Sequential and Cooperative Distributed SA-Type Algorithms for Energy Optimization in Embedded Systems

Reducing memory energy consumption in embedded systems is crucial. In this paper, we propose new sequential and distributed algorithms based on Simulated Annealing (SA) in order to reduce memory energy consumption in embedded systems. Our algorithms outperform the Tabu Search (TS) approach. In fact, our algorithms manage to consume nearly from 76% up to 98% less memory energy than TS. Execution time savings for the distributed SA version (from 65.63% up to 75.34% for a cluster of 6 PCs) are also recorded.