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


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 publicationEuropean Quantum Electronics Conference, EQEC_2019
PublisherOptica Publishing Group (formerly OSA)
ISBN (Print)9781728104690
StatePublished - 2019
EventEuropean Quantum Electronics Conference, EQEC_2019 - Munich, United Kingdom
Duration: Jun 23 2019Jun 27 2019

Publication series

NameOptics InfoBase Conference Papers
VolumePart F143-EQEC 2019
ISSN (Electronic)2162-2701


ConferenceEuropean Quantum Electronics Conference, EQEC_2019
Country/TerritoryUnited Kingdom

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Mechanics of Materials


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