Using Machine-Learning to Efficiently Explore the Architecture/Compiler Co-Design Space

Using Machine-Learning to Efficiently Explore the Architecture/Compiler Co-Design Space

di Christophe Dubach

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Contenuto

This thesis proposes the use of machine-learning to address architecture/compiler co-design. The techniques developed in represent a new methodology with the potential to speed up the design of new processors and automate the generation of the corresponding optimising compilers, resulting in higher system efficiency and shorter time-to-market.

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