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

This work extends an earlier manual design space exploration of our developed Selective Load Value Prediction based superscalar architecture to the L2 unified cache. After that we perform an automatic design space exploration using a special developed software tool by varying several architectural parameters. Our goal is to find optimal configurations in terms of CPI (Cycles per Instruction) and energy consumption. By varying 19 architectural parameters, as we proposed, the design space is over 2.5 millions of billions configurations which obviously means that only heuristic search can be considered. Therefore, we propose different methods of automatic design space exploration based on our developed FADSE tool which allow us to evaluate only 2500 configurations of the above mentioned huge design space! The experimental results show that our automatic design space exploration (DSE) provides significantly better configurations than our previous manual DSE approach, considering the proposed multi-objective approach.