Camelia Slimani

ENSTA Bretagne · Lab-STICC

Random forests is a widely used classification algorithm. It consists of a set of decision trees each of which is a classifier built on the basis of a random subset of the training data-set. In an environment where the memory work-space is low in comparison to the data-set size, when training a decision tree, a large proportion of the execution tim...

Random Forest based classification is a widely used Machine Learning algorithm. Training a random forest consists of building several decision trees that classify elements of the input dataset according to their features. This process is memory intensive. When datasets are larger than the available memory, the number of I/O operations grows signifi...

Non-volatile memories, such as Phase Change Memories (PCM), have interesting energy properties. In effect, their static energy consumption is negligible while the consumed dynamic energy depends on the performed operation (read/write). Several Dynamic Voltage and Frequency Scaling (DVFS) mechanisms have been proposed to optimize the energy consumpt...