PhD Project Name: Efficient and High-Performance Model for Sequence Processing for AIoT

This project aims to develop new Artificial Intelligence (AI) models to allow their integration in environments with constraints over computational resources or time.

In particular, continual AI models offer a more efficient way to process sequence, with little to no loss in performance in comparison to their non-continual counterparts. Moreover, there are other methods to create even more light models, such as dimensionality reduction or low-rank approximation. All of this could be complemented with other techniques to improve performance with no increase in computational cost, such as making a clever processing of the data available.

The developments of the project will allow a reduction of the energy footprint of AI models and to make them a feasible option for a wider range of cases for Artificial Intelligence of Things.