Multi-objective optimisations for a superscalar architecture with selective value prediction

This work extends an earlier manual design space exploration (DSE) of the authors?? developed selective load value prediction-based superscalar architecture to the L2 unified cache. After that the authors perform an automatic DSE using a special developed software tool by varying several architectural parameters. The goal is to find optimal configurations in terms of cycles per instruction and energy consumption. By varying 19 architectural parameters, as the authors proposed, the design space is over 2.5 millions of billions configurations which obviously means that only a heuristic search can be considered. Therefore the authors propose different methods of automatic DSE based on their developed framework for automatic design space exploration which allow them to evaluate only 2500 configurations of the above mentioned huge design space! The experimental results show that their automatic DSE provides significantly better configurations than the previous manual DSE approach, considering the proposed multi-objective approach.