Silicon microring synapses enable photonic deep learning beyond 9-bit precision
Abstract
Deep neural networks (DNNs) consist of layers of neurons interconnected by synaptic weights. A high bit-precision in weights is generally required to guarantee high accuracy in many applications. Minimizing error accumulation between layers is also essential when building large-scale networks. Recent demonstrations of photonic neural networks are limited in bit-precision due to cross talk and the high sensitivity of optical components (e.g., resonators). Here, we experimentally demonstrate a record-high precision of 9 bits with a dithering control scheme for photonic synapses. We then numerically simulated the impact with increased synaptic precision on a wireless signal classification application. This work could help realize the potential of photonic neural networks for many practical, real-world tasks.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- May 20, 2022
- Source ID
- 10.1364/optica.446100
Entities
People
- Aashu Jha
- Bhavin J. Shastri
- Chaoran Huang
- Eric Blow
- Hsuan-Tung Peng
- Paul Prucnal
- Simon Bilodeau
- Thomas Ferreira de Lima
- Weipeng Zhang
Organizations
- Defense Advanced Research Projects Agency
- Fonds de Recherche du Québec Nature et technologies
- NEC Laboratories America
- National Science Foundation
- Natural Sciences and Engineering Research Council
- Princeton University
- Queen's University
- State Research Center of Virology and Biotechnology VECTOR
- The Chinese University of Hong Kong