Multi-objective optimization of energy consumption and execution time in a single level cache memory for embedded systems

We present a new parallel optimization method to optimize cache memory designs.It obtains optimal designs of cache memories with respect to designer's metrics.We also reduce the execution time and energy consumption up to 64.43% and 91.69%.As a result, we have outlined other interesting future research lines in this area. Current embedded systems are specifically designed to run multimedia applications. These applications have a big impact on both performance and energy consumption. Both metrics can be optimized selecting the best cache configuration for a target set of applications. Multi-objective optimization may help to minimize both conflicting metrics in an independent manner. In this work, we propose an optimization method that based on Multi-Objective Evolutionary Algorithms, is able to find the best cache configuration for a given set of applications. To evaluate the goodness of candidate solutions, the execution of the optimization algorithm is combined with a static profiling methodology using several well-known simulation tools. Results show that our optimization framework is able to obtain an optimized cache for Mediabench applications. Compared to a baseline cache memory, our design method reaches an average improvement of 64.43 and 91.69% in execution time and energy consumption, respectively.