Using Machine-learning To Efficiently Explore The Architecture/compiler Co-design Space

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.