P. Prucnal (US) Princeton University
Princeton UniversityAuthor Of 2 Presentations
(PIP) Photonic Integration and Packaging
MD2.4 - NEUROMORPHIC PHOTONICS FOR DEEP LEARNING
Presentation Type
Invited Submission
Authors
- V. Bangari (CA) Queen's University
- B. Marquez (CA) Queen's University
- A. Tait (US) National Institute of Standards and Technology
- M. Nahmias (US) Princeton University
- T. Ferreira de Lima (US) Princeton University
- H. Peng (US) Princeton University
- P. Prucnal (US) Princeton University
- B. Shastri (CA) Queen's University, Physics, Engineering Physics & Astronomy
Date
09/30/2019
Time
10:30 AM - 12:00 PM
Room
El Mirador C West
Duration
30 Minutes
Lecture Time
11:30 AM - 12:00 PM
Abstract
Abstract
Co-integrated neuromorphic photonic and electronic processors promise orders of magnitude improvements in both speed and energy efficiency over purely digital electronic approaches. We discuss neuromorphic photonic systems and their application to deep convolutional neural networks inference.
(PIP) Photonic Integration and Packaging
WP10 - AUTOMATED CONTROL OF THE TRANSFER FUNCTION OF AN INTEGRATED CASCADED MACH-ZEHNDER INTERFEROMETER
Presentation Type
Contributed Submission
Authors
Date
10/02/2019
Time
06:00 PM - 08:00 PM
Room
El Mirador B/C
Lecture Time
06:00 PM - 06:00 PM
Abstract
Abstract
Thermal crosstalk and fabrication variations are detrimental to accurate control of integrated photonic systems. We build a calibration-based control algorithm accounting for these factors in a dual Mach-Zehnder Interferometer (MZI) structure, and experimentally demonstrate automated control of optical transmission with only 3% error.