Hyperdimensional computing (HDC) is a neural inspired computing paradigm derived from the cognitive model proposed by Pentti Kanerva in 1988. Hyperdimensional computing consists in representing data through pseudo-random ultra-wide vectors, thus referred as hypervectors, with independent and identically distributed (i.i.d) components. A typical dimension (D) of an hypervector is D = 10000. The high dimensionality comes from the human brain complex structure which uses billions of synapses and neurons. Hyperdimensional computing is employed to compute similarity between data. Hence, an encoding phase is needed to transform data into hypervectors. Two are the main encoding methods, the record-based method and the N-gram based method.