Xiao Li will present her General Exam "Optical Optical Neural Network Achieved by Metalens Camera" on Tuesday, May 16, 2023 at 12:15 PM in CS 301 & via Zoom.
Committee Members: Felix Heide (advisor), Szymon Rusinkiewicz, Jason Fleischer
Abstract:
The explosive growth of computation and energy cost of artificial intelligence has spurred strong interests in new computing modalities in lieu of conventional electronic processors. Photonic processors that execute operations using photons instead of electrons, have promised to enable optical neural networks with ultra-low latency and power consumption. Existing optical neural networks, limited by the underlying network designs, have achieved image recognition accuracy much lower than state-of-the-art electronic neural networks. In this work, we close this gap by introducing a large-kernel spatially-varying convolutional neural network learned via low-dimensional reparameterization techniques. Such a network possesses superior representation capability and can be naturally implemented by wave optics.
We experimentally instantiate the network with a compact optical system that consists of an array of metasurface devices designed to induce angle-dependent responses. Combined with an extremely lightweight electronic backend with no more than 1K parameters, we are developing a nanophotonic neural network that can potentially outperform the first modern digital neural network -- AlexNet with 61M parameters, on the blind test classification accuracy on CIFAR-10 dataset, bringing optical neural network into modern deep learning era.
Reading List:
Everyone is invited to attend the talk, and those faculty wishing to remain for the oral exam following are welcome to do so.