[en] Artificial Intelligence (AI) has emerged as one of the most revolutionary fields ofthis decade. Part of its success is due to advancements in High Performance Computing(HPC), which have made it possible to handle increasingly demanding models.However, the reliance on large-scale computational infrastructures emphasizes the challenge of optimizing computational efficiency, as energy and resource costs growsignificantly. This Thesis tackles these challenges by developing new mechanisms to achieve a convergence between Automated Machine Learning (AutoML) and HPC. Its motivation arises from two key observations: the over-parameterization of current models and the limited coupling between the architecture of these models andthe hardware on which they are executed. To overcome these issues, this Thesis explores the automatic generation of simplified yet accurate network architectures that optimize resource usage. Specifically, it introduces novel algorithms for pruning neural network architectures and highly optimized implementations of linear algebraroutines designed to enhance their performance on GPU. To achieve this, ourproposal defines two new interrelated and highly parameterizable search spaces, one focused on accuracy and the other on computational performance. By leveragingthese search spaces, we develop new unified autotuning strategies that enable the automatic generation of models with optimized accuracy-to-performance tradeoffs.Our evaluation demonstrates that it is possible to generate compressed models with high computational efficiency while maintaining little to no impact on accuracy.

[gl] A Intelixencia Artificial (IA) emerxeu como un dos campos máis revolucionarios desta década. Parte do seu éxito débese aos avances na Computación de Altas Prestacións (HPC), que permitiron manexar modelos cada vez máis esixentes. Porén,a dependencia de infraestruturas computacionais a grande escala acentúa o desafío de optimizar a eficiencia computacional, xa que os custos enerxéticos e de recursos medran notablemente. Esta Tese aborda estes desafíos desenvolvendo novos mecanismos para acadar unha converxencia entre o Automated Machine Learning (AutoML) e o HPC. A súa motivación xorde de dúas observacións chave: a sobreparametrización dos modelos actuais e o baixo acoplamento entre a arquitectura destes modelos e o hardware no que se executan. Para abordar estes problemas, esta Tese explora a xeración automática de arquitecturas de redes simplificadas, pero precisas,que optimizan o uso de recursos. Concretamente, presenta novos algoritmos para apoda de arquitecturas de redes neurais e o desenvolvemento de rutinas de álxebra lineal altamente optimizadas, deseñadas para mellorar o seu rendemento en GPU. Para lograr isto, a nosa proposta define e emprega dous novos espazos de procura interrelacionados e altamente parametrizables, un centrado na precisión e o outro norendemento computacional. Facendo uso destes espazos de procura, desenvolvemos estratexias de autoaxuste unificado que permiten xerar automaticamente modelos cunha relación precisión-rendemento superior. A nosa avaliación demostra que é posible xerar modelos comprimidos cunha alta eficiencia computacional e cun impacto mínimo ou nulo na precisión.