Nowadays, System-on-Chip architectures are composed of several execution resources which support complex applications. As it shares silicon area and limits the cost of the global circuit, the embedding of a reconfigurable resource in these SoC provides flexibility to the hardware. In this case, several implementations of the same algorithm, offering different characteristics, can be considered in order to optimize performances. In general, the tasks mapped on reconfigurable resources are algorithms that can be defined through several levels of parallelism. Clearly, parallelism directly affects the area and the execution time, this paper shows that the energy consumption is not constant, and decreases when the parallelism grows up.