Digital Electronics and Analog Photonics for Convolutional Neural Networks (DEAP-CNNs)

Viraj Bangari, Bicky A. Marquez, Heidi Miller, Alexander N. Tait, Mitchell A. Nahmias, Thomas Ferreira De Lima, Hsuan Tung Peng, Paul R. Prucnal, Bhavin J. Shastri

Research output: Contribution to journalArticlepeer-review

125 Scopus citations


Convolutional Neural Networks (CNNs) are powerful and highly ubiquitous tools for extracting features from large datasets for applications such as computer vision and natural language processing. However, a convolution is a computationally expensive operation in digital electronics. In contrast, neuromorphic photonic systems, which have experienced a recent surge of interest over the last few years, propose higher bandwidth and energy efficiencies for neural network training and inference. Neuromorphic photonics exploits the advantages of optical electronics, including the ease of analog processing, and busing multiple signals on a single waveguide at the speed of light. Here, we propose a Digital Electronic and Analog Photonic (DEAP) CNN hardware architecture that has potential to be 2.8 to 14 times faster while using almost 25% less energy than current state-of-the-art graphical processing units (GPUs).

Original languageEnglish (US)
Article number8859364
JournalIEEE Journal of Selected Topics in Quantum Electronics
Issue number1
StatePublished - Jan 1 2020

All Science Journal Classification (ASJC) codes

  • Atomic and Molecular Physics, and Optics
  • Electrical and Electronic Engineering


  • Deep learning
  • convolutional neural network (CNN)
  • machine learning
  • neuromorphic photonics
  • photonic neural networks


Dive into the research topics of 'Digital Electronics and Analog Photonics for Convolutional Neural Networks (DEAP-CNNs)'. Together they form a unique fingerprint.

Cite this