H. Peng (US) Princeton University
Princeton UniversityAuthor Of 1 Presentation
(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.