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This work presents an exploration framework which performs data assignment and access scheduling exploration for applications given a multilayer memory architecture. Our framework uses multiobjective criteria during exploration, such as application execution time, energy, bandwidth, and data size. In order to tackle the complexity of the exploration, it is divided into three phases; Pareto diagram composition, data assignment, and access scheduling. The first phase produces multidimensional Pareto points for our application. After this phase, our framework produces distinct data assignments which are represented as Pareto points in a two dimensional space defined by bandwidth requirements and size requirements. Finally, the scheduling phase finds possibly optimal order of the tasks and performs precise scheduling of the tasks. Three feedbacks paths are present which can be used to iteratively improve exploration results. It is possible to trade off the quality of the results and the algorithm runtime. We have evaluated our framework on a medical image processing application. We have shown that our algorithms can perform exploration of the huge design space in an iterative manner and obtains good Pareto diagram coverage.