Silicon Photonic Modulator Neuron

There has been a recently renewed interest in neuromorphic photonics, a field promising to access pivotal and unexplored regimes of machine intelligence. Progress has been made on isolated neurons and analog interconnects; nevertheless, this renewal has yet to produce a demonstration of a silicon photonic neuron capable of interacting with other like neurons. We report a modulator-class photonic neuron fabricated in a conventional silicon photonic process line. We demonstrate behaviors of transfer function configurability, fan-in, inhibition, time-resolved processing, and, crucially, autaptic cascadability -- a sufficient set of behaviors for a device to act as a neuron participating in a network of like neurons. The silicon photonic modulator neuron constitutes the final piece needed to make photonic neural networks fully integrated on currently available silicon photonic platforms.

[1]  Jianping Yao,et al.  Broadband Chaotic Signals and Breather Oscillations in an Optoelectronic Oscillator Incorporating a Microwave Photonic Filter , 2014, Journal of Lightwave Technology.

[2]  Jun Deguchi,et al.  A Neuromorphic Chip Optimized for Deep Learning and CMOS Technology With Time-Domain Analog and Digital Mixed-Signal Processing , 2017, IEEE Journal of Solid-State Circuits.

[3]  F ROSENBLATT,et al.  The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.

[4]  Ming Li,et al.  A fully reconfigurable photonic integrated signal processor , 2016, Nature Photonics.

[5]  Jie Sun,et al.  Open Foundry Platform for High-performance Electronic-photonic Integration References and Links , 2022 .

[6]  Bhavin J. Shastri,et al.  Neuromorphic Photonic Integrated Circuits , 2018, IEEE Journal of Selected Topics in Quantum Electronics.

[7]  Wei Pan,et al.  Cascadable Neuron-Like Spiking Dynamics in Coupled VCSELs Subject to Orthogonally Polarized Optical Pulse Injection , 2017, IEEE Journal of Selected Topics in Quantum Electronics.

[8]  Johannes Schemmel,et al.  Reward-based learning under hardware constraints—using a RISC processor embedded in a neuromorphic substrate , 2013, Front. Neurosci..

[9]  R. Soref.  Silicon Photonics: A Review of Recent Literature , 2010 .

[10]  Henry Markram,et al.  Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations , 2002, Neural Computation.

[11]  Paul R. Prucnal,et al.  Recent progress in semiconductor excitable lasers for photonic spike processing , 2016 .

[12]  P. Bienstman,et al.  Excitation transfer between optically injected microdisk lasers. , 2013, Optics express.

[13]  Joshua Robertson,et al.  Controlled Propagation of Spiking Dynamics in Vertical-Cavity Surface-Emitting Lasers: Towards Neuromorphic Photonic Networks , 2017, IEEE Journal of Selected Topics in Quantum Electronics.

[14]  Wofgang Maas,et al.  Networks of spiking neurons: the third generation of neural network models , 1997 .

[15]  Qianfan Xu,et al.  12.5 Gbit/s carrier-injection-based silicon micro-ring silicon modulators. , 2007, Optics express.

[16]  Paul R. Prucnal,et al.  Spike processing with a graphene excitable laser , 2016, Scientific Reports.

[17]  Michiel Hermans,et al.  Optoelectronic Systems Trained With Backpropagation Through Time , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[18]  Johannes Schemmel,et al.  Six Networks on a Universal Neuromorphic Computing Substrate , 2012, Front. Neurosci..

[19]  Chris Eliasmith,et al.  Neural Engineering: Computation, Representation, and Dynamics in Neurobiological Systems , 2004, IEEE Transactions on Neural Networks.

[20]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[21]  Ke Li,et al.  Multipurpose silicon photonics signal processor core , 2017, Nature Communications.

[22]  Dirk Englund,et al.  Deep learning with coherent nanophotonic circuits , 2017, 2017 Fifth Berkeley Symposium on Energy Efficient Electronic Systems & Steep Transistors Workshop (E3S).

[23]  Shajahan Kutty,et al.  Beamforming for Millimeter Wave Communications: An Inclusive Survey , 2016, IEEE Communications Surveys & Tutorials.

[24]  M. Pickett,et al.  A scalable neuristor built with Mott memristors. , 2013, Nature materials.

[25]  L Pesquera,et al.  Photonic information processing beyond Turing: an optoelectronic implementation of reservoir computing. , 2012, Optics express.

[26]  Philip Y. Ma,et al.  Feedback control for microring weight banks. , 2018, Optics express.

[27]  Hong Wang,et al.  Loihi: A Neuromorphic Manycore Processor with On-Chip Learning , 2018, IEEE Micro.

[28]  James Gary Eden,et al.  IEEE Journal of Quantum Electronics: Editorial , 2002 .

[29]  Miguel C. Soriano,et al.  Minimal approach to neuro-inspired information processing , 2015, Front. Comput. Neurosci..

[30]  Sergio Gomez Colmenarejo,et al.  Hybrid computing using a neural network with dynamic external memory , 2016, Nature.

