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State-of-the-art models such as LLMs are too large to fit in a single compute node (GPU, NPU, CPU), both for training and inference on a device (e.g., phone, laptop, tablet) or in larger-scale data centers. There is a need to develop optimization techniques to split and place these models onto a distributed set of compute nodes so that the overall system performance is maximized. The research will be focused on optimizing the placement of AI models onto distributed systems considering training time, energy consumption, and computational resources.

In the proposed project, we are interested in the mobile setting, and propose the use of depth information, on top of the usual RGB (Red, Green, Blue) pixel data acquired by mobile device cameras, to track and quantify visual attention.

En aquest treball es desenvoluparan procediments metaheurístics per resoldre un problema de (re)seqüenciació d'ordres de fabricació tenint en compte el preu fluctuant de l'energia elèctrica i la possible autogeneració fotovoltaica. Com a resultat, l'algoritme ha de ser resolt en un temps breu (<15 segons) i oferir alternatives que millorin el procediment actual de les empreses amb les quals es col·labora.

Consulta ofertes d'altres estudis i especialitats