Photonic computing for massively
Laurent Daudet, CTO and co-founder at LightOn
Monday, Nov. 8, 2021 3:15 – 4:15pm (CET)
Or on site INF 328
Recent large-scale AI models, in the wake of OpenAi’s GPT-3 model for NLP, offer tremendous potential for applications. However, training such models requires massive amounts of computing resources, already challenging the capacity of some of the largest supercomputing architectures.
In this talk I will present LightOn’s view on how future AI hardware and software should be co-designed, to address some of the hardest computing challenges, such for scaling up language models, recommender systems, or graph neural networks. In particular, I will discuss how LightOn Optical Processing Units (OPUs) can already be seamlessly integrated into a variety of hybrid photonics / silicon pipelines, leveraging randomized algorithms for Machine Learning and Scientific Computing: alternative training methods with guarantees on differential privacy or robustness to adversarial attacks, reinforcement learning, anomaly detection on time series, randomized numerical linear algebra, etc.
Laurent Daudet is currently employed as CTO at LightOn, a startup he co-founded in 2016, where he manages cross-disciplinary R&D projects, involving machine learning, optics, signal processing, electronics, and software engineering.
Laurent is a recognized expert in signal processing and wave physics, and is currently on leave from his position of Professor of Physics at the Université de Paris. Prior to that or in parallel, he has held various academic positions: fellow of the Institut Universitaire de France, associate professor at Universite Pierre et Marie Curie, Visiting Senior Lecturer at Queen Mary University of London, Visiting Professor at the National Institute for Informatics in Tokyo.
Laurent has authored or co-authored more than 200 scientific publications, has been a consultant to various small and large companies, and is a co-inventor in several patents. He is a graduate in physics from Ecole Normale Superieure in Paris, and holds a PhD in Applied Mathematics from Marseille University.