Efficient instruction-level optimization methodology for low-power embedded systems

For low-power embedded systems, we solve the instruction scheduling and reordering problem as a Precedence Constrained Hamiltonian Path Problem for DAGs and the traveling salesman problem (TSP), both of which are NP-hard (W.J. Cook et al., 1998; V. Jain et al., 1999). We propose an efficient instruction-level optimization algorithm for solving the NP-hard problem. Minimum spanning tree (MST) and simulated annealing (SA) mechanisms are used for the optimization. We describe the methods for generating the control flow and data dependence graph (CDG), power dissipation table (PDT), and weighted strongly connected graph (SCG) for the instruction-level low-power analysis. In addition, confidence limits with error tolerance are considered for the validation of the optimization. Finally, experimental results that demonstrate the effectiveness and efficiency of the proposed algorithms are shown.