A multi-schedule data-path synthesis framework is described that is an improvement over conventional single-schedule methods. The multi-schedule approach retains all feasible scheduling options and generates complete data-path solutions for each schedule. Constraints on time and resources are used to create a cost-ordered set of feasible scheduling solutions. Hence scheduling optimality is assured within the model of constraints. The algorithms have been converted to parallel form allowing execution on a cluster of workstations or an Intel hypercube. The results show that multi-schedule synthesis approach often outperforms well-known high-level synthesis systems in terms of both solution quality and run time. (C) 1999 Elsevier Science B.V. All rights reserved.