Design space exploration in multi-objective hierarchical SOC design

Nowadays, multi-objective hierarchical design methodology has received more attention for its applicability to deal with complex SOC design. Reducing decision data across hierarchy levels is crucial to the hierarchical designs. However, previous works overlooked the importance of this feature and just nested the optimizing procedures of multiple levels. This paper discussed the way to compress decision data across hierarchical levels. Pareto-optimal theory was employed and developed to explore the design space of multi-objective hierarchical system. Furthermore, this paper proved that, under the independence condition, optimization in each hierarchical level could be performed independently. This is the very first time to explore the design space of multi-objective hierarchical system formally, which contributes to the promotion of novel hierarchical partition and synthesis methodology.