Silicon microring synapses enable photonic deep learning beyond 9-bit precision

TL;DR

This paper demonstrates a photonic neural network synapse with a record-high 9-bit precision using a dithering control scheme, enhancing the potential for practical deep learning applications beyond previous optical limits.

Contribution

The work introduces a novel dithering control scheme enabling 9-bit precision in photonic synapses, surpassing previous limitations caused by crosstalk and component sensitivity.

Findings

Achieved 9-bit precision in photonic synapses experimentally.

Simulated improved accuracy in wireless signal classification with higher synaptic precision.

Potential for practical deep learning applications using photonic neural networks.

Abstract

Deep neural networks (DNN) 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 crosstalk 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.