Genetic micro-rollback self-recovery synthesis

Self-recovery micro-rollback synthesis (SMS) has currently become an important issue in high level synthesis. The problem of SMS combines the problem of functional unit scheduling and assignment with the problem of checkpoint insertion and microprogram optimization. It has been shown that these problems are NP-complete. The most studied problem is functional unit scheduling and assignment. Several heuristic techniques, including as soon as possible (ASAP), as last as possible (ALAP), integer programming, spring elasticity model, graph based mobility model, and genetic algorithm, are proposed. However, there are few studies on self-recovery micro-rollback synthesis and the technique of solution space searching by genetic algorithm has not been attempted. We study the feasibility of the genetic algorithm for the problem of SMS constrained on: the number of functional units, control steps, number of checkpoints, and the functional unit areas.