Alexander N. Tait

A Leaky Integrate-and-Fire Laser Neuron for Ultrafast Cognitive Computing

We propose an original design for a neuron-inspired photonic computational primitive for a large-scale, ultrafast cognitive computing platform. The laser exhibits excitability and behaves analogously to a leaky integrate-and-fire (LIF) neuron. This model is both fast and scalable, operating up to a billion times faster than a biological equivalent and is realizable in a compact, vertical-cavity surface-emitting laser (VCSEL). We show that-under a certain set of conditions-the rate equations governing a laser with an embedded saturable absorber reduces to the behavior of LIF neurons. We simulate the laser using realistic rate equations governing a VCSEL cavity, and show behavior representative of cortical spiking algorithms simulated in small circuits of excitable lasers. Pairing this technology with ultrafast, neural learning algorithms would open up a new domain of processing.

Broadcast and Weight: An Integrated Network For Scalable Photonic Spike Processing

We propose an on-chip optical architecture to support massive parallel communication among high-performance spiking laser neurons. Designs for a network protocol, computational element, and waveguide medium are described, and novel methods are considered in relation to prior research in optical on-chip networking, neural networking, and computing. Broadcast-and-weight is a new approach for combining neuromorphic processing and optoelectronic physics, a pairing that is found to yield a variety of advantageous features. We discuss properties and design considerations for architectures for scalable wavelength reuse and biologically relevant organizational capabilities, in addition to aspects of practical feasibility. Given recent developments commercial photonic systems integration and neuromorphic computing, we suggest that a novel approach to photonic spike processing represents a promising opportunity in unconventional computing.

Spike processing with a graphene excitable laser.

Novel materials and devices in photonics have the potential to revolutionize optical information processing, beyond conventional binary-logic approaches. Laser systems offer a rich repertoire of useful dynamical behaviors, including the excitable dynamics also found in the time-resolved “spiking” of neurons. Spiking reconciles the expressiveness and efficiency of analog processing with the robustness and scalability of digital processing. We demonstrate a unified platform for spike processing with a graphene-coupled laser system. We show that this platform can simultaneously exhibit logic-level restoration, cascadability and input-output isolation—fundamental challenges in optical information processing. We also implement low-level spike-processing tasks that are critical for higher level processing: temporal pattern detection and stable recurrent memory. We study these properties in the context of a fiber laser system and also propose and simulate an analogous integrated device. The addition of graphene leads to a number of advantages which stem from its unique properties, including high absorption and fast carrier relaxation. These could lead to significant speed and efficiency improvements in unconventional laser processing devices, and ongoing research on graphene microfabrication promises compatibility with integrated laser platforms.Novel materials and devices in photonics have the potential to revolutionize optical information processing, beyond conventional binary-logic approaches. Laser systems offer a rich repertoire of useful dynamical behaviors, including the excitable dynamics also found in the time-resolved “spiking” of neurons. Spiking reconciles the expressiveness and efficiency of analog processing with the robustness and scalability of digital processing. We demonstrate that graphene-coupled laser systems offer a unified low-level spike optical processing paradigm that goes well beyond previously studied laser dynamics. We show that this platform can simultaneously exhibit logic-level restoration, cascadability and input-output isolation—fundamental challenges in optical information processing. We also implement low-level spike-processing tasks that are critical for higher level processing: temporal pattern detection and stable recurrent memory. We study these properties in the context of a fiber laser system, but the addition of graphene leads to a number of advantages which stem from its unique properties, including high absorption and fast carrier relaxation. These could lead to significant speed and efficiency improvements in unconventional laser processing devices, and ongoing research on graphene microfabrication promises compatibility with integrated laser platforms.

Recent progress in semiconductor excitable lasers for photonic spike processing

Recently, there has been tremendous interest in excitable optoelectronic devices and in particular excitable semiconductor lasers that could potentially enable unconventional processing approaches beyond conventional binary-logic-based approaches. In parallel, there has been renewed investigation of non-von Neumann architectures driven in part by incipient limitations in aspects of Moore’s law. These neuromorphic architectures attempt to decentralize processing by interweaving interconnection with computing while simultaneously incorporating time-resolved dynamics, loosely classified as spiking (a.k.a. excitability). The rapid and efficient advances in CMOS-compatible photonic interconnect technologies have led to opportunities in optics and photonics for unconventional circuits and systems. Effort in the budding research field of photonic spike processing aims to synergistically integrate the underlying physics of photonics with bio-inspired processing. Lasers operating in the excitable regime are dynamically analogous with the spiking dynamics observed in neuron biophysics but roughly 8 orders of magnitude faster. The field is reaching a critical juncture at which there is a shift from studying single devices to studying an interconnected network of lasers. In this paper, we review the recent research in the information processing abilities of such lasers, dubbed “photonic neurons,” “laser neurons,” or “optical neurons.” An integrated network of such lasers on a chip could potentially grant the capacity for complex, ultrafast categorization and decision making to provide a range of computing and signal processing applications, such as sensing and manipulating the radio frequency spectrum and for hypersonic aircraft control.

