Using Machine-Learning to Efficiently Explore the Architecture/Compiler Co-Design Space (Distinguished Dissertations)
Free Shipping Included! Using Machine-Learning to Efficiently Explore the Architecture/Compiler Co-Design Space (Distinguished Dissertations) by BCS at EMS Linux. Hurry! Limited time offer. Offer valid only while supplies last. Designing new microprocessors is a time consuming task. Architects rely on slow simulators to evaluate performance and a significant proportion of
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.
|Item Weight:||0.91 pounds|
|Item Size:||0.35 x 11.69 x 11.69 inches|
|Package Weight:||0.97 pounds|
|Package Size:||8.11 x 0.47 x 0.47 inches|
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