Silicon microring synapses enable photonic deep learning beyond 9-bit precision
Weipeng Zhang, Chaoran Huang, Hsuan-Tung Peng, Simon Bilodeau, Aashu Jha, Eric C. Blow, Thomas Ferreira de Lima, Bhavin J. Shastri, Paul R. Prucnal
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.