Using Machine-Learning to Efficiently Explore the Architecture/Compiler Co-Design Space (Distinguished Dissertation) - Tapa blanda
Sinopsis
Designing new microprocessors is a time consuming task. Architects rely on slow simulators to evaluate performance and a significant proportion of the design space has to be explored before an implementation is chosen. This process becomes more time consuming when compiler optimisations are also considered. Once the architecture is selected, a new compiler must be developed and tuned. What is needed are techniques that can speedup this whole process and develop a new optimising compiler automatically. This thesis proposes the use of machine-learning techniques to address architecture/compiler co-design.
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Acerca del autor
Christophe Dubach received his Ph.D in Informatics from the University of Edinburgh in 2009 and holds a M.Sc. degree in Computer Science from EPFL, Switzerland. He is currently an RAEng/EPSRC Research Fellow in the Institute for Computing Systems Architecture at the University of Edinburgh. His research interests include co-design of both computer architecture and optimising compiler technology, adaptive microprocessor and software and the application of machine-learning in these areas.
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Using Machine-Learning to Efficiently Explore the Architecture/Compiler Co-Design Space
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