Programmable recirculating bricks mesh architecture for photonic neural networks

TL;DR

This paper introduces a recirculating bricks mesh architecture for programmable photonic neural networks, enabling versatile functions, self-monitoring, and reconfiguration in a single optical system.

Contribution

The work presents a novel recirculating bricks mesh design that can be reprogrammed for multiple functions, including neural network operations and circuit stabilization.

Findings

Single system performs various functions after reprogramming

Mesh can implement crossbar networks and interference circuits

Supports power monitoring and self-calibration

Abstract

General-purpose programmable photonic processors are considered a crucial technology because they combine the ultra high-speed, massive bandwidth, and energy efficiency of light-based computing with the flexibility of software-defined hardware. Unlike application-specific photonic integrated circuits (ASPIC) designed for one task, these processors use reconfigurable waveguide meshes to implement various functions, such as switching, filtering, or AI computation, on a single chip, allowing for rapid prototyping and versatile, on-demand hardware redefinition. Here we report a recirculating bricks mesh architecture that can be easily implemented in photonic neural networks. It will be shown that a single programmable optical system is capable of performing various functions depending on the requirements. In particular, we will show that the same network, after being reprogrammed, can perform many different functions, ranging from a crossbar network to optical interference circuits with variable structures, which can then be subjected to Singular Value Decomposition. Furthermore, the "bricks" mesh serves as an excellent foundation for implementing a monitoring system capable of monitoring the power in each location of the circuit and, subsequently, sel-fcalibrating and stabilizing the circuit using a feedback loop.