Date of Award
Master of Science
Electrical and Computer Engineering
In this modern computing world, with the advancement in technology, many-core systems are accelerating towards the emerging heterogeneous architecture. Optimization of the application performance is a crucial task for the unexplored aspect of design space exploration. Different research approaches have been proposed regarding the improvement in the efficiency of AMP(asymmetric many-core processor) with the adjustment to the diversity in workloads. In this respect, prediction of workload performance is very important for run-time optimization techniques. This is the reason we address the approach to consider the trade-offs exploration for the architectural simulation in our work. This approach can be followed by providing a feasible scenario with the design space parameters for the architectural exploration. Machine learning, along with the transfer learning technique, is taken into consideration to build the performance model for the prediction.
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