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

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.

Photonic Neuromorphic Signal Processing and Computing

There has been a recent explosion of interest in spiking neural networks (SNNs), which code information as spikes or events in time. Spike encoding is widely accepted as the information medium underlying the brain, but it has also inspired a new generation of neuromorphic hardware. Although electronics can match biological time scales and exceed them, they eventually reach a bandwidth fan-in trade-off. An alternative platform is photonics, which could process highly interactive information at speeds that electronics could never reach. Correspondingly, processing techniques inspired by biology could compensate for many of the shortcomings that bar digital photonic computing from feasibility, including high defect rates and signal control problems. We summarize properties of photonic spike processing and initial experiments with discrete components. A technique for mapping this paradigm to scalable, integrated laser devices is explored and simulated in small networks. This approach promises to wed the advantageous aspects of both photonic physics and unconventional computing systems. Further development could allow for fully scalable photonic networks that would open up a new domain of ultrafast, robust, and adaptive processing. Applications of this technology ranging from nanosecond response control systems to fast cognitive radio could potentially revitalize specialized photonic computing.

Signal feature recognition based on lightwave neuromorphic signal processing

We developed a hybrid analog/digital lightwave neuromorphic processing device that effectively performs signal feature recognition. The approach, which mimics the neurons in a crayfish responsible for the escape response mechanism, provides a fast and accurate reaction to its inputs. The analog processing portion of the device uses the integration characteristic of an electro-absorption modulator, while the digital processing portion employ optical thresholding in a highly Ge-doped nonlinear loop mirror. The device can be configured to respond to different sets of input patterns by simply varying the weights and delays of the inputs. We experimentally demonstrated the use of the proposed lightwave neuromorphic signal processing device for recognizing specific input patterns.

The DREAM: An Integrated Photonic Thresholder

We propose a novel all-optical integrated thresholder called the dual resonator enhanced asymmetric Mach-Zehnder interferometer (DREAM). Unlike prior integrated photonic devices, the DREAM exhibits properties of stable binary decision making, outputting a constant “one” power value for signals above a certain power level and “zero” for signals of lower powers. This thresholding shape arises from the interference of complementary nonlinear effects of two microring resonators (MRR), one in each arm of a Mach-Zehnder interferometer (MZI). The proposed device performs several orders of magnitude better in size, decision latency, energy efficiency, and stability compared to fiber-based methods of optical thresholding. It is best suited for application in densely integrated systems where rapid conversion between analog and digital signal domains is ubiquitous, such as hybrid analog-digital and neuromorphic processing architectures. We derive analytical steady-state solutions to the nonlinear MRR, which enable design simulation, optimization, and automation of a continuous signal thresholder about three orders of magnitude faster than with numerical simulation. Additional numerical simulations indicate the possibility of a 50 GHz pulse thresholder with a 380 pJ switching threshold in a silicon-on-insulator (SOI) platform. The proposed circuit design techniques are potentially applicable to a wide range of materials, waveguide platforms, and resonator types, but for concreteness, we limit the focus of this paper to MRRs in SOI.

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 types of laser neurons with saturable absorber 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.

Demonstration of WDM weighted addition for principal component analysis

We consider an optical technique for performing tunable weighted addition using wavelength-division multiplexed (WDM) inputs, the enabling function of a recently proposed photonic spike processing architecture [J. Lightwave Technol., 32 (2014)]. WDM weighted addition provides important advantages to performance, integrability, and networking capability that were not possible in any past approaches to optical neurocomputing. In this letter, we report a WDM weighted addition prototype used to find the first principal component of a 1Gbps, 8-channel signal. Wideband, multivariate techniques have immediate relevance to modern radio systems, and photonic spike processing networks enabled by WDM could open new domains of information processing that bring unprecedented bandwidth and intelligence to problems in radio communications, ultrafast control, and scientific computing.