Supervisors: Prof. Ian O’Connor (EC Lyon), Dr Fabio Pavanello (EC Lyon) and Prof. Arnan Mitchell (RMIT)
A photonic neural network (PNN) is a physical implementation of an artificial NN with photonic components. With respect to conventional NN implementations, PNNs offer several key benefits, such as orders of magnitude improvements in energy consumption, latency and bandwidth density. However, photonics- based CNNs, widely deployed for e.g., image recognition tasks, have been mostly focused on photonic accelerators due the complex task of implementing non-linear functions in photonics. The group of research topics below aims to explore how Lithium Niobate on Insulator (LNOI) platforms can be leveraged to achieve more complex functionalities missing in standard Silicon-based platforms, thus enabling the realization of the whole CNN, rather than only the acceleration portion for input pre-processing (e.g., kernel multiplication) in challenging energy-efficient edge computing applications, or for environment safety-critical applications.
Project 1: Photonic CNN for large-scale image processing
In this project we will consider implementations of high-speed low-latency photonic convolutional neural networks (CNNs) integrated on-chip based on Lithium Niobate on Insulator (LNOI) platforms. We will analyze required performance of key components which will be developed at RMIT. System-level simulations will be carried out with models built on the experimental data. Systems will be fabricated at RMIT and demonstrators will be tested at INL/RMIT for large-scale image processing applications.
Project 2: Photonic CNN for low-power edge applications
In this project we will consider implementations of low-power energy-efficient photonic convolutional neural networks (CNNs) integrated on-chip based on Lithium Niobate on Insulator (LNOI) platforms. We will analyze required performance of key components which will be developed at RMIT. System-level simulations will be carried out with models built on the experimental data. Systems will be fabricated at RMIT and demonstrators will be tested at INL/RMIT for edge computing applications.
Project 3: Photonic CNN for safety-critical applications
In this project we will consider implementations of robust photonic convolutional neural networks (CNNs) integrated on-chip based on Lithium Niobate on Insulator (LNOI) platforms. We will analyze required performance of key components which will be developed at RMIT. System-level simulations will be carried out with models built on the experimental data. Systems will be fabricated at RMIT and demonstrators will be tested at INL/RMIT for environment-sensitive safety-critical applications.
