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