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2 March 2020 Automatic classification of video using a scalable photonic neuro-inspired architecture
Damien Rontani, Piotr Antonik, Nicolas Marsal, Daniel Brunner
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Proceedings Volume 11274, Physics and Simulation of Optoelectronic Devices XXVIII; 112740F (2020) https://doi.org/10.1117/12.2551368
Event: SPIE OPTO, 2020, San Francisco, California, United States
Abstract
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.
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Damien Rontani, Piotr Antonik, Nicolas Marsal, and Daniel Brunner "Automatic classification of video using a scalable photonic neuro-inspired architecture", Proc. SPIE 11274, Physics and Simulation of Optoelectronic Devices XXVIII, 112740F (2 March 2020); https://doi.org/10.1117/12.2551368
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KEYWORDS
Video
Spatial light modulators
Cameras
Optical computing
Computing systems
Neural networks
Neurons
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