic reservoir computing

2017

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Abstract:We review a novel paradigm that has emerged in analogue neuromorphic optical computing. The goal is to implement a reservoir computer in optics, where information is encoded in the intensity and phase of the optical field. Reservoir computing is a bio-inspired approach especially suited for processing time-dependent information. The reservoir's complex and high-dimensional transient response to the input signal is capable of universal computation. The reservoir does not need to be trained, which makes it very well suited for optics. As such, much of the promise of photonic reservoirs lies in their minimal hardware requirements, a tremendous advantage over other hardware-intensive neural network models. We review the two main approaches to optical reservoir computing: networks implemented with multiple discrete optical nodes and the continuous system of a single nonlinear device coupled to delayed feedback.

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Section: Excitable Photonic Devices For Rcmentioning

confidence: 99%

“…When biased adequately close to a stability threshold, a laser with a saturable absorber becomes an excitable system [39,41,43,44]. In their dynamical behavior, these devices approximate the integrate and fire behavior of biological neurons with remarkable quality.…”

Section: Excitable Photonic Devices For Rcmentioning

confidence: 99%

Advances in photonic reservoir computing

2017

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“…We recently [38,39] reviewed the recent surge of interest [52,69,[72][73][74][75][76][77][78][79][80][81][82][83][84][85][86][87][88][89][90] in the information processing abilities of semiconductor devices that exploit the dynamical isomorphism between semiconductor photocarriers and neuron biophysics. Many of these proposals for "photonic neurons" or "laser neurons" or "optical neurons" for spike processing are based on lasers operating in an excitable regime ( Fig.…”

Section: Photonic Spike Processingmentioning

confidence: 99%

Principles of Neuromorphic Photonics

Shastri¹,

Tait²,

Lima³

et al. 2018

Unconventional Computing

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GLOSSARYBenchmark A standardized task that can be performed by disparate computing approaches, used to assess their relative processing merit in specific cases. Bifurcation A qualitative change in behavior of a dynamical system in response to parameter variation. Examples include cusp (from monostable to bistable), Hopf (from stable to oscillating), transcritical (exchange of stability between two steady states). Brain-inspired computing (a.k.a. neuro-inspired computing) A biologically inspired approach to build processors, devices, and computing models for applications including adaptive control, machine learning, and cognitive radio. Similarities with biological signal processing include architectural, such as distributed, representational, such as analog or spiking, or algorithmic, such as adaptation. Broadcast and Weight A multi-wavelength analog networking protocol in which multiple all photonic neuron outputs are multiplexed and distributed to all neuron inputs. Weights are reconfigured by tunable spectral filters. Excitability A far-from-equilibrium nonlinear dynamical mechanism underlying all-or-none responses to small perturbations. Fan-in The number of inputs to a neuron. Layered network A network topology consisting of a series of sets (i.e., layers) of neurons. The neurons in each set project their outputs only to neurons in the subsequent layer. Most commonly used type of network used for machine learning. Metric A quantity assessing performance of a device in reference to a specific computing approach. Microring weight bank A silicon photonic implementation of a reconfigurable spectral filter capable of independently setting transmission at multiple carrier wavelengths. Modulation The act of representing an abstract variable in a physical quantity, such as photon rate (i.e., optical power), free carrier density (i.e., optical gain), carrier drift (i.e., current). Electrooptic modulators are devices that convert from an electrical signal to the power envelope of an optical signal. Recently, 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 neuromorphic photonics (Fig. 1). 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.tion processing, describe the photonic neural-network approaches being developed by our lab and others, and photonic integrated circuit (PIC) platforms, which have recently undergone rapid growth. PICs are becoming a

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“…Cascadability has been proposed and numerically demonstrated in both optically [44,66] and electrically [39,58,60] injected lasers. Cascadability in optically injected PNNs presents a challenge because optical interference is sensitive to optical phase noise.…”

Section: Cascadabilitymentioning

confidence: 99%

“…The analysis includes electronic neuromorphic architectures introduced in Section 1.1. For the photonically enhanced system, we studied an optoelectronic neural network with PNNs instantiated within the hybrid silicon/III-V platform [58,122]. We also consider a future photonic crystal instantiation based on fundamental physical considerations.…”

Section: Neuromorphic Platform Comparisonmentioning

confidence: 99%

Progress in neuromorphic photonics

et al. 2017

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As society's appetite for information continues to grow, so does our need to process this information with increasing speed and versatility. Many believe that the one-size-fits-all solution of digital electronics is becoming a limiting factor in certain areas such as data links, cognitive radio, and ultrafast control. Analog photonic devices have found relatively simple signal processing niches where electronics can no longer provide sufficient speed and reconfigurability. 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. By bridging the mathematical prowess of artificial neural networks to the underlying physics of optoelectronic devices, neuromorphic photonics could breach new domains of information processing demanding significant complexity, low cost, and unmatched speed. In this article, we review the progress in neuromorphic photonics, focusing on photonic integrated devices. The challenges and design rules for optoelectronic instantiation of artificial neurons are presented. The proposed photonic architecture revolves around the processing network node composed of two parts: a nonlinear element and a network interface. We then survey excitable lasers in the recent literature as candidates for the nonlinear node and microring-resonator weight banks as the network interface. Finally, we compare metrics between neuromorphic electronics and neuromorphic photonics and discuss potential applications.

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Excitable laser processing network node in hybrid silicon: analysis and simulation

Cited by 42 publications

References 38 publications

Advances in photonic reservoir computing

Advances in photonic reservoir computing

Principles of Neuromorphic Photonics

Progress in neuromorphic photonics

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