[31]  Paul R Prucnal,et al.  A high performance photonic pulse processing device. , 2009, Optics express.

[32]  Ue-Pyng Wen,et al.  A review of Hopfield neural networks for solving mathematical programming problems , 2009, Eur. J. Oper. Res..

[33]  P. R. Prucnal,et al.  A Leaky Integrate-and-Fire Laser Neuron for Ultrafast Cognitive Computing , 2013, IEEE Journal of Selected Topics in Quantum Electronics.

[34]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[35]  Chao Li,et al.  Review of Silicon Photonics Foundry Efforts , 2014, IEEE Journal of Selected Topics in Quantum Electronics.

[36]  Steve B. Furber,et al.  The SpiNNaker Project , 2014, Proceedings of the IEEE.

[37]  Paul R Prucnal,et al.  Two-pole microring weight banks. , 2018, Optics letters.

[38]  Paul R. Prucnal,et al.  Broadcast and Weight: An Integrated Network For Scalable Photonic Spike Processing , 2014, Journal of Lightwave Technology.

[39]  P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .

[40]  Taghi M. Khoshgoftaar,et al.  Deep learning applications and challenges in big data analytics , 2015, Journal of Big Data.

[41]  David F. Barbe.  Very Large Scale Integration (VLSI) , 1980 .

[42]  Paul R. Prucnal,et al.  Progress in neuromorphic photonics , 2017 .

[43]  Petr Sosík,et al.  Advances in Artificial Life , 2001, Lecture Notes in Computer Science.

[44]  Thomas Ferreira de Lima,et al.  Multi-channel control for microring weight banks. , 2016, Optics express.

[45]  L. Chrostowski,et al.  Silicon Photonics Design: From Devices to Systems , 2015 .

[46]  Bhavin J. Shastri,et al.  Neuromorphic photonic networks using silicon photonic weight banks , 2016, Scientific Reports.

[47]  P. Prucnal,et al.  Signal beating elimination using single-mode fiber to multimode fiber coupling. , 2011, Optics letters.

[48]  Ingo Fischer,et al.  Reconfigurable semiconductor laser networks based on diffractive coupling. , 2015, Optics letters.

[49]  Paul R. Prucnal,et al.  Microring Weight Banks , 2016, IEEE Journal of Selected Topics in Quantum Electronics.

[50]  Richard Soref,et al.  Reconfigurable optical directed-logic circuits using microresonator-based optical switches. , 2011, Optics express.

[51]  Andrew S. Cassidy,et al.  A million spiking-neuron integrated circuit with a scalable communication network and interface , 2014, Science.

[52]  Paul R. Prucnal,et al.  Machine Learning With Neuromorphic Photonics , 2019, Journal of Lightwave Technology.

[53]  Henry Markram,et al.  The human brain project. , 2012, Scientific American.

[54]  Zach DeVito,et al.  Opt , 2017 .

[55]  J W Goodman,et al.  Fully parallel, high-speed incoherent optical method for performing discrete Fourier transforms. , 1978, Optics letters.

[56]  Huug de Waardt,et al.  All fiber-optic neural network using coupled SOA based ring lasers , 2002, IEEE Trans. Neural Networks.

[57]  Rodrigo Alvarez-Icaza,et al.  Neurogrid: A Mixed-Analog-Digital Multichip System for Large-Scale Neural Simulations , 2014, Proceedings of the IEEE.

[58]  A. Weiner.  Ultrafast optical pulse shaping: A tutorial review , 2011 .

[59]  B. K. Jenkins,et al.  Analysis of weighted fan-out/fan-in volume holographic optical interconnections. , 1993, Applied optics.

[60]  Vivek K. Pallipuram,et al.  A comparative study of GPU programming models and architectures using neural networks , 2011, The Journal of Supercomputing.

[61]  D. Koshland.  Frontiers in neuroscience. , 1988, Science.

[62]  R Kuszelewicz,et al.  Relative refractory period in an excitable semiconductor laser. , 2014, Physical review letters.

[63]  Jens H. Schmid,et al.  Roadmap on silicon photonics , 2016 .

[64]  Robert W. Keyes,et al.  Optical Logic-in the Light of Computer Technology , 1985 .

[65]  H. Trieu,et al.  Athermal and wavelength-trimmable photonic filters based on TiO₂-cladded amorphous-SOI. , 2015, Optics express.

[66]  Front , 2020, 2020 Fourth World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4).

[67]  Sae Woo Nam,et al.  All-silicon light-emitting diodes waveguide-integrated with superconducting single-photon detectors. , 2017, Applied physics letters.

[68]  Abira Sengupta.  IEEE Journal of Solid-State Circuits Updates [Society News] , 2016 .

[69]  B. Romeira,et al.  Regenerative memory in time-delayed neuromorphic photonic resonators , 2016, Scientific Reports.

[70]  Minghao Qi,et al.  Ultrabroad-bandwidth arbitrary radiofrequency waveform generation with a silicon photonic chip-based spectral shaper , 2010 .

