Improving the performance of embedded systems with variable neighborhood search

Abstract

Embedded systems have become an essential part of our lives, mainly due to the evolution of technology in the last years. However, the power consumption of these devices is one of their most important drawbacks. It has been proven that an efficient use of the memory of the device also improves its energy performance. This work efficiently solves the dynamic memory allocation problem, which can be formally defined as follows: given a program that has to be executed by a circuit, the objective is to fit that program in memory in such a way that the computing time required to execute it is minimized. In this work, we propose a parallel variable neighborhood search strategy to address this problem. We additionally compare this parallel procedure with the sequential version of the algorithm and with the best previous approach. Computational results show the superiority of our proposal, backed up with statistical tests.