Description
Date depot: 13 mars 2019
Titre: Deep Probabilistic Modeling on Novel Hardware
Directeur de thèse:
Maurizio FILIPPONE (Eurecom)
Domaine scientifique: Sciences et technologies de l'information et de la communication
Thématique CNRS : Non defini
Resumé:
Information and Communication
Technologies (ICT) are constantly producing advancements that translate into a
variety of societal changes including improvements to economy, better living
conditions, access to education, wellbeing, and entertainment.
The widespread use and growth of ICT,
however, is posing a huge threat to the sustainability of this development,
given that the energy consumption of current computing devices is growing at an
uncontrolled pace.
Within ICT, machine learning is
currently one of the fastest growing fields, given its pervasive use and
adoption in smart cities, retailing, finance, social media analysis,
communication systems, and transportation.
This project aims at tackling this issue
by investigating ways to perform machine learning exploiting novel hardware,
and in particular the one developed by the French company LightOn which is a
novel optical-based computing unit, called Optical Processing Units (OPU).
OPUs are capable of computing the norm
of random projections of given vectors exploiting the properties of scattering
of light; as a result, these computations happen literally at the speed of
light.
Because of the intimate connection
between the design of modern statistical models and computing, the vision of
this project is that it is necessary to rethink the way we construct and
implement modern statistical models to follow these new trends in computing
hardware.
Such changes need to be introduced at
the level of the design of the models, how to design inference algorithms, and
the implementation with OPUs in distributed environments.
The Ph.D. project will focus on the
development of such novel designs of modern statistical models based on
Gaussian processes, Deep Gaussian processes and Bayesian Deep/Conv Nets.
Advancements in this direction would
ultimately make statistical inference generally applicable to a large variety
of problems while reduce the impact of computing on the environment.
Doctorant.e: Wacker Jonas