Artificial intelligence enabled by automatic dynamic exploration of integrated photonic spiking neural networks

In this project (ARIADNE) I will address several key challenges of today’s hardware-based neuromorphic computing by developing a novel AI system based on a highly complex, dynamical and nonlinear integrated photonic neural network that is easy to fabricate and consumes low power.

The proposed implementation is enabled by an original machine learning approach which allows to considerably relax the requirements on fabrication reproducibility and on observability and tunability of the network parameters.

The AI system is expected to learn to perform multiple complex and time-dependent computational tasks at high speed within a compact device, finding application in real-time control with enhanced cybersecurity (robotics, autonomous vehicles, internet of things, …) and physiological signals analysis (e.g. prediction of epileptic seizures).

ARIADNE will be hosted by the NanoLab research group at the University of Trento (UNITN).The project acronym is inspired by the ancient Greek myth of Theseus and the Minotaur, where princess Ariadne comes up with an ingenious way to help Theseus escape the labyrinth.

In ARIADNE I aim to let a reinforcement learning algorithm learn to solve, loosely speaking, the spatio-temporal maze represented by the complicated dynamics in the considered networks of integrated optical resonators.

In particular, the reinforcement learning algorithm will learn to control a complex feedback loop in order to set favorable dynamical network properties that allow to carry out target computational tasks.The AI system is expected to learn to perform multiple complex and time-dependent computational tasks at high speed within a compact device, finding application in real-time control with enhanced cybersecurity (robotics, autonomous vehicles, internet of things, …) and physiological signals analysis (e.g. prediction of epileptic seizures).