Automatic classification of video using a scalable photonic neuro-inspired architecture
Résumé
We propose a physical alternative of software based approaches for
advanced classification task by considering a photonic-based
architecture implementing a recurrent neural network with up to 16,384
physical neurons. This architecture is realized with o↵-the-shelf
components and can be scaled up to hundred thousand or millions of nodes
while ensuring data-ecient training strategy thanks to the reservoir
computing framework. We use this architecture to perform a challenging
computer vision task: the classification of human actions from a video
feed. For this task, we show for the first time that a physical
architecture with a simple learning strategy, consisting of training one
linear readout for each class, can achieve a >90% success rate in
terms of classification accuracy. This rivals the deep-learning
approaches in terms of level of performance and hence could pave the way
towards novel paradigm for ecient real-time video processing at the
physical layer using photonic systems.