[71]  Siva Yegnanarayanan,et al.  Toward ultimate miniaturization of high Q silicon traveling-wave microresonators. , 2010, Optics express.

[72]  T J Cloonan,et al.  Six-stage digital free-space optical switching network using symmetric self-electro-optic-effect devices. , 1993, Applied optics.

[73]  B. Schrauwen,et al.  Cascadable excitability in microrings. , 2012, Optics express.

[74]  Demetri Psaltis.  Optical Neural Networks. , 1987 .

[75]  Narayanan Vijaykrishnan,et al.  Always-On Speech Recognition Using TrueNorth, a Reconfigurable, Neurosynaptic Processor , 2017, IEEE Transactions on Computers.

[76]  Ali Adibi,et al.  Robust postfabrication trimming of ultracompact resonators on silicon on insulator with relaxed requirements on resolution and alignment. , 2015, Optics letters.

[77]  Wolfgang Banzhaf,et al.  Advances in Artificial Life , 2003, Lecture Notes in Computer Science.

[78]  Alexander Norman Tait.  Silicon Photonic Neural Networks , 2018 .

[79]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[80]  I. Sagnes,et al.  Excitability and self-pulsing in a photonic crystal nanocavity , 2012 .

[81]  Joseph W. Goodman,et al.  Fan-in and Fan-out with Optical Interconnections , 1985 .

[82]  Thomas Nowotny,et al.  Comparing Neuromorphic Solutions in Action: Implementing a Bio-Inspired Solution to a Benchmark Classification Task on Three Parallel-Computing Platforms , 2019 .

[83]  J. Danckaert,et al.  Solitary and coupled semiconductor ring lasers as optical spiking neurons. , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[84]  S. Höfling,et al.  Electromechanical tuning of vertically-coupled photonic crystal nanobeams. , 2012, Optics express.

[85]  Sae Woo Nam,et al.  Superconducting optoelectronic circuits for neuromorphic computing , 2016, ArXiv.

[86]  Nelson S. Da Fonseca,et al.  IEEE Communications Surveys & Tutorials - third quarter 2008 , 2008, IEEE Communications Surveys & Tutorials.

[87]  Paul R. Prucnal,et al.  An integrated analog O/E/O link for multi-channel laser neurons , 2016 .

[88]  Qianfan Xu,et al.  All-optical logic based on silicon micro-ring resonators. , 2007, Optics express.

[89]  Odile Liboiron-Ladouceur,et al.  Responsivity optimization of a high-speed germanium-on-silicon photodetector. , 2016, Optics express.

[90]  Bernard Brezzo,et al.  TrueNorth: Design and Tool Flow of a 65 mW 1 Million Neuron Programmable Neurosynaptic Chip , 2015, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[91]  J. Wang,et al.  (European Journal of Operational Research, 177(2):1044-1061)A possibilistic decision model for new product supply chain design , 2007 .

[92]  Terrence C. Stewart,et al.  Large-Scale Synthesis of Functional Spiking Neural Circuits , 2014, Proceedings of the IEEE.

[93]  Michael J. Watts,et al.  IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS Publication Information , 2020, IEEE Transactions on Neural Networks and Learning Systems.

[94]  Rahul Sarpeshkar,et al.  Analog Versus Digital: Extrapolating from Electronics to Neurobiology , 1998, Neural Computation.

[95]  John Chang,et al.  Demonstration of WDM weighted addition for principal component analysis. , 2015, Optics express.

[96]  Andrew G. Glen,et al.  APPL , 2001 .

[97]  Karl K Berggren,et al.  A superconducting-nanowire three-terminal electrothermal device. , 2014, Nano letters.

[98]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[99]  J. J. Hopfield,et al.  “Neural” computation of decisions in optimization problems , 1985, Biological Cybernetics.

[100]  M. C. Soriano,et al.  A Unified Framework for Reservoir Computing and Extreme Learning Machines based on a Single Time-delayed Neuron , 2015, Scientific Reports.

[101]  J. O'Brien,et al.  Universal linear optics , 2015, Science.

[102]  Paul R. Prucnal,et al.  Implementing a Novel Highly Scalable Adaptive Photonic Beamformer using “Blind” Guided Accelerated Random Search , 2014, Journal of Lightwave Technology.

[103]  Tarek El-Ghazawi,et al.  Neuromorphic photonics with electro-absorption modulators. , 2018, Optics express.

[104]  Ellen Zhou,et al.  Neuromorphic photonic networks using silicon photonic weight banks , 2017, Scientific Reports.

[105]  Andrew Mundy,et al.  Real time Spaun on SpiNNaker: functional brain simulation on a massively-parallel computer architecture , 2017 .

[106]  Jennifer Hasler,et al.  Finding a roadmap to achieve large neuromorphic hardware systems , 2013, Front. Neurosci..

[107]  R. Soref,et al.  Electrooptical effects in silicon , 1987 .