SIMPEL: Circuit model for photonic spike processing laser neurons

Shastri, Bhavin J.; Nahmias, Mitchell A.; Tait, Alexander N.; Wu, Ben; Prucnal, Paul R.

doi:10.1364/oe.23.008029

Cited by 42 publications

(21 citation statements)

References 61 publications

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“…The absorber is assumed to have a faster lifetime, which helps improve the dynamics and consistency of the input signals [13]. The equations can be simplified to the undimensionalized set of equations through a set of variable substitutions [12,38]. The results are shown in Fig.…”

Section: Devicementioning

confidence: 99%

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

Nahmias¹,

Tait²,

Shastri³

et al. 2015

Opt. Express

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The combination of ultrafast laser dynamics and dense on-chip multiwavelength networking could potentially address new domains of real-time signal processing that require both speed and complexity. We present a physically realistic optoelectronic simulation model of a circuit for dynamical laser neural networks and verify its behavior. We describe the physics, dynamics, and parasitics of one network node, which includes a bank of filters, a photodetector, and excitable laser. This unconventional circuit exhibits both cascadability and fan-in, critical properties for the large-scale networking of information processors based on laser excitability. In addition, it can be instantiated on a photonic integrated circuit platform and requires no off-chip optical I/O. Our proposed processing system could find use in emerging applications, including cognitive radio and low-latency control.

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Section: Devicementioning

confidence: 99%

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

Nahmias¹,

Tait²,

Shastri³

et al. 2015

Opt. Express

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“…Because information processing in the brain is associated with the excitable action potentials that propagate through neuronal axons [2], many efforts are being done to develop excitable devices that could mimic neuronal behavior in bio-inspired information processing networks. Particularly, semiconductor lasers with optical injection [3][4][5], optical feedback [6,7] or saturated absorber [8][9][10][11] have been investigated with this purpose.…”

Section: Introductionmentioning

confidence: 99%

Effects of periodic forcing on the temporally correlated spikes of a semiconductor laser with feedback

Sorrentino¹,

Quintero-Quiroz²,

Aragoneses³

et al. 2015

Opt. Express

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Optical excitable devices that mimic neuronal behavior can be building-blocks of novel, brain-inspired information processing systems. A relevant issue is to understand how such systems represent, via correlated spikes, the information of a weak external input. Semiconductor lasers with optical feedback operating in the low frequency fluctuations regime have been shown to display optical spikes with intrinsic temporal correlations similar to those of biological neurons. Here we investigate how the spiking laser output represents a weak periodic input that is implemented via direct modulation of the laser pump current. We focus on understanding the influence of the modulation frequency. Experimental sequences of inter-spike-intervals (ISIs) are recorded and analyzed by using the ordinal symbolic methodology that identifies and characterizes serial correlations in datasets. The change in the statistics of the various symbols with the modulation frequency is empirically shown to be related to specific changes in the ISI distribution, which arise due to different phase-locking regimes. A good qualitative agreement is also found between simulations of the Lang and Kobayashi model and observations. This methodology is an efficient way to detect subtle changes in noisy correlated ISI sequences and may be applied to investigate other optical excitable devices.

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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%

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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SIMPEL: Circuit model for photonic spike processing laser neurons

Cited by 42 publications

References 61 publications

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

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

Effects of periodic forcing on the temporally correlated spikes of a semiconductor laser with feedback

Progress in neuromorphic photonics

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