@inproceedings{21568,
  abstract     = {Optical approaches to AI acceleration have gained intense interest recently due to the potentially breakthrough advantages of photonics: high bandwidth, low power consumption, and efficient data movement. We overview leading photonic AI platforms based on beamsplitter mesh networks, weight banks, and photoelectric multiplication. While the theoretical performance can be orders of magnitude beyond current state of the art, practical issues of chip area, input / output, and crosstalk paint a more nuanced near-term picture of photonic AI acceleration. Both fundamental and near-term limitations to energy efficiency are addressed, and bandwidth limitations due to temporal crosstalk are analyzed.},
  author       = {Hamerly, R. and Sludds, A. and Bernstein, L. and Prabhu, M. and Roques-Carmes, Charles and Carolan, J. and Yamamoto, Y. and Soljacic, M. and Englund, D.},
  booktitle    = {2019 IEEE International Electron Devices Meeting},
  issn         = {2156-017X},
  location     = {San Francisco, CA, United States},
  publisher    = {IEEE},
  title        = {{Towards large-scale photonic neural-network accelerators}},
  doi          = {10.1109/iedm19573.2019.8993624},
  year         = {2019},
}