Multiwavelength neuromorphic silicon photonics

Bhavin J. Shastri, Alexander N. Tait, Mitchell A. Nahmias, Thomas Ferreira De Lima, Hsuan Tung Peng, Paul R. Prucnal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Artificial Intelligence (AI) is transforming our lives in the same way as the advent of the Internet and cellular phones has done. AI is revolutionizing the healthcare industry with complex medical data analysis, actualizing self-driving cars, and beating humans at strategy games such as Go. However, it takes thousands of CPUs and GPUs, and many weeks to train the neural networks in AI hardware. Over the last six years, this compute power has doubled every 3.5 months. Traditional CPUs, GPUs and even neuromorphic electronics (IBM TrueNorth [1] and Google TPU [2]) have improved both energy efficiency and speed enhancement for learning (inference) tasks. However, electronic architectures face fundamental limits as Moore's law is slowing down. Furthermore, moving data electronically on metal wires has fundamental bandwidth and energy efficiency limitations, thus remaining a critical challenge facing deep learning hardware accelerators [3].

Original languageEnglish (US)
Title of host publication2019 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728104690
DOIs
StatePublished - Jun 2019
Event2019 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2019 - Munich, Germany
Duration: Jun 23 2019Jun 27 2019

Publication series

Name2019 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2019

Conference

Conference2019 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2019
CountryGermany
CityMunich
Period6/23/196/27/19

All Science Journal Classification (ASJC) codes

  • Spectroscopy
  • Electronic, Optical and Magnetic Materials
  • Instrumentation
  • Atomic and Molecular Physics, and Optics
  • Computer Networks and Communications

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    Shastri, B. J., Tait, A. N., Nahmias, M. A., De Lima, T. F., Peng, H. T., & Prucnal, P. R. (2019). Multiwavelength neuromorphic silicon photonics. In 2019 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2019 [8872872] (2019 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CLEOE-EQEC.2019.8872872