TY - JOUR
T1 - Silicon Photonics for Training Deep Neural Networks
AU - Shastri, Bhavin J.
AU - Filipovich, Matthew J.
AU - Guo, Zhimu
AU - Prucnal, Paul R.
AU - Shekhar, Sudip
AU - Sorger, Volker J.
N1 - Publisher Copyright:
© 2022 The Author(s) © IEEE 2022.
PY - 2022
Y1 - 2022
N2 - Analog photonic networks as deep learning hardware accelerators are trained on standard digital electronics. We propose an on-chip training of neural networks enabled by a silicon photonic architecture for parallel, efficient, and fast data operations.
AB - Analog photonic networks as deep learning hardware accelerators are trained on standard digital electronics. We propose an on-chip training of neural networks enabled by a silicon photonic architecture for parallel, efficient, and fast data operations.
UR - https://www.scopus.com/pages/publications/85166474089
UR - https://www.scopus.com/pages/publications/85166474089#tab=citedBy
U2 - 10.1364/CLEOPR.2022.CThA13B_02
DO - 10.1364/CLEOPR.2022.CThA13B_02
M3 - Conference article
AN - SCOPUS:85166474089
SN - 2162-2701
JO - Optics InfoBase Conference Papers
JF - Optics InfoBase Conference Papers
T2 - 2022 Conference on Lasers and Electro-Optics Pacific Rim, CLEO/PR 2022
Y2 - 31 August 2022 through 5 September 2022
ER -