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Computer architectures become more and more complex. It requires more effortto develop techniques that improve the programs of performance and allow toexploit material resources efficiently. As a result, many transformations areapplied on various levels of code abstraction. The first level is the highlevel, where the representation is close to the high level language. The secondone is the low level, where the presentation is close to the machine code.Those transformations are called code optimizations. Optimizing programsrequires deep expertise. On one hand, it is a tedious task, because it requiresa lot of tests to find out the best combination of optimizations to apply withtheir best factors. On the other hand, this task is critical, because it maydegrade the performance of the program instead of improving it. Theautomatization of this task can deal with this problem and permit to obtaingood results. Our end of study project consists on proposing a novel approachbased on neural networks to automatically optimize loops in Tiramisu. Tiramisuis a new language to create a code of high performance. It allows to separatebetween the algorithm and its optimizations. We have chosen loop unrolling as astudy case. Our contribution aims to automate the choice of the best loopunrolling factor for a program written in Tiramisu.

Proposition d'un mod\`ele pour l'optimisation automatique de boucles dans le compilateur Tiramisu : cas d'optimisation de d\'eroulage