Rule-Based Power-Balanced VLIW Instruction Scheduling with Uncertainty

Power-balanced instruction scheduling for Very Long Instruction Word (VLIW) processors is an optimization problem which requires a good instruction-level power model for the target processor. Conventionally, these power models are deterministic. However, in reality, there will always be some degree of imprecision involved. For power critical applications, it is desirable to find an optimal schedule which makes sure that the effects of these uncertainties could be minimized. The scheduling algorithm has to be computationally efficient in order to be practical for use in compilers. In this paper, we propose a rule based genetic algorithm to efficiently solve the optimization problem of power-balanced VLIW instruction scheduling with uncertainties in the power consumption model. We theoretically prove our rule-based genetic algorithm can produce as good optimal schedules as the existing algorithms proposed for this problem. Furthermore, its computational efficiency is significantly improved.