Multi-objective optimization of energy consumption and execution time in a single level cache memory for embedded systems
This addresses energy and performance optimization for embedded systems running multimedia applications, but it is incremental as it applies known multi-objective methods to a specific domain.
The paper tackles the problem of optimizing both energy consumption and execution time in embedded systems by proposing a multi-objective evolutionary algorithm to find the best cache configuration for multimedia applications, achieving average improvements of 64.43% in execution time and 91.69% in energy consumption compared to a baseline.
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.