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

TL;DR

This paper presents a multi-objective evolutionary algorithm-based method to optimize cache configurations in embedded systems, significantly reducing energy consumption and execution time for multimedia applications.

Contribution

It introduces a novel optimization framework combining multi-objective evolutionary algorithms with static profiling for cache configuration in embedded systems.

Findings

Achieved 64.43% reduction in execution time.

Achieved 91.69% reduction in energy consumption.

Effective optimization for Mediabench multimedia applications.

Abstract

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.