Exploring energy aware microarchitectural design space via computationally efficient genetic programming

Efficiently exploring the microarchitectural design space is crucial in order to find promising design subspaces satisfying better power constraints. Based on our previous work on Guided Search Space Genetic Programming (GSS-GP), we introduce a new fitness function based on Fisher Linear Discriminant, in addition to the weighted fitness function designed to improve unbalanced classification accuracy. Experimental results show that GSS-GP outperforms classical GP in both accuracy and convergence times, with a minor class accuracy improvement of 9.05 percentage points. In addition, GSS-GP resulted in a significant reduction of more than 99% in processing time compared to other robust classifiers like Support Vector Machines.