Scheduling with integer time budgeting for low-power optimization

In this paper we present a mathematical programming formulation of the integer time budgeting problem for directed acyclic graphs. In particular, we formally prove that our constraint matrix has a special property that enables a polynomial-time algorithm to solve the problem optimally with a guaranteed integral solution. Our theory can be directly applied to solving a scheduling problem in behavioral synthesis with the objective of minimizing the system power consumption. Given a set of scheduling constraints and a collection of convex power-delay tradeoff curves for each type of operation, our scheduler can intelligently schedule the operations to appropriate clock cycles and simultaneously select the module implementations that lead to low-power solutions. Experiments demonstrate that our proposed technique can produce near-optimal results (within 6% of the optimum by the ILP formulation), with 40x+ speedup.