Reducing memory energy consumption of embedded applications that process dynamically allocated data

The authors present a set of strategies for reducing the energy consumption in a multibank memory architecture using an energy conscious dynamic memory allocation/deallocation. Applications that make dynamic memory allocations are used very frequently in embedded computing and mobile networking areas. The authors' strategies focus on such applications exclusively and cluster dynamically allocated data with a temporal affinity in the physical address space such that the data occupy a small number of memory banks. The remaining banks can be shut off, saving energy. One of the authors' strategies also employs dynamic data migration to further increase energy savings. The experimental results show that all the authors' strategies save a significant amount of energy.